Harmonic Auto-Regularization for Non Rigid Groupwise Registration in Cardiac Magnetic Resonance Imaging

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Sanz-Estebanez, S.; Royuela-del-Val, J.; Sevilla, T.; Revilla-Orodea, A.; Aja-Fernandez, S.; Alberola-Lopez, C.

2016-07-01

In this paper we present a new approach for non rigid groupwise registration of cardiac magnetic resonance images by means of free-form deformations, imposing a prior harmonic deformation assumption. The procedure proposes a primal-dual framework for solving an equality constrained minimization problem, which allows an automatic estimate of the trade-off between image fidelity and the Laplacian smoothness terms for each iteration. The method has been applied to both a 4D extended cardio-torso phantom and to a set of voluntary patients. The accuracy of the method has been measured for the synthetic experiment as the difference in modulus between the estimated displacement field and the ground truth; as for the real data, we have calculated the Dice coefficient between the contour manual delineations provided by two cardiologists at end systolic phase and those provided by them at end diastolic phase and, consequently propagated by the registration algorithm to the systolic instant. The automatic procedure turns out to be competitive in motion compensation with other methods even though their parameters have been previously set for optimal performance in different scenarios. (Author)

A new technique for noise reduction at coronary CT angiography with multi-phase data-averaging and non-rigid image registration

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Tatsugami, Fuminari; Higaki, Toru; Nakamura, Yuko; Yamagami, Takuji; Date, Shuji; Awai, Kazuo [Hiroshima University, Department of Diagnostic Radiology, Minami-ku, Hiroshima (Japan); Fujioka, Chikako; Kiguchi, Masao [Hiroshima University, Department of Radiology, Minami-ku, Hiroshima (Japan); Kihara, Yasuki [Hiroshima University, Department of Cardiovascular Medicine, Minami-ku, Hiroshima (Japan)

2015-01-15

To investigate the feasibility of a newly developed noise reduction technique at coronary CT angiography (CTA) that uses multi-phase data-averaging and non-rigid image registration. Sixty-five patients underwent coronary CTA with prospective ECG-triggering. The range of the phase window was set at 70-80 % of the R-R interval. First, three sets of consecutive volume data at 70 %, 75 % and 80 % of the R-R interval were prepared. Second, we applied non-rigid registration to align the 70 % and 80 % images to the 75 % image. Finally, we performed weighted averaging of the three images and generated a de-noised image. The image noise and contrast-to-noise ratio (CNR) in the proximal coronary arteries between the conventional 75 % and the de-noised images were compared. Two radiologists evaluated the image quality using a 5-point scale (1, poor; 5, excellent). On de-noised images, mean image noise was significantly lower than on conventional 75 % images (18.3 HU ± 2.6 vs. 23.0 HU ± 3.3, P < 0.01) and the CNR was significantly higher (P < 0.01). The mean image quality score for conventional 75 % and de-noised images was 3.9 and 4.4, respectively (P < 0.01). Our method reduces image noise and improves image quality at coronary CTA. (orig.)

Non-rigid contour-to-pixel registration of photographic and quantitative light-induced fluorescence imaging of decalcified teeth

Science.gov (United States)

Berkels, Benjamin; Deserno, Thomas; Ehrlich, Eva E.; Fritz, Ulrike B.; Sirazitdinova, Ekaterina; Tatano, Rosalia

2016-03-01

Quantitative light-induced fluorescence (QLF) is widely used to assess the damage of a tooth due to decalcification. In digital photographs, decalcification appears as white spot lesions, i.e. white spots on the tooth surface. We propose a novel multimodal registration approach for the matching of digital photographs and QLF images of decalcified teeth. The registration is based on the idea of contour-to-pixel matching. Here, the curve, which represents the shape of the tooth, is extracted from the QLF image using a contour segmentation by binarization and morphological processing. This curve is aligned to the photo with a non-rigid variational registration approach. Thus, the registration problem is formulated as minimization problem with an objective function that consists of a data term and a regularizer for the deformation. To construct the data term, the photo is pointwise classified into tooth and non-tooth regions. Then, the signed distance function of the tooth region allows to measure the mismatch between curve and photo. As regularizer a higher order, linear elastic prior is used. The resulting minimization problem is solved numerically using bilinear Finite Elements for the spatial discretization and the Gauss-Newton algorithm. The evaluation is based on 150 image pairs, where an average of 5 teeth have been captured from 32 subjects. All registrations have been confirmed correctly by a dental expert. The contour-to-pixel methods can directly be used in 3D for surface-to-voxel tasks.

Vision based tunnel inspection using non-rigid registration

Science.gov (United States)

Badshah, Amir; Ullah, Shan; Shahzad, Danish

2015-04-01

Growing numbers of long tunnels across the globe has increased the need for safety measurements and inspections of tunnels in these days. To avoid serious damages, tunnel inspection is highly recommended at regular intervals of time to find any deformations or cracks at the right time. While following the stringent safety and tunnel accessibility standards, conventional geodetic surveying using techniques of civil engineering and other manual and mechanical methods are time consuming and results in troublesome of routine life. An automatic tunnel inspection by image processing techniques using non rigid registration has been proposed. There are many other image processing methods used for image registration purposes. Most of the processes are operation of images in its spatial domain like finding edges and corners by Harris edge detection method. These methods are quite time consuming and fail for some or other reasons like for blurred or images with noise. Due to use of image features directly by these methods in the process, are known by the group, correlation by image features. The other method is featureless correlation, in which the images are converted into its frequency domain and then correlated with each other. The shift in spatial domain is the same as in frequency domain, but the processing is order faster than in spatial domain. In the proposed method modified normalized phase correlation has been used to find any shift between two images. As pre pre-processing the tunnel images i.e. reference and template are divided into small patches. All these relative patches are registered by the proposed modified normalized phase correlation. By the application of the proposed algorithm we get the pixel movement of the images. And then these pixels shifts are converted to measuring units like mm, cm etc. After the complete process if there is any shift in the tunnel at described points are located.

On removing interpolation and resampling artifacts in rigid image registration.

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Aganj, Iman; Yeo, Boon Thye Thomas; Sabuncu, Mert R; Fischl, Bruce

2013-02-01

We show that image registration using conventional interpolation and summation approximations of continuous integrals can generally fail because of resampling artifacts. These artifacts negatively affect the accuracy of registration by producing local optima, altering the gradient, shifting the global optimum, and making rigid registration asymmetric. In this paper, after an extensive literature review, we demonstrate the causes of the artifacts by comparing inclusion and avoidance of resampling analytically. We show the sum-of-squared-differences cost function formulated as an integral to be more accurate compared with its traditional sum form in a simple case of image registration. We then discuss aliasing that occurs in rotation, which is due to the fact that an image represented in the Cartesian grid is sampled with different rates in different directions, and propose the use of oscillatory isotropic interpolation kernels, which allow better recovery of true global optima by overcoming this type of aliasing. Through our experiments on brain, fingerprint, and white noise images, we illustrate the superior performance of the integral registration cost function in both the Cartesian and spherical coordinates, and also validate the introduced radial interpolation kernel by demonstrating the improvement in registration.

Non-rigid registration of a 3D ultrasound and a MR image data set of the female pelvic floor using a biomechanical model

Directory of Open Access Journals (Sweden)

Rexilius Jan

2005-03-01

Full Text Available Abstract Background The visual combination of different modalities is essential for many medical imaging applications in the field of Computer-Assisted medical Diagnosis (CAD to enhance the clinical information content. Clinically, incontinence is a diagnosis with high clinical prevalence and morbidity rate. The search for a method to identify risk patients and to control the success of operations is still a challenging task. The conjunction of magnetic resonance (MR and 3D ultrasound (US image data sets could lead to a new clinical visual representation of the morphology as we show with corresponding data sets of the female anal canal with this paper. Methods We present a feasibility study for a non-rigid registration technique based on a biomechanical model for MR and US image data sets of the female anal canal as a base for a new innovative clinical visual representation. Results It is shown in this case study that the internal and external sphincter region could be registered elastically and the registration partially corrects the compression induced by the ultrasound transducer, so the MR data set showing the native anatomy is used as a frame for the US data set showing the same region with higher resolution but distorted by the transducer Conclusion The morphology is of special interest in the assessment of anal incontinence and the non-rigid registration of normal clinical MR and US image data sets is a new field of the adaptation of this method incorporating the advantages of both technologies.

Performance evaluation of 2D image registration algorithms with the numeric image registration and comparison platform

International Nuclear Information System (INIS)

Gerganov, G.; Kuvandjiev, V.; Dimitrova, I.; Mitev, K.; Kawrakow, I.

2012-01-01

The objective of this work is to present the capabilities of the NUMERICS web platform for evaluation of the performance of image registration algorithms. The NUMERICS platform is a web accessible tool which provides access to dedicated numerical algorithms for registration and comparison of medical images (http://numerics.phys.uni-sofia.bg). The platform allows comparison of noisy medical images by means of different types of image comparison algorithms, which are based on statistical tests for outliers. The platform also allows 2D image registration with different techniques like Elastic Thin-Plate Spline registration, registration based on rigid transformations, affine transformations, as well as non-rigid image registration based on Mobius transformations. In this work we demonstrate how the platform can be used as a tool for evaluation of the quality of the image registration process. We demonstrate performance evaluation of a deformable image registration technique based on Mobius transformations. The transformations are applied with appropriate cost functions like: Mutual information, Correlation coefficient, Sum of Squared Differences. The accent is on the results provided by the platform to the user and their interpretation in the context of the performance evaluation of 2D image registration. The NUMERICS image registration and image comparison platform provides detailed statistical information about submitted image registration jobs and can be used to perform quantitative evaluation of the performance of different image registration techniques. (authors)

Medical image registration for analysis

International Nuclear Information System (INIS)

Petrovic, V.

2006-01-01

Full text: Image registration techniques represent a rich family of image processing and analysis tools that aim to provide spatial correspondences across sets of medical images of similar and disparate anatomies and modalities. Image registration is a fundamental and usually the first step in medical image analysis and this paper presents a number of advanced techniques as well as demonstrates some of the advanced medical image analysis techniques they make possible. A number of both rigid and non-rigid medical image alignment algorithms of equivalent and merely consistent anatomical structures respectively are presented. The algorithms are compared in terms of their practical aims, inputs, computational complexity and level of operator (e.g. diagnostician) interaction. In particular, the focus of the methods discussion is placed on the applications and practical benefits of medical image registration. Results of medical image registration on a number of different imaging modalities and anatomies are presented demonstrating the accuracy and robustness of their application. Medical image registration is quickly becoming ubiquitous in medical imaging departments with the results of such algorithms increasingly used in complex medical image analysis and diagnostics. This paper aims to demonstrate at least part of the reason why

Elastic Versus Rigid Image Registration in Magnetic Resonance Imaging-transrectal Ultrasound Fusion Prostate Biopsy: A Systematic Review and Meta-analysis.

Science.gov (United States)

Venderink, Wulphert; de Rooij, Maarten; Sedelaar, J P Michiel; Huisman, Henkjan J; Fütterer, Jurgen J

2016-07-29

The main difference between the available magnetic resonance imaging-transrectal ultrasound (MRI-TRUS) fusion platforms for prostate biopsy is the method of image registration being either rigid or elastic. As elastic registration compensates for possible deformation caused by the introduction of an ultrasound probe for example, it is expected that it would perform better than rigid registration. The aim of this meta-analysis is to compare rigid with elastic registration by calculating the detection odds ratio (OR) for both subgroups. The detection OR is defined as the ratio of the odds of detecting clinically significant prostate cancer (csPCa) by MRI-TRUS fusion biopsy compared with systematic TRUS biopsy. Secondary objectives were the OR for any PCa and the OR after pooling both registration techniques. The electronic databases PubMed, Embase, and Cochrane were systematically searched for relevant studies according to the Preferred Reporting Items for Systematic Review and Meta-analysis Statement. Studies comparing MRI-TRUS fusion and systematic TRUS-guided biopsies in the same patient were included. The quality assessment of included studies was performed using the Quality Assessment of Diagnostic Accuracy Studies version 2. Eleven papers describing elastic and 10 describing rigid registration were included. Meta-analysis showed an OR of csPCa for elastic and rigid registration of 1.45 (95% confidence interval [CI]: 1.21-1.73, pimaging-transrectal ultrasound fusion systems which vary in their method of compensating for prostate deformation. Copyright © 2016 European Association of Urology. Published by Elsevier B.V. All rights reserved.

Non-rigid registration of breast surfaces using the laplace and diffusion equations

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Ou Jao J

2010-02-01

Full Text Available Abstract A semi-automated, non-rigid breast surface registration method is presented that involves solving the Laplace or diffusion equations over undeformed and deformed breast surfaces. The resulting potential energy fields and isocontours are used to establish surface correspondence. This novel surface-based method, which does not require intensity images, anatomical landmarks, or fiducials, is compared to a gold standard of thin-plate spline (TPS interpolation. Realistic finite element simulations of breast compression and further testing against a tissue-mimicking phantom demonstrate that this method is capable of registering surfaces experiencing 6 - 36 mm compression to within a mean error of 0.5 - 5.7 mm.

Rigid registration of CT, MR and cryosection images using a GLCM framework

DEFF Research Database (Denmark)

Bro-Nielsen, Morten; Grimson, E.; Mosges, R.

1997-01-01

The majority of the available rigid registration measures are based on a 2-dimensional histogram of corresponding grey-values in the registered images. This paper shows that these features are similar to a family of texture measures based on grey level co-occurrence matrices (GLCM). Features from...

Correction for non-rigid movement artefacts in calcium imaging using local-global optical flow and PCA-based templates

DEFF Research Database (Denmark)

Brazhe, A.; Fordsmann, J.; Lauritzen, M.

2017-01-01

correction of calcium timelapse imaging data is accurate, can represent non-rigid image distortions, robust to noisy data and allows for fast registration of large videos. The implementation is open-source and is programmed in Python, which provides for easy access and merging into downstream image...

Fast fluid registration of medical images

DEFF Research Database (Denmark)

Bro-Nielsen, Morten; Gramkow, Claus

1996-01-01

This paper offers a new fast algorithm for non-rigid viscous fluid registration of medical images that is at least an order of magnitude faster than the previous method by (Christensen et al., 1994). The core algorithm in the fluid registration method is based on a linear elastic deformation...

Complexity and accuracy of image registration methods in SPECT-guided radiation therapy

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Yin, L S; Duzenli, C; Moiseenko, V [Physics and Astronomy, University of British Columbia, 6224 Agricultural Road, Vancouver, BC, V6T 1Z1 (Canada); Tang, L; Hamarneh, G [Computing Science, Simon Fraser University, 9400 TASC1, Burnaby, BC, V5A 1S6 (Canada); Gill, B [Medical Physics, Vancouver Cancer Centre, BC Cancer Agency, 600 West 10th Ave, Vancouver, BC, V5Z 4E6 (Canada); Celler, A; Shcherbinin, S [Department of Radiology, University of British Columbia, 828 West 10th Ave, Vancouver, BC, V5Z 1L8 (Canada); Fua, T F; Thompson, A; Sheehan, F [Radiation Oncology, Vancouver Cancer Centre, BC Cancer Agency, 600 West 10th Ave, Vancouver, BC, V5Z 4E6 (Canada); Liu, M [Radiation Oncology, Fraser Valley Cancer Centre, BC Cancer Agency, 13750 9th Ave, Surrey, BC, V3V 1Z2 (Canada)], E-mail: lyin@bccancer.bc.ca

2010-01-07

The use of functional imaging in radiotherapy treatment (RT) planning requires accurate co-registration of functional imaging scans to CT scans. We evaluated six methods of image registration for use in SPECT-guided radiotherapy treatment planning. Methods varied in complexity from 3D affine transform based on control points to diffeomorphic demons and level set non-rigid registration. Ten lung cancer patients underwent perfusion SPECT-scans prior to their radiotherapy. CT images from a hybrid SPECT/CT scanner were registered to a planning CT, and then the same transformation was applied to the SPECT images. According to registration evaluation measures computed based on the intensity difference between the registered CT images or based on target registration error, non-rigid registrations provided a higher degree of accuracy than rigid methods. However, due to the irregularities in some of the obtained deformation fields, warping the SPECT using these fields may result in unacceptable changes to the SPECT intensity distribution that would preclude use in RT planning. Moreover, the differences between intensity histograms in the original and registered SPECT image sets were the largest for diffeomorphic demons and level set methods. In conclusion, the use of intensity-based validation measures alone is not sufficient for SPECT/CT registration for RTTP. It was also found that the proper evaluation of image registration requires the use of several accuracy metrics.

Remote Sensing Image Registration Using Multiple Image Features

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Kun Yang

2017-06-01

Full Text Available Remote sensing image registration plays an important role in military and civilian fields, such as natural disaster damage assessment, military damage assessment and ground targets identification, etc. However, due to the ground relief variations and imaging viewpoint changes, non-rigid geometric distortion occurs between remote sensing images with different viewpoint, which further increases the difficulty of remote sensing image registration. To address the problem, we propose a multi-viewpoint remote sensing image registration method which contains the following contributions. (i A multiple features based finite mixture model is constructed for dealing with different types of image features. (ii Three features are combined and substituted into the mixture model to form a feature complementation, i.e., the Euclidean distance and shape context are used to measure the similarity of geometric structure, and the SIFT (scale-invariant feature transform distance which is endowed with the intensity information is used to measure the scale space extrema. (iii To prevent the ill-posed problem, a geometric constraint term is introduced into the L2E-based energy function for better behaving the non-rigid transformation. We evaluated the performances of the proposed method by three series of remote sensing images obtained from the unmanned aerial vehicle (UAV and Google Earth, and compared with five state-of-the-art methods where our method shows the best alignments in most cases.

Extracting a Purely Non-rigid Deformation Field of a Single Structure

Science.gov (United States)

Demirci, Stefanie; Manstad-Hulaas, Frode; Navab, Nassir

During endovascular aortic repair (EVAR) treatment, the aortic shape is subject to severe deformation that is imposed by medical instruments such as guide wires, catheters, and the stent graft. The problem definition of deformable registration of images covering the entire abdominal region, however, is highly ill-posed. We present a new method for extracting the deformation of an aneurysmatic aorta. The outline of the procedure includes initial rigid alignment of two abdominal scans, segmentation of abdominal vessel trees, and automatic reduction of their centerline structures to one specified region of interest around the aorta. Our non-rigid registration procedure then only computes local non-rigid deformation and leaves out all remaining global rigid transformations. In order to evaluate our method, experiments for the extraction of aortic deformation fields are conducted on 15 patient datasets from endovascular aortic repair (EVAR) treatment. A visual assessment of the registration results were performed by two vascular surgeons and one interventional radiologist who are all experts in EVAR procedures.

Medical Image Registration and Surgery Simulation

DEFF Research Database (Denmark)

Bro-Nielsen, Morten

1996-01-01

This thesis explores the application of physical models in medical image registration and surgery simulation. The continuum models of elasticity and viscous fluids are described in detail, and this knowledge is used as a basis for most of the methods described here. Real-time deformable models......, and the use of selective matrix vector multiplication. Fluid medical image registration A new and faster algorithm for non-rigid registration using viscous fluid models is presented. This algorithm replaces the core part of the original algorithm with multi-resolution convolution using a new filter, which...... growth is also presented. Using medical knowledge about the growth processes of the mandibular bone, a registration algorithm for time sequence images of the mandible is developed. Since this registration algorithm models the actual development of the mandible, it is possible to simulate the development...

A novel scheme for automatic nonrigid image registration using deformation invariant feature and geometric constraint

Science.gov (United States)

Deng, Zhipeng; Lei, Lin; Zhou, Shilin

2015-10-01

Automatic image registration is a vital yet challenging task, particularly for non-rigid deformation images which are more complicated and common in remote sensing images, such as distorted UAV (unmanned aerial vehicle) images or scanning imaging images caused by flutter. Traditional non-rigid image registration methods are based on the correctly matched corresponding landmarks, which usually needs artificial markers. It is a rather challenging task to locate the accurate position of the points and get accurate homonymy point sets. In this paper, we proposed an automatic non-rigid image registration algorithm which mainly consists of three steps: To begin with, we introduce an automatic feature point extraction method based on non-linear scale space and uniform distribution strategy to extract the points which are uniform distributed along the edge of the image. Next, we propose a hybrid point matching algorithm using DaLI (Deformation and Light Invariant) descriptor and local affine invariant geometric constraint based on triangulation which is constructed by K-nearest neighbor algorithm. Based on the accurate homonymy point sets, the two images are registrated by the model of TPS (Thin Plate Spline). Our method is demonstrated by three deliberately designed experiments. The first two experiments are designed to evaluate the distribution of point set and the correctly matching rate on synthetic data and real data respectively. The last experiment is designed on the non-rigid deformation remote sensing images and the three experimental results demonstrate the accuracy, robustness, and efficiency of the proposed algorithm compared with other traditional methods.

Image registration with auto-mapped control volumes

International Nuclear Information System (INIS)

Schreibmann, Eduard; Xing Lei

2006-01-01

Many image registration algorithms rely on the use of homologous control points on the two input image sets to be registered. In reality, the interactive identification of the control points on both images is tedious, difficult, and often a source of error. We propose a two-step algorithm to automatically identify homologous regions that are used as a priori information during the image registration procedure. First, a number of small control volumes having distinct anatomical features are identified on the model image in a somewhat arbitrary fashion. Instead of attempting to find their correspondences in the reference image through user interaction, in the proposed method, each of the control regions is mapped to the corresponding part of the reference image by using an automated image registration algorithm. A normalized cross-correlation (NCC) function or mutual information was used as the auto-mapping metric and a limited memory Broyden-Fletcher-Goldfarb-Shanno algorithm (L-BFGS) was employed to optimize the function to find the optimal mapping. For rigid registration, the transformation parameters of the system are obtained by averaging that derived from the individual control volumes. In our deformable calculation, the mapped control volumes are treated as the nodes or control points with known positions on the two images. If the number of control volumes is not enough to cover the whole image to be registered, additional nodes are placed on the model image and then located on the reference image in a manner similar to the conventional BSpline deformable calculation. For deformable registration, the established correspondence by the auto-mapped control volumes provides valuable guidance for the registration calculation and greatly reduces the dimensionality of the problem. The performance of the two-step registrations was applied to three rigid registration cases (two PET-CT registrations and a brain MRI-CT registration) and one deformable registration of

Non-rigid registration of 3D ultrasound for neurosurgery using automatic feature detection and matching.

Science.gov (United States)

Machado, Inês; Toews, Matthew; Luo, Jie; Unadkat, Prashin; Essayed, Walid; George, Elizabeth; Teodoro, Pedro; Carvalho, Herculano; Martins, Jorge; Golland, Polina; Pieper, Steve; Frisken, Sarah; Golby, Alexandra; Wells, William

2018-06-04

The brain undergoes significant structural change over the course of neurosurgery, including highly nonlinear deformation and resection. It can be informative to recover the spatial mapping between structures identified in preoperative surgical planning and the intraoperative state of the brain. We present a novel feature-based method for achieving robust, fully automatic deformable registration of intraoperative neurosurgical ultrasound images. A sparse set of local image feature correspondences is first estimated between ultrasound image pairs, after which rigid, affine and thin-plate spline models are used to estimate dense mappings throughout the image. Correspondences are derived from 3D features, distinctive generic image patterns that are automatically extracted from 3D ultrasound images and characterized in terms of their geometry (i.e., location, scale, and orientation) and a descriptor of local image appearance. Feature correspondences between ultrasound images are achieved based on a nearest-neighbor descriptor matching and probabilistic voting model similar to the Hough transform. Experiments demonstrate our method on intraoperative ultrasound images acquired before and after opening of the dura mater, during resection and after resection in nine clinical cases. A total of 1620 automatically extracted 3D feature correspondences were manually validated by eleven experts and used to guide the registration. Then, using manually labeled corresponding landmarks in the pre- and post-resection ultrasound images, we show that our feature-based registration reduces the mean target registration error from an initial value of 3.3 to 1.5 mm. This result demonstrates that the 3D features promise to offer a robust and accurate solution for 3D ultrasound registration and to correct for brain shift in image-guided neurosurgery.

The influence of non-rigid anatomy and patient positioning on endoscopy-CT image registration in the head and neck.

Science.gov (United States)

Ingram, W Scott; Yang, Jinzhong; Wendt, Richard; Beadle, Beth M; Rao, Arvind; Wang, Xin A; Court, Laurence E

2017-08-01

To assess the influence of non-rigid anatomy and differences in patient positioning between CT acquisition and endoscopic examination on endoscopy-CT image registration in the head and neck. Radiotherapy planning CTs and 31-35 daily treatment-room CTs were acquired for nineteen patients. Diagnostic CTs were acquired for thirteen of the patients. The surfaces of the airways were segmented on all scans and triangular meshes were created to render virtual endoscopic images with a calibrated pinhole model of an endoscope. The virtual images were used to take projective measurements throughout the meshes, with reference measurements defined as those taken on the planning CTs and test measurements defined as those taken on the daily or diagnostic CTs. The influence of non-rigid anatomy was quantified by 3D distance errors between reference and test measurements on the daily CTs, and the influence of patient positioning was quantified by 3D distance errors between reference and test measurements on the diagnostic CTs. The daily CT measurements were also used to investigate the influences of camera-to-surface distance, surface angle, and the interval of time between scans. Average errors in the daily CTs were 0.36 ± 0.61 cm in the nasal cavity, 0.58 ± 0.83 cm in the naso- and oropharynx, and 0.47 ± 0.73 cm in the hypopharynx and larynx. Average errors in the diagnostic CTs in those regions were 0.52 ± 0.69 cm, 0.65 ± 0.84 cm, and 0.69 ± 0.90 cm, respectively. All CTs had errors heavily skewed towards 0, albeit with large outliers. Large camera-to-surface distances were found to increase the errors, but the angle at which the camera viewed the surface had no effect. The errors in the Day 1 and Day 15 CTs were found to be significantly smaller than those in the Day 30 CTs (P projective measurement errors. In general, these errors are largest when the camera is in the superior pharynx, where it sees large distances and a lot of muscle motion. The

Co-registration of the BNCT treatment planning images for clinical practice

International Nuclear Information System (INIS)

Salli, Eero; Seppaelae, Tiina; Kankaanranta, Leena; Asikainen, Sami; Savolainen, Sauli; Koivunoro, Hanna

2006-01-01

We have co-registered MRI, CT and FBPA-PET images for BNCT in clinical practice. Co-registration improves the spatial accuracy of the treatment planning by enabling use of information from all the co-registered modalities. The multimodal co-registration has been implemented as a service product provided by the Imaging Center of Helsinki University Central Hospital to other departments. To increase the accuracy of co-registration and patient positioning in the head area BNCT, a patient-specific fixation mask suitable for PET, MRI and CT was developed. The goal of the fixation mask is to normalize the orientation of the patient's head and neck. Co-registration is performed at the image processing unit by using a rigid body model, mutual-information based algorithms and partly in-house developed software tools. The accuracy of co-registration is verified by comparing the locations of the external skin markers and anatomical landmarks in different modalities. After co-registration, the images are transformed and covered into a format required by the BNCT dose-planning software and set to the dose-planning unit of the hospital. So far co-registration has been done for 22 patients. The co-registration protocol has proved to be reliable and efficient. Some registration errors are seen on some patients in the neck area because the rigid-body model used in co-registration is not fully valid for the brain-neck entity. The registration accuracy in this area could likely be improved by implementing a co-registration procedure utilizing a partly non-rigid body model. (author)

High-performance GPU-based rendering for real-time, rigid 2D/3D-image registration and motion prediction in radiation oncology.

Science.gov (United States)

Spoerk, Jakob; Gendrin, Christelle; Weber, Christoph; Figl, Michael; Pawiro, Supriyanto Ardjo; Furtado, Hugo; Fabri, Daniella; Bloch, Christoph; Bergmann, Helmar; Gröller, Eduard; Birkfellner, Wolfgang

2012-02-01

A common problem in image-guided radiation therapy (IGRT) of lung cancer as well as other malignant diseases is the compensation of periodic and aperiodic motion during dose delivery. Modern systems for image-guided radiation oncology allow for the acquisition of cone-beam computed tomography data in the treatment room as well as the acquisition of planar radiographs during the treatment. A mid-term research goal is the compensation of tumor target volume motion by 2D/3D Registration. In 2D/3D registration, spatial information on organ location is derived by an iterative comparison of perspective volume renderings, so-called digitally rendered radiographs (DRR) from computed tomography volume data, and planar reference x-rays. Currently, this rendering process is very time consuming, and real-time registration, which should at least provide data on organ position in less than a second, has not come into existence. We present two GPU-based rendering algorithms which generate a DRR of 512×512 pixels size from a CT dataset of 53 MB size at a pace of almost 100 Hz. This rendering rate is feasible by applying a number of algorithmic simplifications which range from alternative volume-driven rendering approaches - namely so-called wobbled splatting - to sub-sampling of the DRR-image by means of specialized raycasting techniques. Furthermore, general purpose graphics processing unit (GPGPU) programming paradigms were consequently utilized. Rendering quality and performance as well as the influence on the quality and performance of the overall registration process were measured and analyzed in detail. The results show that both methods are competitive and pave the way for fast motion compensation by rigid and possibly even non-rigid 2D/3D registration and, beyond that, adaptive filtering of motion models in IGRT. Copyright © 2011. Published by Elsevier GmbH.

High-performance GPU-based rendering for real-time, rigid 2D/3D-image registration and motion prediction in radiation oncology

Energy Technology Data Exchange (ETDEWEB)

Spoerk, Jakob; Gendrin, Christelle; Weber, Christoph [Medical University of Vienna (Austria). Center of Medical Physics and Biomedical Engineering] [and others

2012-07-01

A common problem in image-guided radiation therapy (IGRT) of lung cancer as well as other malignant diseases is the compensation of periodic and aperiodic motion during dose delivery. Modern systems for image-guided radiation oncology allow for the acquisition of cone-beam computed tomography data in the treatment room as well as the acquisition of planar radiographs during the treatment. A mid-term research goal is the compensation of tumor target volume motion by 2D/3D Registration. In 2D/3D registration, spatial information on organ location is derived by an iterative comparison of perspective volume renderings, so-called digitally rendered radiographs (DRR) from computed tomography volume data, and planar reference X-rays. Currently, this rendering process is very time consuming, and real-time registration, which should at least provide data on organ position in less than a second, has not come into existence. We present two GPU-based rendering algorithms which generate a DRR of 512 x 512 pixels size from a CT dataset of 53 MB size at a pace of almost 100 Hz. This rendering rate is feasible by applying a number of algorithmic simplifications which range from alternative volume-driven rendering approaches - namely so-called wobbled splatting - to sub-sampling of the DRR-image by means of specialized raycasting techniques. Furthermore, general purpose graphics processing unit (GPGPU) programming paradigms were consequently utilized. Rendering quality and performance as well as the influence on the quality and performance of the overall registration process were measured and analyzed in detail. The results show that both methods are competitive and pave the way for fast motion compensation by rigid and possibly even non-rigid 2D/3D registration and, beyond that, adaptive filtering of motion models in IGRT. (orig.)

Sensitivity study of voxel-based PET image comparison to image registration algorithms

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Yip, Stephen, E-mail: syip@lroc.harvard.edu; Chen, Aileen B.; Berbeco, Ross [Department of Radiation Oncology, Brigham and Women’s Hospital, Dana-Farber Cancer Institute and Harvard Medical School, Boston, Massachusetts 02115 (United States); Aerts, Hugo J. W. L. [Department of Radiation Oncology, Brigham and Women’s Hospital, Dana-Farber Cancer Institute and Harvard Medical School, Boston, Massachusetts 02115 and Department of Radiology, Brigham and Women’s Hospital and Harvard Medical School, Boston, Massachusetts 02115 (United States)

2014-11-01

Purpose: Accurate deformable registration is essential for voxel-based comparison of sequential positron emission tomography (PET) images for proper adaptation of treatment plan and treatment response assessment. The comparison may be sensitive to the method of deformable registration as the optimal algorithm is unknown. This study investigated the impact of registration algorithm choice on therapy response evaluation. Methods: Sixteen patients with 20 lung tumors underwent a pre- and post-treatment computed tomography (CT) and 4D FDG-PET scans before and after chemoradiotherapy. All CT images were coregistered using a rigid and ten deformable registration algorithms. The resulting transformations were then applied to the respective PET images. Moreover, the tumor region defined by a physician on the registered PET images was classified into progressor, stable-disease, and responder subvolumes. Particularly, voxels with standardized uptake value (SUV) decreases >30% were classified as responder, while voxels with SUV increases >30% were progressor. All other voxels were considered stable-disease. The agreement of the subvolumes resulting from difference registration algorithms was assessed by Dice similarity index (DSI). Coefficient of variation (CV) was computed to assess variability of DSI between individual tumors. Root mean square difference (RMS{sub rigid}) of the rigidly registered CT images was used to measure the degree of tumor deformation. RMS{sub rigid} and DSI were correlated by Spearman correlation coefficient (R) to investigate the effect of tumor deformation on DSI. Results: Median DSI{sub rigid} was found to be 72%, 66%, and 80%, for progressor, stable-disease, and responder, respectively. Median DSI{sub deformable} was 63%–84%, 65%–81%, and 82%–89%. Variability of DSI was substantial and similar for both rigid and deformable algorithms with CV > 10% for all subvolumes. Tumor deformation had moderate to significant impact on DSI for progressor

A Robust and Accurate Two-Step Auto-Labeling Conditional Iterative Closest Points (TACICP Algorithm for Three-Dimensional Multi-Modal Carotid Image Registration.

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Hengkai Guo

Full Text Available Atherosclerosis is among the leading causes of death and disability. Combining information from multi-modal vascular images is an effective and efficient way to diagnose and monitor atherosclerosis, in which image registration is a key technique. In this paper a feature-based registration algorithm, Two-step Auto-labeling Conditional Iterative Closed Points (TACICP algorithm, is proposed to align three-dimensional carotid image datasets from ultrasound (US and magnetic resonance (MR. Based on 2D segmented contours, a coarse-to-fine strategy is employed with two steps: rigid initialization step and non-rigid refinement step. Conditional Iterative Closest Points (CICP algorithm is given in rigid initialization step to obtain the robust rigid transformation and label configurations. Then the labels and CICP algorithm with non-rigid thin-plate-spline (TPS transformation model is introduced to solve non-rigid carotid deformation between different body positions. The results demonstrate that proposed TACICP algorithm has achieved an average registration error of less than 0.2mm with no failure case, which is superior to the state-of-the-art feature-based methods.

Efficient nonrigid registration using ranked order statistics

DEFF Research Database (Denmark)

Tennakoon, Ruwan B.; Bab-Hadiashar, Alireza; de Bruijne, Marleen

2013-01-01

of research. In this paper we propose a fast and accurate non-rigid registration method for intra-modality volumetric images. Our approach exploits the information provided by an order statistics based segmentation method, to find the important regions for registration and use an appropriate sampling scheme......Non-rigid image registration techniques are widely used in medical imaging applications. Due to high computational complexities of these techniques, finding appropriate registration method to both reduce the computation burden and increase the registration accuracy has become an intense area...... to target those areas and reduce the registration computation time. A unique advantage of the proposed method is its ability to identify the point of diminishing returns and stop the registration process. Our experiments on registration of real lung CT images, with expert annotated landmarks, show...

A Rigid Image Registration Based on the Nonsubsampled Contourlet Transform and Genetic Algorithms

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Nasreddine Taleb

2010-09-01

Full Text Available Image registration is a fundamental task used in image processing to match two or more images taken at different times, from different sensors or from different viewpoints. The objective is to find in a huge search space of geometric transformations, an acceptable accurate solution in a reasonable time to provide better registered images. Exhaustive search is computationally expensive and the computational cost increases exponentially with the number of transformation parameters and the size of the data set. In this work, we present an efficient image registration algorithm that uses genetic algorithms within a multi-resolution framework based on the Non-Subsampled Contourlet Transform (NSCT. An adaptable genetic algorithm for registration is adopted in order to minimize the search space. This approach is used within a hybrid scheme applying the two techniques fitness sharing and elitism. Two NSCT based methods are proposed for registration. A comparative study is established between these methods and a wavelet based one. Because the NSCT is a shift-invariant multidirectional transform, the second method is adopted for its search speeding up property. Simulation results clearly show that both proposed techniques are really promising methods for image registration compared to the wavelet approach, while the second technique has led to the best performance results of all. Moreover, to demonstrate the effectiveness of these methods, these registration techniques have been successfully applied to register SPOT, IKONOS and Synthetic Aperture Radar (SAR images. The algorithm has been shown to work perfectly well for multi-temporal satellite images as well, even in the presence of noise.

A rigid image registration based on the nonsubsampled contourlet transform and genetic algorithms.

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Meskine, Fatiha; Chikr El Mezouar, Miloud; Taleb, Nasreddine

2010-01-01

Image registration is a fundamental task used in image processing to match two or more images taken at different times, from different sensors or from different viewpoints. The objective is to find in a huge search space of geometric transformations, an acceptable accurate solution in a reasonable time to provide better registered images. Exhaustive search is computationally expensive and the computational cost increases exponentially with the number of transformation parameters and the size of the data set. In this work, we present an efficient image registration algorithm that uses genetic algorithms within a multi-resolution framework based on the Non-Subsampled Contourlet Transform (NSCT). An adaptable genetic algorithm for registration is adopted in order to minimize the search space. This approach is used within a hybrid scheme applying the two techniques fitness sharing and elitism. Two NSCT based methods are proposed for registration. A comparative study is established between these methods and a wavelet based one. Because the NSCT is a shift-invariant multidirectional transform, the second method is adopted for its search speeding up property. Simulation results clearly show that both proposed techniques are really promising methods for image registration compared to the wavelet approach, while the second technique has led to the best performance results of all. Moreover, to demonstrate the effectiveness of these methods, these registration techniques have been successfully applied to register SPOT, IKONOS and Synthetic Aperture Radar (SAR) images. The algorithm has been shown to work perfectly well for multi-temporal satellite images as well, even in the presence of noise.

SU-E-J-209: Verification of 3D Surface Registration Between Stereograms and CT Images

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Han, T; Gifford, K [UT MD Anderson Cancer Center, Houston, TX (United States); Smith, B [MD Anderson Cancer Center, Houston, TX (United States); Salehpour, M [M.D. Anderson Cancer Center, Houston, TX (United States)

2014-06-01

Purpose: Stereography can provide a visualization of the skin surface for radiation therapy patients. The aim of this study was to verify the registration algorithm in a commercial image analysis software, 3dMDVultus, for the fusion of stereograms and CT images. Methods: CT and stereographic scans were acquired of a head phantom and a deformable phantom. CT images were imported in 3dMDVultus and the surface contours were generated by threshold segmentation. Stereograms were reconstructed in 3dMDVultus. The resulting surfaces were registered with Vultus algorithm and then exported to in-house registration software and compared with four algorithms: rigid, affine, non-rigid iterative closest point (ICP) and b-spline algorithm. RMS (root-mean-square residuals of the surface point distances) error between the registered CT and stereogram surfaces was calculated and analyzed. Results: For the head phantom, the maximum RMS error between registered CT surfaces to stereogram was 6.6 mm for Vultus algorithm, whereas the mean RMS error was 0.7 mm. For the deformable phantom, the maximum RMS error was 16.2 mm for Vultus algorithm, whereas the mean RMS error was 4.4 mm. Non-rigid ICP demonstrated the best registration accuracy, as the mean of RMS errors were both within 1 mm. Conclusion: The accuracy of registration algorithm in 3dMDVultus was verified and exceeded RMS of 2 mm for deformable cases. Non-rigid ICP and b-spline algorithms improve the registration accuracy for both phantoms, especially in deformable one. For those patients whose body habitus deforms during radiation therapy, more advanced nonrigid algorithms need to be used.

Value of a probabilistic atlas in medical image segmentation regarding non-rigid registration of abdominal CT scans

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Park, Hyunjin; Meyer, Charles R.

2012-10-01

A probabilistic atlas provides important information to help segmentation and registration applications in medical image analysis. We construct a probabilistic atlas by picking a target geometry and mapping other training scans onto that target and then summing the results into one probabilistic atlas. By choosing an atlas space close to the desired target, we construct an atlas that represents the population well. Image registration used to map one image geometry onto another is a primary task in atlas building. One of the main parameters of registration is the choice of degrees of freedom (DOFs) of the geometric transform. Herein, we measure the effect of the registration's DOFs on the segmentation performance of the resulting probabilistic atlas. Twenty-three normal abdominal CT scans were used, and four organs (liver, spinal cord, left and right kidneys) were segmented for each scan. A well-known manifold learning method, ISOMAP, was used to find the best target space to build an atlas. In summary, segmentation performance was high for high DOF registrations regardless of the chosen target space, while segmentation performance was lowered for low DOF registrations if a target space was far from the best target space. At the 0.05 level of statistical significance, there were no significant differences at high DOF registrations while there were significant differences at low DOF registrations when choosing different targets.

Evaluating a method for automated rigid registration

DEFF Research Database (Denmark)

Darkner, Sune; Vester-Christensen, Martin; Larsen, Rasmus

2007-01-01

to point distance. T-test for common mean are used to determine the performance of the two methods (supported by a Wilcoxon signed rank test). The performance influence of sampling density, sampling quantity, and norms is analyzed using a similar method.......We evaluate a novel method for fully automated rigid registration of 2D manifolds in 3D space based on distance maps, the Gibbs sampler and Iterated Conditional Modes (ICM). The method is tested against the ICP considered as the gold standard for automated rigid registration. Furthermore...

A Novel Technique for Prealignment in Multimodality Medical Image Registration

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Wu Zhou

2014-01-01

Full Text Available Image pair is often aligned initially based on a rigid or affine transformation before a deformable registration method is applied in medical image registration. Inappropriate initial registration may compromise the registration speed or impede the convergence of the optimization algorithm. In this work, a novel technique was proposed for prealignment in both monomodality and multimodality image registration based on statistical correlation of gradient information. A simple and robust algorithm was proposed to determine the rotational differences between two images based on orientation histogram matching accumulated from local orientation of each pixel without any feature extraction. Experimental results showed that it was effective to acquire the orientation angle between two unregistered images with advantages over the existed method based on edge-map in multimodalities. Applying the orientation detection into the registration of CT/MR, T1/T2 MRI, and monomadality images with respect to rigid and nonrigid deformation improved the chances of finding the global optimization of the registration and reduced the search space of optimization.

4D ultrasound and 3D MRI registration of beating heart

International Nuclear Information System (INIS)

Herlambang, N.; Matsumiya, K.; Masamune, K.; Dohi, T.; Liao, H.; Tsukihara, H.; Takamoto, S.

2007-01-01

To realize intra-cardiac surgery without cardio-pulmonary bypass, a medical imaging technique with both high image quality and data acquisition rate that is fast enough to follow heart beat movements is required. In this research, we proposed a method that utilized the image quality of MRI and the speed of ultrasound. We developed a 4D image reconstruction method using image registration of 3D MRI and 4D ultrasound images. The registration method consists of rigid registration between 3D MRI and 3D ultrasound with the same heart beat phase, and non-rigid registration between 3D ultrasound images from different heart beat phases. Non-rigid registration was performed with B-spline based registration using variable spring model. In phantom experiment using balloon phantom, registration accuracy was less than 2 mm for total heart volume variation range of 10%. We applied our registration method on 3D MRI and 4D ultrasound images of a volunteer's beating heart data and confirmed through visual observation that heart beat pattern was well reproduced. (orig.)

Microscopic validation of whole mouse micro-metastatic tumor imaging agents using cryo-imaging and sliding organ image registration

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Liu, Yiqiao; Zhou, Bo; Qutaish, Mohammed; Wilson, David L.

2016-03-01

We created a metastasis imaging, analysis platform consisting of software and multi-spectral cryo-imaging system suitable for evaluating emerging imaging agents targeting micro-metastatic tumor. We analyzed CREKA-Gd in MRI, followed by cryo-imaging which repeatedly sectioned and tiled microscope images of the tissue block face, providing anatomical bright field and molecular fluorescence, enabling 3D microscopic imaging of the entire mouse with single metastatic cell sensitivity. To register MRI volumes to the cryo bright field reference, we used our standard mutual information, non-rigid registration which proceeded: preprocess --> affine --> B-spline non-rigid 3D registration. In this report, we created two modified approaches: mask where we registered locally over a smaller rectangular solid, and sliding organ. Briefly, in sliding organ, we segmented the organ, registered the organ and body volumes separately and combined results. Though sliding organ required manual annotation, it provided the best result as a standard to measure other registration methods. Regularization parameters for standard and mask methods were optimized in a grid search. Evaluations consisted of DICE, and visual scoring of a checkerboard display. Standard had accuracy of 2 voxels in all regions except near the kidney, where there were 5 voxels sliding. After mask and sliding organ correction, kidneys sliding were within 2 voxels, and Dice overlap increased 4%-10% in mask compared to standard. Mask generated comparable results with sliding organ and allowed a semi-automatic process.

A spline-based non-linear diffeomorphism for multimodal prostate registration.

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Mitra, Jhimli; Kato, Zoltan; Martí, Robert; Oliver, Arnau; Lladó, Xavier; Sidibé, Désiré; Ghose, Soumya; Vilanova, Joan C; Comet, Josep; Meriaudeau, Fabrice

2012-08-01

This paper presents a novel method for non-rigid registration of transrectal ultrasound and magnetic resonance prostate images based on a non-linear regularized framework of point correspondences obtained from a statistical measure of shape-contexts. The segmented prostate shapes are represented by shape-contexts and the Bhattacharyya distance between the shape representations is used to find the point correspondences between the 2D fixed and moving images. The registration method involves parametric estimation of the non-linear diffeomorphism between the multimodal images and has its basis in solving a set of non-linear equations of thin-plate splines. The solution is obtained as the least-squares solution of an over-determined system of non-linear equations constructed by integrating a set of non-linear functions over the fixed and moving images. However, this may not result in clinically acceptable transformations of the anatomical targets. Therefore, the regularized bending energy of the thin-plate splines along with the localization error of established correspondences should be included in the system of equations. The registration accuracies of the proposed method are evaluated in 20 pairs of prostate mid-gland ultrasound and magnetic resonance images. The results obtained in terms of Dice similarity coefficient show an average of 0.980±0.004, average 95% Hausdorff distance of 1.63±0.48 mm and mean target registration and target localization errors of 1.60±1.17 mm and 0.15±0.12 mm respectively. Copyright © 2012 Elsevier B.V. All rights reserved.

A framework for automatic creation of gold-standard rigid 3D-2D registration datasets.

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Madan, Hennadii; Pernuš, Franjo; Likar, Boštjan; Špiclin, Žiga

2017-02-01

Advanced image-guided medical procedures incorporate 2D intra-interventional information into pre-interventional 3D image and plan of the procedure through 3D/2D image registration (32R). To enter clinical use, and even for publication purposes, novel and existing 32R methods have to be rigorously validated. The performance of a 32R method can be estimated by comparing it to an accurate reference or gold standard method (usually based on fiducial markers) on the same set of images (gold standard dataset). Objective validation and comparison of methods are possible only if evaluation methodology is standardized, and the gold standard  dataset is made publicly available. Currently, very few such datasets exist and only one contains images of multiple patients acquired during a procedure. To encourage the creation of gold standard 32R datasets, we propose an automatic framework. The framework is based on rigid registration of fiducial markers. The main novelty is spatial grouping of fiducial markers on the carrier device, which enables automatic marker localization and identification across the 3D and 2D images. The proposed framework was demonstrated on clinical angiograms of 20 patients. Rigid 32R computed by the framework was more accurate than that obtained manually, with the respective target registration error below 0.027 mm compared to 0.040 mm. The framework is applicable for gold standard setup on any rigid anatomy, provided that the acquired images contain spatially grouped fiducial markers. The gold standard datasets and software will be made publicly available.

Non-rigid point set registration of curves: registration of the superficial vessel centerlines of the brain

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Marreiros, Filipe M. M.; Wang, Chunliang; Rossitti, Sandro; Smedby, Örjan

2016-03-01

In this study we present a non-rigid point set registration for 3D curves (composed by 3D set of points). The method was evaluated in the task of registration of 3D superficial vessels of the brain where it was used to match vessel centerline points. It consists of a combination of the Coherent Point Drift (CPD) and the Thin-Plate Spline (TPS) semilandmarks. The CPD is used to perform the initial matching of centerline 3D points, while the semilandmark method iteratively relaxes/slides the points. For the evaluation, a Magnetic Resonance Angiography (MRA) dataset was used. Deformations were applied to the extracted vessels centerlines to simulate brain bulging and sinking, using a TPS deformation where a few control points were manipulated to obtain the desired transformation (T1). Once the correspondences are known, the corresponding points are used to define a new TPS deformation(T2). The errors are measured in the deformed space, by transforming the original points using T1 and T2 and measuring the distance between them. To simulate cases where the deformed vessel data is incomplete, parts of the reference vessels were cut and then deformed. Furthermore, anisotropic normally distributed noise was added. The results show that the error estimates (root mean square error and mean error) are below 1 mm, even in the presence of noise and incomplete data.

Effects of x-ray and CT image enhancements on the robustness and accuracy of a rigid 3D/2D image registration

International Nuclear Information System (INIS)

Kim, Jinkoo; Yin Fangfang; Zhao Yang; Kim, Jae Ho

2005-01-01

A rigid body three-dimensional/two-dimensional (3D/2D) registration method has been implemented using mutual information, gradient ascent, and 3D texturemap-based digitally reconstructed radiographs. Nine combinations of commonly used x-ray and computed tomography (CT) image enhancement methods, including window leveling, histogram equalization, and adaptive histogram equalization, were examined to assess their effects on accuracy and robustness of the registration method. From a set of experiments using an anthropomorphic chest phantom, we were able to draw several conclusions. First, the CT and x-ray preprocessing combination with the widest attraction range was the one that linearly stretched the histograms onto the entire display range on both CT and x-ray images. The average attraction ranges of this combination were 71.3 mm and 61.3 deg in the translation and rotation dimensions, respectively, and the average errors were 0.12 deg and 0.47 mm. Second, the combination of the CT image with tissue and bone information and the x-ray images with adaptive histogram equalization also showed subvoxel accuracy, especially the best in the translation dimensions. However, its attraction ranges were the smallest among the examined combinations (on average 36 mm and 19 deg). Last the bone-only information on the CT image did not show convergency property to the correct registration

Validation of non-rigid point-set registration methods using a porcine bladder pelvic phantom

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Zakariaee, Roja; Hamarneh, Ghassan; Brown, Colin J.; Spadinger, Ingrid

2016-01-01

The problem of accurate dose accumulation in fractionated radiotherapy treatment for highly deformable organs, such as bladder, has garnered increasing interest over the past few years. However, more research is required in order to find a robust and efficient solution and to increase the accuracy over the current methods. The purpose of this study was to evaluate the feasibility and accuracy of utilizing non-rigid (affine or deformable) point-set registration in accumulating dose in bladder of different sizes and shapes. A pelvic phantom was built to house an ex vivo porcine bladder with fiducial landmarks adhered onto its surface. Four different volume fillings of the bladder were used (90, 180, 360 and 480 cc). The performance of MATLAB implementations of five different methods were compared, in aligning the bladder contour point-sets. The approaches evaluated were coherent point drift (CPD), gaussian mixture model, shape context, thin-plate spline robust point matching (TPS-RPM) and finite iterative closest point (ICP-finite). The evaluation metrics included registration runtime, target registration error (TRE), root-mean-square error (RMS) and Hausdorff distance (HD). The reference (source) dataset was alternated through all four points-sets, in order to study the effect of reference volume on the registration outcomes. While all deformable algorithms provided reasonable registration results, CPD provided the best TRE values (6.4 mm), and TPS-RPM yielded the best mean RMS and HD values (1.4 and 6.8 mm, respectively). ICP-finite was the fastest technique and TPS-RPM, the slowest.

Validation of non-rigid point-set registration methods using a porcine bladder pelvic phantom

International Nuclear Information System (INIS)

Zakariaee, Roja; Hamarneh, Ghassan; Brown, Colin J; Spadinger, Ingrid

2016-01-01

The problem of accurate dose accumulation in fractionated radiotherapy treatment for highly deformable organs, such as bladder, has garnered increasing interest over the past few years. However, more research is required in order to find a robust and efficient solution and to increase the accuracy over the current methods. The purpose of this study was to evaluate the feasibility and accuracy of utilizing non-rigid (affine or deformable) point-set registration in accumulating dose in bladder of different sizes and shapes. A pelvic phantom was built to house an ex vivo porcine bladder with fiducial landmarks adhered onto its surface. Four different volume fillings of the bladder were used (90, 180, 360 and 480 cc). The performance of MATLAB implementations of five different methods were compared, in aligning the bladder contour point-sets. The approaches evaluated were coherent point drift (CPD), gaussian mixture model, shape context, thin-plate spline robust point matching (TPS-RPM) and finite iterative closest point (ICP-finite). The evaluation metrics included registration runtime, target registration error (TRE), root-mean-square error (RMS) and Hausdorff distance (HD). The reference (source) dataset was alternated through all four points-sets, in order to study the effect of reference volume on the registration outcomes. While all deformable algorithms provided reasonable registration results, CPD provided the best TRE values (6.4 mm), and TPS-RPM yielded the best mean RMS and HD values (1.4 and 6.8 mm, respectively). ICP-finite was the fastest technique and TPS-RPM, the slowest. (paper)

Non-rigid CT/CBCT to CBCT registration for online external beam radiotherapy guidance

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Zachiu, Cornel; de Senneville, Baudouin Denis; Tijssen, Rob H. N.; Kotte, Alexis N. T. J.; Houweling, Antonetta C.; Kerkmeijer, Linda G. W.; Lagendijk, Jan J. W.; Moonen, Chrit T. W.; Ries, Mario

2018-01-01

Image-guided external beam radiotherapy (EBRT) allows radiation dose deposition with a high degree of accuracy and precision. Guidance is usually achieved by estimating the displacements, via image registration, between cone beam computed tomography (CBCT) and computed tomography (CT) images acquired at different stages of the therapy. The resulting displacements are then used to reposition the patient such that the location of the tumor at the time of treatment matches its position during planning. Moreover, ongoing research aims to use CBCT-CT image registration for online plan adaptation. However, CBCT images are usually acquired using a small number of x-ray projections and/or low beam intensities. This often leads to the images being subject to low contrast, low signal-to-noise ratio and artifacts, which ends-up hampering the image registration process. Previous studies addressed this by integrating additional image processing steps into the registration procedure. However, these steps are usually designed for particular image acquisition schemes, therefore limiting their use on a case-by-case basis. In the current study we address CT to CBCT and CBCT to CBCT registration by the means of the recently proposed EVolution registration algorithm. Contrary to previous approaches, EVolution does not require the integration of additional image processing steps in the registration scheme. Moreover, the algorithm requires a low number of input parameters, is easily parallelizable and provides an elastic deformation on a point-by-point basis. Results have shown that relative to a pure CT-based registration, the intrinsic artifacts present in typical CBCT images only have a sub-millimeter impact on the accuracy and precision of the estimated deformation. In addition, the algorithm has low computational requirements, which are compatible with online image-based guidance of EBRT treatments.

Topology-Preserving Rigid Transformation of 2D Digital Images.

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Ngo, Phuc; Passat, Nicolas; Kenmochi, Yukiko; Talbot, Hugues

2014-02-01

We provide conditions under which 2D digital images preserve their topological properties under rigid transformations. We consider the two most common digital topology models, namely dual adjacency and well-composedness. This paper leads to the proposal of optimal preprocessing strategies that ensure the topological invariance of images under arbitrary rigid transformations. These results and methods are proved to be valid for various kinds of images (binary, gray-level, label), thus providing generic and efficient tools, which can be used in particular in the context of image registration and warping.

Nonrigid Image Registration for Head and Neck Cancer Radiotherapy Treatment Planning With PET/CT

International Nuclear Information System (INIS)

Ireland, Rob H.; Dyker, Karen E.; Barber, David C.; Wood, Steven M.; Hanney, Michael B.; Tindale, Wendy B.; Woodhouse, Neil; Hoggard, Nigel; Conway, John; Robinson, Martin H.

2007-01-01

Purpose: Head and neck radiotherapy planning with positron emission tomography/computed tomography (PET/CT) requires the images to be reliably registered with treatment planning CT. Acquiring PET/CT in treatment position is problematic, and in practice for some patients it may be beneficial to use diagnostic PET/CT for radiotherapy planning. Therefore, the aim of this study was first to quantify the image registration accuracy of PET/CT to radiotherapy CT and, second, to assess whether PET/CT acquired in diagnostic position can be registered to planning CT. Methods and Materials: Positron emission tomography/CT acquired in diagnostic and treatment position for five patients with head and neck cancer was registered to radiotherapy planning CT using both rigid and nonrigid image registration. The root mean squared error for each method was calculated from a set of anatomic landmarks marked by four independent observers. Results: Nonrigid and rigid registration errors for treatment position PET/CT to planning CT were 2.77 ± 0.80 mm and 4.96 ± 2.38 mm, respectively, p = 0.001. Applying the nonrigid registration to diagnostic position PET/CT produced a more accurate match to the planning CT than rigid registration of treatment position PET/CT (3.20 ± 1.22 mm and 4.96 ± 2.38 mm, respectively, p = 0.012). Conclusions: Nonrigid registration provides a more accurate registration of head and neck PET/CT to treatment planning CT than rigid registration. In addition, nonrigid registration of PET/CT acquired with patients in a standardized, diagnostic position can provide images registered to planning CT with greater accuracy than a rigid registration of PET/CT images acquired in treatment position. This may allow greater flexibility in the timing of PET/CT for head and neck cancer patients due to undergo radiotherapy

Non-Rigid Contour-Based Registration of Cell Nuclei in 2-D Live Cell Microscopy Images Using a Dynamic Elasticity Model.

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Sorokin, Dmitry V; Peterlik, Igor; Tektonidis, Marco; Rohr, Karl; Matula, Pavel

2018-01-01

The analysis of the pure motion of subnuclear structures without influence of the cell nucleus motion and deformation is essential in live cell imaging. In this paper, we propose a 2-D contour-based image registration approach for compensation of nucleus motion and deformation in fluorescence microscopy time-lapse sequences. The proposed approach extends our previous approach, which uses a static elasticity model to register cell images. Compared with that scheme, the new approach employs a dynamic elasticity model for the forward simulation of nucleus motion and deformation based on the motion of its contours. The contour matching process is embedded as a constraint into the system of equations describing the elastic behavior of the nucleus. This results in better performance in terms of the registration accuracy. Our approach was successfully applied to real live cell microscopy image sequences of different types of cells including image data that was specifically designed and acquired for evaluation of cell image registration methods. An experimental comparison with the existing contour-based registration methods and an intensity-based registration method has been performed. We also studied the dependence of the results on the choice of method parameters.

Dynamic Non-Rigid Objects Reconstruction with a Single RGB-D Sensor

Directory of Open Access Journals (Sweden)

Sen Wang

2018-03-01

Full Text Available This paper deals with the 3D reconstruction problem for dynamic non-rigid objects with a single RGB-D sensor. It is a challenging task as we consider the almost inevitable accumulation error issue in some previous sequential fusion methods and also the possible failure of surface tracking in a long sequence. Therefore, we propose a global non-rigid registration framework and tackle the drifting problem via an explicit loop closure. Our novel scheme starts with a fusion step to get multiple partial scans from the input sequence, followed by a pairwise non-rigid registration and loop detection step to obtain correspondences between neighboring partial pieces and those pieces that form a loop. Then, we perform a global registration procedure to align all those pieces together into a consistent canonical space as guided by those matches that we have established. Finally, our proposed model-update step helps fixing potential misalignments that still exist after the global registration. Both geometric and appearance constraints are enforced during our alignment; therefore, we are able to get the recovered model with accurate geometry as well as high fidelity color maps for the mesh. Experiments on both synthetic and various real datasets have demonstrated the capability of our approach to reconstruct complete and watertight deformable objects.

Multi-stage 3D-2D registration for correction of anatomical deformation in image-guided spine surgery

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Ketcha, M. D.; De Silva, T.; Uneri, A.; Jacobson, M. W.; Goerres, J.; Kleinszig, G.; Vogt, S.; Wolinsky, J.-P.; Siewerdsen, J. H.

2017-06-01

A multi-stage image-based 3D-2D registration method is presented that maps annotations in a 3D image (e.g. point labels annotating individual vertebrae in preoperative CT) to an intraoperative radiograph in which the patient has undergone non-rigid anatomical deformation due to changes in patient positioning or due to the intervention itself. The proposed method (termed msLevelCheck) extends a previous rigid registration solution (LevelCheck) to provide an accurate mapping of vertebral labels in the presence of spinal deformation. The method employs a multi-stage series of rigid 3D-2D registrations performed on sets of automatically determined and increasingly localized sub-images, with the final stage achieving a rigid mapping for each label to yield a locally rigid yet globally deformable solution. The method was evaluated first in a phantom study in which a CT image of the spine was acquired followed by a series of 7 mobile radiographs with increasing degree of deformation applied. Second, the method was validated using a clinical data set of patients exhibiting strong spinal deformation during thoracolumbar spine surgery. Registration accuracy was assessed using projection distance error (PDE) and failure rate (PDE  >  20 mm—i.e. label registered outside vertebra). The msLevelCheck method was able to register all vertebrae accurately for all cases of deformation in the phantom study, improving the maximum PDE of the rigid method from 22.4 mm to 3.9 mm. The clinical study demonstrated the feasibility of the approach in real patient data by accurately registering all vertebral labels in each case, eliminating all instances of failure encountered in the conventional rigid method. The multi-stage approach demonstrated accurate mapping of vertebral labels in the presence of strong spinal deformation. The msLevelCheck method maintains other advantageous aspects of the original LevelCheck method (e.g. compatibility with standard clinical workflow, large

Spatially weighted mutual information image registration for image guided radiation therapy

International Nuclear Information System (INIS)

Park, Samuel B.; Rhee, Frank C.; Monroe, James I.; Sohn, Jason W.

2010-01-01

Purpose: To develop a new metric for image registration that incorporates the (sub)pixelwise differential importance along spatial location and to demonstrate its application for image guided radiation therapy (IGRT). Methods: It is well known that rigid-body image registration with mutual information is dependent on the size and location of the image subset on which the alignment analysis is based [the designated region of interest (ROI)]. Therefore, careful review and manual adjustments of the resulting registration are frequently necessary. Although there were some investigations of weighted mutual information (WMI), these efforts could not apply the differential importance to a particular spatial location since WMI only applies the weight to the joint histogram space. The authors developed the spatially weighted mutual information (SWMI) metric by incorporating an adaptable weight function with spatial localization into mutual information. SWMI enables the user to apply the selected transform to medically ''important'' areas such as tumors and critical structures, so SWMI is neither dominated by, nor neglects the neighboring structures. Since SWMI can be utilized with any weight function form, the authors presented two examples of weight functions for IGRT application: A Gaussian-shaped weight function (GW) applied to a user-defined location and a structures-of-interest (SOI) based weight function. An image registration example using a synthesized 2D image is presented to illustrate the efficacy of SWMI. The convergence and feasibility of the registration method as applied to clinical imaging is illustrated by fusing a prostate treatment planning CT with a clinical cone beam CT (CBCT) image set acquired for patient alignment. Forty-one trials are run to test the speed of convergence. The authors also applied SWMI registration using two types of weight functions to two head and neck cases and a prostate case with clinically acquired CBCT/MVCT image sets. The

Spatially weighted mutual information image registration for image guided radiation therapy.

Science.gov (United States)

Park, Samuel B; Rhee, Frank C; Monroe, James I; Sohn, Jason W

2010-09-01

To develop a new metric for image registration that incorporates the (sub)pixelwise differential importance along spatial location and to demonstrate its application for image guided radiation therapy (IGRT). It is well known that rigid-body image registration with mutual information is dependent on the size and location of the image subset on which the alignment analysis is based [the designated region of interest (ROI)]. Therefore, careful review and manual adjustments of the resulting registration are frequently necessary. Although there were some investigations of weighted mutual information (WMI), these efforts could not apply the differential importance to a particular spatial location since WMI only applies the weight to the joint histogram space. The authors developed the spatially weighted mutual information (SWMI) metric by incorporating an adaptable weight function with spatial localization into mutual information. SWMI enables the user to apply the selected transform to medically "important" areas such as tumors and critical structures, so SWMI is neither dominated by, nor neglects the neighboring structures. Since SWMI can be utilized with any weight function form, the authors presented two examples of weight functions for IGRT application: A Gaussian-shaped weight function (GW) applied to a user-defined location and a structures-of-interest (SOI) based weight function. An image registration example using a synthesized 2D image is presented to illustrate the efficacy of SWMI. The convergence and feasibility of the registration method as applied to clinical imaging is illustrated by fusing a prostate treatment planning CT with a clinical cone beam CT (CBCT) image set acquired for patient alignment. Forty-one trials are run to test the speed of convergence. The authors also applied SWMI registration using two types of weight functions to two head and neck cases and a prostate case with clinically acquired CBCT/ MVCT image sets. The SWMI registration with

CT image registration in sinogram space.

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Mao, Weihua; Li, Tianfang; Wink, Nicole; Xing, Lei

2007-09-01

Object displacement in a CT scan is generally reflected in CT projection data or sinogram. In this work, the direct relationship between object motion and the change of CT projection data (sinogram) is investigated and this knowledge is applied to create a novel algorithm for sinogram registration. Calculated and experimental results demonstrate that the registration technique works well for registering rigid 2D or 3D motion in parallel and fan beam samplings. Problem and solution for 3D sinogram-based registration of metallic fiducials are also addressed. Since the motion is registered before image reconstruction, the presented algorithm is particularly useful when registering images with metal or truncation artifacts. In addition, this algorithm is valuable for dealing with situations where only limited projection data are available, making it appealing for various applications in image guided radiation therapy.

CT image registration in sinogram space

International Nuclear Information System (INIS)

Mao Weihua; Li Tianfang; Wink, Nicole; Xing Lei

2007-01-01

Object displacement in a CT scan is generally reflected in CT projection data or sinogram. In this work, the direct relationship between object motion and the change of CT projection data (sinogram) is investigated and this knowledge is applied to create a novel algorithm for sinogram registration. Calculated and experimental results demonstrate that the registration technique works well for registering rigid 2D or 3D motion in parallel and fan beam samplings. Problem and solution for 3D sinogram-based registration of metallic fiducials are also addressed. Since the motion is registered before image reconstruction, the presented algorithm is particularly useful when registering images with metal or truncation artifacts. In addition, this algorithm is valuable for dealing with situations where only limited projection data are available, making it appealing for various applications in image guided radiation therapy

Use of the CT component of PET-CT to improve PET-MR registration: demonstration in soft-tissue sarcoma

International Nuclear Information System (INIS)

Somer, Edward J; Benatar, Nigel A; O'Doherty, Michael J; Smith, Mike A; Marsden, Paul K

2007-01-01

We have investigated improvements to PET-MR image registration offered by PET-CT scanning. Ten subjects with suspected soft-tissue sarcomas were scanned with an in-line PET-CT and a clinical MR scanner. PET to CT, CT to MR and PET to MR image registrations were performed using a rigid-body external marker technique and rigid and non-rigid voxel-similarity algorithms. PET-MR registration was also performed using transformations derived from the registration of CT to MR. The external marker technique gave fiducial registration errors of 2.1 mm, 5.1 mm and 5.3 mm for PET-CT, PET-MR and CT-MR registration. Target registration errors were 3.9 mm, 9.0 mm and 9.3 mm, respectively. Voxel-based algorithms were evaluated by measuring the distance between corresponding fiducials after registration. Registration errors of 6.4 mm, 14.5 mm and 9.5 mm, respectively, for PET-CT, PET-MR and CT-MR were observed for rigid-body registration while non-rigid registration gave errors of 6.8 mm, 16.3 mm and 7.6 mm for the same modality combinations. The application of rigid and non-rigid CT to MR transformations to accompanying PET data gives significantly reduced PET-MR errors of 10.0 mm and 8.5 mm, respectively. Visual comparison by two independent observers confirmed the improvement over direct PET-MR registration. We conclude that PET-MR registration can be more accurately and reliably achieved using the hybrid technique described than through direct rigid-body registration of PET to MR

ACIR: automatic cochlea image registration

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Al-Dhamari, Ibraheem; Bauer, Sabine; Paulus, Dietrich; Lissek, Friedrich; Jacob, Roland

2017-02-01

Efficient Cochlear Implant (CI) surgery requires prior knowledge of the cochlea's size and its characteristics. This information helps to select suitable implants for different patients. To get these measurements, a segmentation method of cochlea medical images is needed. An important pre-processing step for good cochlea segmentation involves efficient image registration. The cochlea's small size and complex structure, in addition to the different resolutions and head positions during imaging, reveals a big challenge for the automated registration of the different image modalities. In this paper, an Automatic Cochlea Image Registration (ACIR) method for multi- modal human cochlea images is proposed. This method is based on using small areas that have clear structures from both input images instead of registering the complete image. It uses the Adaptive Stochastic Gradient Descent Optimizer (ASGD) and Mattes's Mutual Information metric (MMI) to estimate 3D rigid transform parameters. The use of state of the art medical image registration optimizers published over the last two years are studied and compared quantitatively using the standard Dice Similarity Coefficient (DSC). ACIR requires only 4.86 seconds on average to align cochlea images automatically and to put all the modalities in the same spatial locations without human interference. The source code is based on the tool elastix and is provided for free as a 3D Slicer plugin. Another contribution of this work is a proposed public cochlea standard dataset which can be downloaded for free from a public XNAT server.

SU-E-J-266: A Pitfall of a Deformable Image Registration in Lung Cancer

Energy Technology Data Exchange (ETDEWEB)

Sugawara, Y [The National Center for Global Health and Medicine, Shinjuku, Tokyo (Japan); Tachibana, H [The National Cancer Center Hospital East, Kashiwa, Chiba (Japan); Moriya, S [Komazawa University, Setagaya, Tokyo (Japan); Sawant, A [UT Southwestern Medical Center, Dallas, TX (United States)

2014-06-01

Purpose: For four-dimensional (4D) planning and adaptive radiotherapy, deformable image registration (DIR) is needed and the accuracy is essential. We evaluated the accuracy of one free-downloadable DIR software library package (NiftyReg) and one commercial DIR software (MIM) in lung SBRT cancer patients. Methods: A rigid and non-rigid registrations were implemented to our in-house software. The non-rigid registration algorithm of the NiftyReg and MIM was based on the free-form deformation. The accuracy of the two software was evaluated when contoured structures to peak-inhale and peak-exhale 4DCT image data sets were measured using the dice similarity coefficient (DSC). The evaluation was performed in 20 lung SBRT patients. Results: In our visual evaluation, the eighteen cases show good agreement between the deformed structures for the peak-inhale phase and the peak-exhale phase structures (more than 0.8 DSC value). In the evaluation of the DSC in-house software, averaged DSC values of GTV and lung, heart, spinal cord, stomach and body were 0.862 and 0.979, 0.932, 0.974, 0.860, 0.998, respectively. As the Resultof the registration using the MIM program in the two cases which had less than 0.7 DSC value when analyzed using the in-house software, the DSC value were improved to 0.8. The CT images in a case with low DSC value shows the tumor was surrounded by the structure with the similar CT values, which were the chest wall or the diaphragm. Conclusion: Not only a free-downloadable DIR software but also a commercial software may provide unexpected results and there is a possibility that the results would make us misjudge the treatment planning. Therefore, we recommend that a commissioning test of any DIR software should be performed before clinical use and we should understand the characteristics of the software.

SU-E-J-266: A Pitfall of a Deformable Image Registration in Lung Cancer

International Nuclear Information System (INIS)

Sugawara, Y; Tachibana, H; Moriya, S; Sawant, A

2014-01-01

Purpose: For four-dimensional (4D) planning and adaptive radiotherapy, deformable image registration (DIR) is needed and the accuracy is essential. We evaluated the accuracy of one free-downloadable DIR software library package (NiftyReg) and one commercial DIR software (MIM) in lung SBRT cancer patients. Methods: A rigid and non-rigid registrations were implemented to our in-house software. The non-rigid registration algorithm of the NiftyReg and MIM was based on the free-form deformation. The accuracy of the two software was evaluated when contoured structures to peak-inhale and peak-exhale 4DCT image data sets were measured using the dice similarity coefficient (DSC). The evaluation was performed in 20 lung SBRT patients. Results: In our visual evaluation, the eighteen cases show good agreement between the deformed structures for the peak-inhale phase and the peak-exhale phase structures (more than 0.8 DSC value). In the evaluation of the DSC in-house software, averaged DSC values of GTV and lung, heart, spinal cord, stomach and body were 0.862 and 0.979, 0.932, 0.974, 0.860, 0.998, respectively. As the Resultof the registration using the MIM program in the two cases which had less than 0.7 DSC value when analyzed using the in-house software, the DSC value were improved to 0.8. The CT images in a case with low DSC value shows the tumor was surrounded by the structure with the similar CT values, which were the chest wall or the diaphragm. Conclusion: Not only a free-downloadable DIR software but also a commercial software may provide unexpected results and there is a possibility that the results would make us misjudge the treatment planning. Therefore, we recommend that a commissioning test of any DIR software should be performed before clinical use and we should understand the characteristics of the software

Canny edge-based deformable image registration.

Science.gov (United States)

Kearney, Vasant; Huang, Yihui; Mao, Weihua; Yuan, Baohong; Tang, Liping

2017-02-07

This work focuses on developing a 2D Canny edge-based deformable image registration (Canny DIR) algorithm to register in vivo white light images taken at various time points. This method uses a sparse interpolation deformation algorithm to sparsely register regions of the image with strong edge information. A stability criterion is enforced which removes regions of edges that do not deform in a smooth uniform manner. Using a synthetic mouse surface ground truth model, the accuracy of the Canny DIR algorithm was evaluated under axial rotation in the presence of deformation. The accuracy was also tested using fluorescent dye injections, which were then used for gamma analysis to establish a second ground truth. The results indicate that the Canny DIR algorithm performs better than rigid registration, intensity corrected Demons, and distinctive features for all evaluation matrices and ground truth scenarios. In conclusion Canny DIR performs well in the presence of the unique lighting and shading variations associated with white-light-based image registration.

Automatic motion correction for in vivo human skin optical coherence tomography angiography through combined rigid and nonrigid registration

Science.gov (United States)

Wei, David Wei; Deegan, Anthony J.; Wang, Ruikang K.

2017-06-01

When using optical coherence tomography angiography (OCTA), the development of artifacts due to involuntary movements can severely compromise the visualization and subsequent quantitation of tissue microvasculatures. To correct such an occurrence, we propose a motion compensation method to eliminate artifacts from human skin OCTA by means of step-by-step rigid affine registration, rigid subpixel registration, and nonrigid B-spline registration. To accommodate this remedial process, OCTA is conducted using two matching all-depth volume scans. Affine transformation is first performed on the large vessels of the deep reticular dermis, and then the resulting affine parameters are applied to all-depth vasculatures with a further subpixel registration to refine the alignment between superficial smaller vessels. Finally, the coregistration of both volumes is carried out to result in the final artifact-free composite image via an algorithm based upon cubic B-spline free-form deformation. We demonstrate that the proposed method can provide a considerable improvement to the final en face OCTA images with substantial artifact removal. In addition, the correlation coefficients and peak signal-to-noise ratios of the corrected images are evaluated and compared with those of the original images, further validating the effectiveness of the proposed method. We expect that the proposed method can be useful in improving qualitative and quantitative assessment of the OCTA images of scanned tissue beds.

Do Tumors in the Lung Deform During Normal Respiration? An Image Registration Investigation

International Nuclear Information System (INIS)

Wu Jianzhou; Lei Peng; Shekhar, Raj; Li Huiling; Suntharalingam, Mohan; D'Souza, Warren D.

2009-01-01

Purpose: The purpose of this study was to investigate whether lung tumors may be described adequately using a rigid body assumption or whether they deform during normal respiration. Methods and Materials: Thirty patients with early stage non-small-cell lung cancer underwent four-dimensional (4D) computed tomography (CT) simulation. The gross tumor volume (GTV) was delineated on the 4D CT images. Image registration was performed in the vicinity of the GTV. The volume of interest for registration was the GTV and minimal volume of surrounding non-GTV tissue. Three types of registration were performed: translation only, translation + rotation, and deformable. The GTV contour from end-inhale was mapped to end-exhale using the registration-derived transformation field. The results were evaluated using three metrics: overlap index (OI), root-mean-squared distance (RMS), and Hausdorff distance (HD). Results: After translation only image registration, on average OI increased by 21.3%, RMS and HD reduced by 1.2 mm and 2.0 mm, respectively. The succeeding increases in OI after translation + rotation and deformable registration were 1.1% and 1.4% respectively. The succeeding reductions in RMS were 0.1 mm and 0.2 mm respectively. No reduction in HD was observed after translation + rotation and deformable image registration compared with translation only registration. The difference in the results from the three registration scenarios was independent of GTV size and motion amplitude. Conclusions: The primary effect of normal respiration on lung tumors was the translation of tumors. Rotation and deformation of lung tumors was determined to be minimal.

Vertical dimensional stability and rigidity of occlusal registration materials.

Science.gov (United States)

Walker, Mary P; Wu, Edis; Heckman, M Elizabeth; Alderman, Nicholas

2009-01-01

Dimensionally accurate occlusal registration records are essential for restorative dentistry; moreover, since records are not used immediately or may be used more than once, the registration material should exhibit accuracy over time (a concept known as dimensional stability). It has been speculated that materials with increased hardness or rigidity should produce more accurate registration records due to an increased resistance to distortion. This study compared the rigidity and associated dimensional accuracy of a recently marketed bisacrylic occlusal registration material and a vinyl polysiloxane (VPS). Maxillary and mandibular typodont arches were mounted on a plasterless articulator from which teeth No. 3, 13, and 15 had been removed to simulate edentulous spaces. After preparing teeth No. 2, 4, 12, and 14 as bridge abutments, the remaining teeth were equilibrated selectively to produce even anterior contact. Four digital photographs were taken to make vertical interarch measurements at four locations (teeth No. 3, 7, 10, and 14). Following initial photos (controls), 10 interocclusal records were made using each registration material, with material placed only in the segments in which teeth were prepared. The records were used for mounting the maxillary arch against the mandibular arch after 48, 72, and 120 hours. There were significant effects on vertical dimensional change related to arch location, material, and mounting time. Both materials demonstrated significantly larger posterior vertical openings than anterior vertical openings, while the bisacrylate produced a larger posterior opening than VPS at 48 and 72 hours and a larger anterior opening at all mounting times. There also was a significant difference in hardness/rigidity due to material and measurement time; at all measurement times, bisacrylate exhibited a significantly higher hardness number.

Automated landmark-guided deformable image registration.

Science.gov (United States)

Kearney, Vasant; Chen, Susie; Gu, Xuejun; Chiu, Tsuicheng; Liu, Honghuan; Jiang, Lan; Wang, Jing; Yordy, John; Nedzi, Lucien; Mao, Weihua

2015-01-07

The purpose of this work is to develop an automated landmark-guided deformable image registration (LDIR) algorithm between the planning CT and daily cone-beam CT (CBCT) with low image quality. This method uses an automated landmark generation algorithm in conjunction with a local small volume gradient matching search engine to map corresponding landmarks between the CBCT and the planning CT. The landmarks act as stabilizing control points in the following Demons deformable image registration. LDIR is implemented on graphics processing units (GPUs) for parallel computation to achieve ultra fast calculation. The accuracy of the LDIR algorithm has been evaluated on a synthetic case in the presence of different noise levels and data of six head and neck cancer patients. The results indicate that LDIR performed better than rigid registration, Demons, and intensity corrected Demons for all similarity metrics used. In conclusion, LDIR achieves high accuracy in the presence of multimodality intensity mismatch and CBCT noise contamination, while simultaneously preserving high computational efficiency.

Automated landmark-guided deformable image registration

International Nuclear Information System (INIS)

Kearney, Vasant; Chen, Susie; Gu, Xuejun; Chiu, Tsuicheng; Liu, Honghuan; Jiang, Lan; Wang, Jing; Yordy, John; Nedzi, Lucien; Mao, Weihua

2015-01-01

The purpose of this work is to develop an automated landmark-guided deformable image registration (LDIR) algorithm between the planning CT and daily cone-beam CT (CBCT) with low image quality. This method uses an automated landmark generation algorithm in conjunction with a local small volume gradient matching search engine to map corresponding landmarks between the CBCT and the planning CT. The landmarks act as stabilizing control points in the following Demons deformable image registration. LDIR is implemented on graphics processing units (GPUs) for parallel computation to achieve ultra fast calculation. The accuracy of the LDIR algorithm has been evaluated on a synthetic case in the presence of different noise levels and data of six head and neck cancer patients. The results indicate that LDIR performed better than rigid registration, Demons, and intensity corrected Demons for all similarity metrics used. In conclusion, LDIR achieves high accuracy in the presence of multimodality intensity mismatch and CBCT noise contamination, while simultaneously preserving high computational efficiency. (paper)

Modeling susceptibility difference artifacts produced by metallic implants in magnetic resonance imaging with point-based thin-plate spline image registration.

Science.gov (United States)

Pauchard, Y; Smith, M; Mintchev, M

2004-01-01

Magnetic resonance imaging (MRI) suffers from geometric distortions arising from various sources. One such source are the non-linearities associated with the presence of metallic implants, which can profoundly distort the obtained images. These non-linearities result in pixel shifts and intensity changes in the vicinity of the implant, often precluding any meaningful assessment of the entire image. This paper presents a method for correcting these distortions based on non-rigid image registration techniques. Two images from a modelled three-dimensional (3D) grid phantom were subjected to point-based thin-plate spline registration. The reference image (without distortions) was obtained from a grid model including a spherical implant, and the corresponding test image containing the distortions was obtained using previously reported technique for spatial modelling of magnetic susceptibility artifacts. After identifying the nonrecoverable area in the distorted image, the calculated spline model was able to quantitatively account for the distortions, thus facilitating their compensation. Upon the completion of the compensation procedure, the non-recoverable area was removed from the reference image and the latter was compared to the compensated image. Quantitative assessment of the goodness of the proposed compensation technique is presented.

Multimodality image registration with software: state-of-the-art

International Nuclear Information System (INIS)

Slomka, Piotr J.; Baum, Richard P.

2009-01-01

Multimodality image integration of functional and anatomical data can be performed by means of dedicated hybrid imaging systems or by software image co-registration techniques. Hybrid positron emission tomography (PET)/computed tomography (CT) systems have found wide acceptance in oncological imaging, while software registration techniques have a significant role in patient-specific, cost-effective, and radiation dose-effective application of integrated imaging. Software techniques allow accurate (2-3 mm) rigid image registration of brain PET with CT and MRI. Nonlinear techniques are used in whole-body image registration, and recent developments allow for significantly accelerated computing times. Nonlinear software registration of PET with CT or MRI is required for multimodality radiation planning. Difficulties remain in the validation of nonlinear registration of soft tissue organs. The utilization of software-based multimodality image integration in a clinical environment is sometimes hindered by the lack of appropriate picture archiving and communication systems (PACS) infrastructure needed to efficiently and automatically integrate all available images into one common database. In cardiology applications, multimodality PET/single photon emission computed tomography and coronary CT angiography imaging is typically not required unless the results of one of the tests are equivocal. Software image registration is likely to be used in a complementary fashion with hybrid PET/CT or PET/magnetic resonance imaging systems. Software registration of stand-alone scans ''paved the way'' for the clinical application of hybrid scanners, demonstrating practical benefits of image integration before the hybrid dual-modality devices were available. (orig.)

Anatomical accuracy of lesion localization. Retrospective interactive rigid image registration between 18F-FDG-PET and X-ray CT

International Nuclear Information System (INIS)

Noemayr, A.; Roemer, W.; Kuwert, T.; Hothorn, T.; Pfahlberg, A.; Hornegger, J.; Bautz, W.

2005-01-01

The aim of this study was to evaluate the anatomical accuracy and reproducibility of retrospective interactive rigid image registration (RIR) between routinely archived X-ray computer tomography (CT) and positron emission tomography performed with 18 F-deoxyglucose (FDG-PET) in oncological patients. Methods: two observers registered PET and CT data obtained in 37 patients using a commercially available image fusion tool. RIR was performed separately for the thorax and the abdomen using physiological FDG uptake in several organs as a reference. One observer performed the procedure twice (01a and 01b), another person once (02). For 94 malignant lesions, clearly visible in CT and PET, the signed and absolute distances between their representation on PET and CT were measured in X-, Y-, and Z-direction with reference to a coordinate system centered in the CT representation of each lesion (X-, Y-, Z-distances). Results: the mean differences of the signed and absolute distances between 01a, 01b, and 02 did not exceed 3 mm in any dimension. The absolute X-, Y-, and Z-distances ranged between 0.57 ± 0.58 cm for 01a (X-direction) and 1.12 ± 1.28 cm for 02 (Z-direction). When averaging the absolute distances measured by 01a, 01b, and 02, the percentage of lesions misregistered by less than 1.5 cm was 91% for the X-, 88% for the Y-, and 77% for the Z-direction. The larger error of fusion determined for the remaining lesions was caused by non-rigid body transformations due to differences in breathing, arm position, or bowel movements between the two examinations. Mixed effects analysis of the signed and absolute X-, Y-, and Z-distances disclosed a significantly greater misalignment in the thorax than in the abdomen as well as axially than transaxially. Conclusion: the anatomical inaccuracy of RIR can be expected to be <1.5 cm for the majority of neoplastic foci. errors of alignment are bigger in the thorax and in Z-direction, due to non-rigid body transformations caused, e

Optic disc boundary segmentation from diffeomorphic demons registration of monocular fundus image sequences versus 3D visualization of stereo fundus image pairs for automated early stage glaucoma assessment

Science.gov (United States)

Gatti, Vijay; Hill, Jason; Mitra, Sunanda; Nutter, Brian

2014-03-01

Despite the current availability in resource-rich regions of advanced technologies in scanning and 3-D imaging in current ophthalmology practice, world-wide screening tests for early detection and progression of glaucoma still consist of a variety of simple tools, including fundus image-based parameters such as CDR (cup to disc diameter ratio) and CAR (cup to disc area ratio), especially in resource -poor regions. Reliable automated computation of the relevant parameters from fundus image sequences requires robust non-rigid registration and segmentation techniques. Recent research work demonstrated that proper non-rigid registration of multi-view monocular fundus image sequences could result in acceptable segmentation of cup boundaries for automated computation of CAR and CDR. This research work introduces a composite diffeomorphic demons registration algorithm for segmentation of cup boundaries from a sequence of monocular images and compares the resulting CAR and CDR values with those computed manually by experts and from 3-D visualization of stereo pairs. Our preliminary results show that the automated computation of CDR and CAR from composite diffeomorphic segmentation of monocular image sequences yield values comparable with those from the other two techniques and thus may provide global healthcare with a cost-effective yet accurate tool for management of glaucoma in its early stage.

Efficient Constrained Local Model Fitting for Non-Rigid Face Alignment.

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Lucey, Simon; Wang, Yang; Cox, Mark; Sridharan, Sridha; Cohn, Jeffery F

2009-11-01

Active appearance models (AAMs) have demonstrated great utility when being employed for non-rigid face alignment/tracking. The "simultaneous" algorithm for fitting an AAM achieves good non-rigid face registration performance, but has poor real time performance (2-3 fps). The "project-out" algorithm for fitting an AAM achieves faster than real time performance (> 200 fps) but suffers from poor generic alignment performance. In this paper we introduce an extension to a discriminative method for non-rigid face registration/tracking referred to as a constrained local model (CLM). Our proposed method is able to achieve superior performance to the "simultaneous" AAM algorithm along with real time fitting speeds (35 fps). We improve upon the canonical CLM formulation, to gain this performance, in a number of ways by employing: (i) linear SVMs as patch-experts, (ii) a simplified optimization criteria, and (iii) a composite rather than additive warp update step. Most notably, our simplified optimization criteria for fitting the CLM divides the problem of finding a single complex registration/warp displacement into that of finding N simple warp displacements. From these N simple warp displacements, a single complex warp displacement is estimated using a weighted least-squares constraint. Another major advantage of this simplified optimization lends from its ability to be parallelized, a step which we also theoretically explore in this paper. We refer to our approach for fitting the CLM as the "exhaustive local search" (ELS) algorithm. Experiments were conducted on the CMU Multi-PIE database.

Real-time motion compensated patient positioning and non-rigid deformation estimation using 4-D shape priors.

Science.gov (United States)

Wasza, Jakob; Bauer, Sebastian; Hornegger, Joachim

2012-01-01

Over the last years, range imaging (RI) techniques have been proposed for patient positioning and respiration analysis in motion compensation. Yet, current RI based approaches for patient positioning employ rigid-body transformations, thus neglecting free-form deformations induced by respiratory motion. Furthermore, RI based respiration analysis relies on non-rigid registration techniques with run-times of several seconds. In this paper we propose a real-time framework based on RI to perform respiratory motion compensated positioning and non-rigid surface deformation estimation in a joint manner. The core of our method are pre-procedurally obtained 4-D shape priors that drive the intra-procedural alignment of the patient to the reference state, simultaneously yielding a rigid-body table transformation and a free-form deformation accounting for respiratory motion. We show that our method outperforms conventional alignment strategies by a factor of 3.0 and 2.3 in the rotation and translation accuracy, respectively. Using a GPU based implementation, we achieve run-times of 40 ms.

3D registration of surfaces for change detection in medical images

Science.gov (United States)

Fisher, Elizabeth; van der Stelt, Paul F.; Dunn, Stanley M.

1997-04-01

Spatial registration of data sets is essential for quantifying changes that take place over time in cases where the position of a patient with respect to the sensor has been altered. Changes within the region of interest can be problematic for automatic methods of registration. This research addresses the problem of automatic 3D registration of surfaces derived from serial, single-modality images for the purpose of quantifying changes over time. The registration algorithm utilizes motion-invariant, curvature- based geometric properties to derive an approximation to an initial rigid transformation to align two image sets. Following the initial registration, changed portions of the surface are detected and excluded before refining the transformation parameters. The performance of the algorithm was tested using simulation experiments. To quantitatively assess the registration, random noise at various levels, known rigid motion transformations, and analytically-defined volume changes were applied to the initial surface data acquired from models of teeth. These simulation experiments demonstrated that the calculated transformation parameters were accurate to within 1.2 percent of the total applied rotation and 2.9 percent of the total applied translation, even at the highest applied noise levels and simulated wear values.

Development and application of pulmonary structure-function registration methods: towards pulmonary image-guidance tools for improved airway targeted therapies and outcomes

Science.gov (United States)

Guo, Fumin; Pike, Damien; Svenningsen, Sarah; Coxson, Harvey O.; Drozd, John J.; Yuan, Jing; Fenster, Aaron; Parraga, Grace

2014-03-01

Objectives: We aimed to develop a way to rapidly generate multi-modality (MRI-CT) pulmonary imaging structurefunction maps using novel non-rigid image registration methods. This objective is part of our overarching goal to provide an image processing pipeline to generate pulmonary structure-function maps and guide airway-targeted therapies. Methods: Anatomical 1H and functional 3He MRI were acquired in 5 healthy asymptomatic ex-smokers and 7 ex-smokers with chronic obstructive pulmonary disease (COPD) at inspiration breath-hold. Thoracic CT was performed within ten minutes of MRI using the same breath-hold volume. Landmark-based affine registration methods previously validated for imaging of COPD, was based on corresponding fiducial markers located in both CT and 1H MRI coronal slices and compared with shape-based CT-MRI non-rigid registration. Shape-based CT-MRI registration was developed by first identifying the shapes of the lung cavities manually, and then registering the two shapes using affine and thin-plate spline algorithms. We compared registration accuracy using the fiducial localization error (FLE) and target registration error (TRE). Results: For landmark-based registration, the TRE was 8.4±5.3 mm for whole lung and 7.8±4.6 mm for the R and L lungs registered independently (p=0.4). For shape-based registration, the TRE was 8.0±4.6 mm for whole lung as compared to 6.9±4.4 mm for the R and L lung registered independently and this difference was significant (p=0.01). The difference for shape-based (6.9±4.4 mm) and landmark-based R and L lung registration (7.8±4.6 mm) was also significant (p=.04) Conclusion: Shape-based registration TRE was significantly improved compared to landmark-based registration when considering L and R lungs independently.

3D ultrasound-CT registration of the liver using combined landmark-intensity information

International Nuclear Information System (INIS)

Lange, Thomas; Schlag, Peter M.; Papenberg, Nils; Heldmann, Stefan; Modersitzki, Jan; Fischer, Bernd; Lamecker, Hans

2009-01-01

An important issue in computer-assisted surgery of the liver is a fast and reliable transfer of preoperative resection plans to the intraoperative situation. One problem is to match the planning data, derived from preoperative CT or MR images, with 3D ultrasound images of the liver, acquired during surgery. As the liver deforms significantly in the intraoperative situation non-rigid registration is necessary. This is a particularly challenging task because pre- and intraoperative image data stem from different modalities and ultrasound images are generally very noisy. One way to overcome these problems is to incorporate prior knowledge into the registration process. We propose a method of combining anatomical landmark information with a fast non-parametric intensity registration approach. Mathematically, this leads to a constrained optimization problem. As distance measure we use the normalized gradient field which allows for multimodal image registration. A qualitative and quantitative validation on clinical liver data sets of three different patients has been performed. We used the distance of dense corresponding points on vessel center lines for quantitative validation. The combined landmark and intensity approach improves the mean and percentage of point distances above 3 mm compared to rigid and thin-plate spline registration based only on landmarks. The proposed algorithm offers the possibility to incorporate additional a priori knowledge - in terms of few landmarks - provided by a human expert into a non-rigid registration process. (orig.)

Multimodality image registration with software: state-of-the-art

Energy Technology Data Exchange (ETDEWEB)

Slomka, Piotr J. [Cedars-Sinai Medical Center, AIM Program/Department of Imaging, Los Angeles, CA (United States); University of California, David Geffen School of Medicine, Los Angeles, CA (United States); Baum, Richard P. [Center for PET, Department of Nuclear Medicine, Bad Berka (Germany)

2009-03-15

Multimodality image integration of functional and anatomical data can be performed by means of dedicated hybrid imaging systems or by software image co-registration techniques. Hybrid positron emission tomography (PET)/computed tomography (CT) systems have found wide acceptance in oncological imaging, while software registration techniques have a significant role in patient-specific, cost-effective, and radiation dose-effective application of integrated imaging. Software techniques allow accurate (2-3 mm) rigid image registration of brain PET with CT and MRI. Nonlinear techniques are used in whole-body image registration, and recent developments allow for significantly accelerated computing times. Nonlinear software registration of PET with CT or MRI is required for multimodality radiation planning. Difficulties remain in the validation of nonlinear registration of soft tissue organs. The utilization of software-based multimodality image integration in a clinical environment is sometimes hindered by the lack of appropriate picture archiving and communication systems (PACS) infrastructure needed to efficiently and automatically integrate all available images into one common database. In cardiology applications, multimodality PET/single photon emission computed tomography and coronary CT angiography imaging is typically not required unless the results of one of the tests are equivocal. Software image registration is likely to be used in a complementary fashion with hybrid PET/CT or PET/magnetic resonance imaging systems. Software registration of stand-alone scans ''paved the way'' for the clinical application of hybrid scanners, demonstrating practical benefits of image integration before the hybrid dual-modality devices were available. (orig.)

A Variational Approach to Video Registration with Subspace Constraints.

Science.gov (United States)

Garg, Ravi; Roussos, Anastasios; Agapito, Lourdes

2013-01-01

This paper addresses the problem of non-rigid video registration, or the computation of optical flow from a reference frame to each of the subsequent images in a sequence, when the camera views deformable objects. We exploit the high correlation between 2D trajectories of different points on the same non-rigid surface by assuming that the displacement of any point throughout the sequence can be expressed in a compact way as a linear combination of a low-rank motion basis. This subspace constraint effectively acts as a trajectory regularization term leading to temporally consistent optical flow. We formulate it as a robust soft constraint within a variational framework by penalizing flow fields that lie outside the low-rank manifold. The resulting energy functional can be decoupled into the optimization of the brightness constancy and spatial regularization terms, leading to an efficient optimization scheme. Additionally, we propose a novel optimization scheme for the case of vector valued images, based on the dualization of the data term. This allows us to extend our approach to deal with colour images which results in significant improvements on the registration results. Finally, we provide a new benchmark dataset, based on motion capture data of a flag waving in the wind, with dense ground truth optical flow for evaluation of multi-frame optical flow algorithms for non-rigid surfaces. Our experiments show that our proposed approach outperforms state of the art optical flow and dense non-rigid registration algorithms.

Deformable image registration for cone-beam CT guided transoral robotic base-of-tongue surgery

International Nuclear Information System (INIS)

Reaungamornrat, S; Liu, W P; Otake, Y; Uneri, A; Siewerdsen, J H; Taylor, R H; Wang, A S; Nithiananthan, S; Schafer, S; Tryggestad, E; Richmon, J; Sorger, J M

2013-01-01

Transoral robotic surgery (TORS) offers a minimally invasive approach to resection of base-of-tongue tumors. However, precise localization of the surgical target and adjacent critical structures can be challenged by the highly deformed intraoperative setup. We propose a deformable registration method using intraoperative cone-beam computed tomography (CBCT) to accurately align preoperative CT or MR images with the intraoperative scene. The registration method combines a Gaussian mixture (GM) model followed by a variation of the Demons algorithm. First, following segmentation of the volume of interest (i.e. volume of the tongue extending to the hyoid), a GM model is applied to surface point clouds for rigid initialization (GM rigid) followed by nonrigid deformation (GM nonrigid). Second, the registration is refined using the Demons algorithm applied to distance map transforms of the (GM-registered) preoperative image and intraoperative CBCT. Performance was evaluated in repeat cadaver studies (25 image pairs) in terms of target registration error (TRE), entropy correlation coefficient (ECC) and normalized pointwise mutual information (NPMI). Retraction of the tongue in the TORS operative setup induced gross deformation >30 mm. The mean TRE following the GM rigid, GM nonrigid and Demons steps was 4.6, 2.1 and 1.7 mm, respectively. The respective ECC was 0.57, 0.70 and 0.73, and NPMI was 0.46, 0.57 and 0.60. Registration accuracy was best across the superior aspect of the tongue and in proximity to the hyoid (by virtue of GM registration of surface points on these structures). The Demons step refined registration primarily in deeper portions of the tongue further from the surface and hyoid bone. Since the method does not use image intensities directly, it is suitable to multi-modality registration of preoperative CT or MR with intraoperative CBCT. Extending the 3D image registration to the fusion of image and planning data in stereo-endoscopic video is anticipated to

Comparison of numerical results between related shapes using a non-rigid mapping with statistical quantication of uncertainty

CSIR Research Space (South Africa)

Jansen van Rensburg, Gerhardus J

2011-10-01

Full Text Available In the present study, numerical results obtained on different but related shapes are compared by using a non-rigid mapping. Non-rigid registration is employed to obtain mesh representations of different human skull geometries with the same mesh...

Multi-modal image registration: matching MRI with histology

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Alic, Lejla; Haeck, Joost C.; Klein, Stefan; Bol, Karin; van Tiel, Sandra T.; Wielopolski, Piotr A.; Bijster, Magda; Niessen, Wiro J.; Bernsen, Monique; Veenland, Jifke F.; de Jong, Marion

2010-03-01

Spatial correspondence between histology and multi sequence MRI can provide information about the capabilities of non-invasive imaging to characterize cancerous tissue. However, shrinkage and deformation occurring during the excision of the tumor and the histological processing complicate the co registration of MR images with histological sections. This work proposes a methodology to establish a detailed 3D relation between histology sections and in vivo MRI tumor data. The key features of the methodology are a very dense histological sampling (up to 100 histology slices per tumor), mutual information based non-rigid B-spline registration, the utilization of the whole 3D data sets, and the exploitation of an intermediate ex vivo MRI. In this proof of concept paper, the methodology was applied to one tumor. We found that, after registration, the visual alignment of tumor borders and internal structures was fairly accurate. Utilizing the intermediate ex vivo MRI, it was possible to account for changes caused by the excision of the tumor: we observed a tumor expansion of 20%. Also the effects of fixation, dehydration and histological sectioning could be determined: 26% shrinkage of the tumor was found. The annotation of viable tissue, performed in histology and transformed to the in vivo MRI, matched clearly with high intensity regions in MRI. With this methodology, histological annotation can be directly related to the corresponding in vivo MRI. This is a vital step for the evaluation of the feasibility of multi-spectral MRI to depict histological groundtruth.

Diffusion Maps for Multimodal Registration

Directory of Open Access Journals (Sweden)

Gemma Piella

2014-06-01

Full Text Available Multimodal image registration is a difficult task, due to the significant intensity variations between the images. A common approach is to use sophisticated similarity measures, such as mutual information, that are robust to those intensity variations. However, these similarity measures are computationally expensive and, moreover, often fail to capture the geometry and the associated dynamics linked with the images. Another approach is the transformation of the images into a common space where modalities can be directly compared. Within this approach, we propose to register multimodal images by using diffusion maps to describe the geometric and spectral properties of the data. Through diffusion maps, the multimodal data is transformed into a new set of canonical coordinates that reflect its geometry uniformly across modalities, so that meaningful correspondences can be established between them. Images in this new representation can then be registered using a simple Euclidean distance as a similarity measure. Registration accuracy was evaluated on both real and simulated brain images with known ground-truth for both rigid and non-rigid registration. Results showed that the proposed approach achieved higher accuracy than the conventional approach using mutual information.

Medical image registration by combining global and local information: a chain-type diffeomorphic demons algorithm

International Nuclear Information System (INIS)

Liu, Xiaozheng; Yuan, Zhenming; Zhu, Junming; Xu, Dongrong

2013-01-01

The demons algorithm is a popular algorithm for non-rigid image registration because of its computational efficiency and simple implementation. The deformation forces of the classic demons algorithm were derived from image gradients by considering the deformation to decrease the intensity dissimilarity between images. However, the methods using the difference of image intensity for medical image registration are easily affected by image artifacts, such as image noise, non-uniform imaging and partial volume effects. The gradient magnitude image is constructed from the local information of an image, so the difference in a gradient magnitude image can be regarded as more reliable and robust for these artifacts. Then, registering medical images by considering the differences in both image intensity and gradient magnitude is a straightforward selection. In this paper, based on a diffeomorphic demons algorithm, we propose a chain-type diffeomorphic demons algorithm by combining the differences in both image intensity and gradient magnitude for medical image registration. Previous work had shown that the classic demons algorithm can be considered as an approximation of a second order gradient descent on the sum of the squared intensity differences. By optimizing the new dissimilarity criteria, we also present a set of new demons forces which were derived from the gradients of the image and gradient magnitude image. We show that, in controlled experiments, this advantage is confirmed, and yields a fast convergence. (paper)

Real-time non-rigid target tracking for ultrasound-guided clinical interventions

Science.gov (United States)

Zachiu, C.; Ries, M.; Ramaekers, P.; Guey, J.-L.; Moonen, C. T. W.; de Senneville, B. Denis

2017-10-01

Biological motion is a problem for non- or mini-invasive interventions when conducted in mobile/deformable organs due to the targeted pathology moving/deforming with the organ. This may lead to high miss rates and/or incomplete treatment of the pathology. Therefore, real-time tracking of the target anatomy during the intervention would be beneficial for such applications. Since the aforementioned interventions are often conducted under B-mode ultrasound (US) guidance, target tracking can be achieved via image registration, by comparing the acquired US images to a separate image established as positional reference. However, such US images are intrinsically altered by speckle noise, introducing incoherent gray-level intensity variations. This may prove problematic for existing intensity-based registration methods. In the current study we address US-based target tracking by employing the recently proposed EVolution registration algorithm. The method is, by construction, robust to transient gray-level intensities. Instead of directly matching image intensities, EVolution aligns similar contrast patterns in the images. Moreover, the displacement is computed by evaluating a matching criterion for image sub-regions rather than on a point-by-point basis, which typically provides more robust motion estimates. However, unlike similar previously published approaches, which assume rigid displacements in the image sub-regions, the EVolution algorithm integrates the matching criterion in a global functional, allowing the estimation of an elastic dense deformation. The approach was validated for soft tissue tracking under free-breathing conditions on the abdomen of seven healthy volunteers. Contact echography was performed on all volunteers, while three of the volunteers also underwent standoff echography. Each of the two modalities is predominantly specific to a particular type of non- or mini-invasive clinical intervention. The method demonstrated on average an accuracy of�

An efficient direct method for image registration of flat objects

Science.gov (United States)

Nikolaev, Dmitry; Tihonkih, Dmitrii; Makovetskii, Artyom; Voronin, Sergei

2017-09-01

Image alignment of rigid surfaces is a rapidly developing area of research and has many practical applications. Alignment methods can be roughly divided into two types: feature-based methods and direct methods. Known SURF and SIFT algorithms are examples of the feature-based methods. Direct methods refer to those that exploit the pixel intensities without resorting to image features and image-based deformations are general direct method to align images of deformable objects in 3D space. Nevertheless, it is not good for the registration of images of 3D rigid objects since the underlying structure cannot be directly evaluated. In the article, we propose a model that is suitable for image alignment of rigid flat objects under various illumination models. The brightness consistency assumptions used for reconstruction of optimal geometrical transformation. Computer simulation results are provided to illustrate the performance of the proposed algorithm for computing of an accordance between pixels of two images.

An algorithm for longitudinal registration of PET/CT images acquired during neoadjuvant chemotherapy in breast cancer: preliminary results.

Science.gov (United States)

Li, Xia; Abramson, Richard G; Arlinghaus, Lori R; Chakravarthy, Anuradha Bapsi; Abramson, Vandana; Mayer, Ingrid; Farley, Jaime; Delbeke, Dominique; Yankeelov, Thomas E

2012-11-16

By providing estimates of tumor glucose metabolism, 18F-fluorodeoxyglucose positron emission tomography (FDG-PET) can potentially characterize the response of breast tumors to treatment. To assess therapy response, serial measurements of FDG-PET parameters (derived from static and/or dynamic images) can be obtained at different time points during the course of treatment. However, most studies track the changes in average parameter values obtained from the whole tumor, thereby discarding all spatial information manifested in tumor heterogeneity. Here, we propose a method whereby serially acquired FDG-PET breast data sets can be spatially co-registered to enable the spatial comparison of parameter maps at the voxel level. The goal is to optimally register normal tissues while simultaneously preventing tumor distortion. In order to accomplish this, we constructed a PET support device to enable PET/CT imaging of the breasts of ten patients in the prone position and applied a mutual information-based rigid body registration followed by a non-rigid registration. The non-rigid registration algorithm extended the adaptive bases algorithm (ABA) by incorporating a tumor volume-preserving constraint, which computed the Jacobian determinant over the tumor regions as outlined on the PET/CT images, into the cost function. We tested this approach on ten breast cancer patients undergoing neoadjuvant chemotherapy. By both qualitative and quantitative evaluation, our constrained algorithm yielded significantly less tumor distortion than the unconstrained algorithm: considering the tumor volume determined from standard uptake value maps, the post-registration median tumor volume changes, and the 25th and 75th quantiles were 3.42% (0%, 13.39%) and 16.93% (9.21%, 49.93%) for the constrained and unconstrained algorithms, respectively (p = 0.002), while the bending energy (a measure of the smoothness of the deformation) was 0.0015 (0.0005, 0.012) and 0.017 (0.005, 0

Qualitative and quantitative evaluation of rigid and deformable motion correction algorithms using dual-energy CT images in view of application to CT perfusion measurements in abdominal organs affected by breathing motion.

Science.gov (United States)

Skornitzke, S; Fritz, F; Klauss, M; Pahn, G; Hansen, J; Hirsch, J; Grenacher, L; Kauczor, H-U; Stiller, W

2015-02-01

To compare six different scenarios for correcting for breathing motion in abdominal dual-energy CT (DECT) perfusion measurements. Rigid [RRComm(80 kVp)] and non-rigid [NRComm(80 kVp)] registration of commercially available CT perfusion software, custom non-rigid registration [NRCustom(80 kVp], demons algorithm) and a control group [CG(80 kVp)] without motion correction were evaluated using 80 kVp images. Additionally, NRCustom was applied to dual-energy (DE)-blended [NRCustom(DE)] and virtual non-contrast [NRCustom(VNC)] images, yielding six evaluated scenarios. After motion correction, perfusion maps were calculated using a combined maximum slope/Patlak model. For qualitative evaluation, three blinded radiologists independently rated motion correction quality and resulting perfusion maps on a four-point scale (4 = best, 1 = worst). For quantitative evaluation, relative changes in metric values, R(2) and residuals of perfusion model fits were calculated. For motion-corrected images, mean ratings differed significantly [NRCustom(80 kVp) and NRCustom(DE), 3.3; NRComm(80 kVp), 3.1; NRCustom(VNC), 2.9; RRComm(80 kVp), 2.7; CG(80 kVp), 2.7; all p VNC), 22.8%; RRComm(80 kVp), 0.6%; CG(80 kVp), 0%]. Regarding perfusion maps, NRCustom(80 kVp) and NRCustom(DE) were rated highest [NRCustom(80 kVp), 3.1; NRCustom(DE), 3.0; NRComm(80 kVp), 2.8; NRCustom(VNC), 2.6; CG(80 kVp), 2.5; RRComm(80 kVp), 2.4] and had significantly higher R(2) and lower residuals. Correlation between qualitative and quantitative evaluation was low to moderate. Non-rigid motion correction improves spatial alignment of the target region and fit of CT perfusion models. Using DE-blended and DE-VNC images for deformable registration offers no significant improvement. Non-rigid algorithms improve the quality of abdominal CT perfusion measurements but do not benefit from DECT post processing.

Rigid 3D-3D registration of TOF MRA integrating vessel segmentation for quantification of recurrence volumes after coiling cerebral aneurysm

International Nuclear Information System (INIS)

Saering, Dennis; Forkert, Nils Daniel; Fiehler, Jens; Ries, Thorsten

2012-01-01

A fast and reproducible quantification of the recurrence volume of coiled aneurysms is required to enable a more timely evaluation of new coils. This paper presents two registration schemes for the semi-automatic quantification of aneurysm recurrence volumes based on baseline and follow-up 3D MRA TOF datasets. The quantification of shape changes requires a previous definition of corresponding structures in both datasets. For this, two different rigid registration methods have been developed and evaluated. Besides a state-of-the-art rigid registration method, a second approach integrating vessel segmentations is presented. After registration, the aneurysm recurrence volume can be calculated based on the difference image. The computed volumes were compared to manually extracted volumes. An evaluation based on 20 TOF MRA datasets (baseline and follow-up) of ten patients showed that both registration schemes are generally capable of providing sufficient registration results. Regarding the quantification of aneurysm recurrence volumes, the results suggest that the second segmentation-based registration method yields better results, while a reduction of the computation and interaction time is achieved at the same time. The proposed registration scheme incorporating vessel segmentation enables an improved quantification of recurrence volumes of coiled aneurysms with reduced computation and interaction time. (orig.)

Robust image registration for multiple exposure high dynamic range image synthesis

Science.gov (United States)

Yao, Susu

2011-03-01

Image registration is an important preprocessing technique in high dynamic range (HDR) image synthesis. This paper proposed a robust image registration method for aligning a group of low dynamic range images (LDR) that are captured with different exposure times. Illumination change and photometric distortion between two images would result in inaccurate registration. We propose to transform intensity image data into phase congruency to eliminate the effect of the changes in image brightness and use phase cross correlation in the Fourier transform domain to perform image registration. Considering the presence of non-overlapped regions due to photometric distortion, evolutionary programming is applied to search for the accurate translation parameters so that the accuracy of registration is able to be achieved at a hundredth of a pixel level. The proposed algorithm works well for under and over-exposed image registration. It has been applied to align LDR images for synthesizing high quality HDR images..

A review of biomechanically informed breast image registration

International Nuclear Information System (INIS)

Hipwell, John H; Vavourakis, Vasileios; Mertzanidou, Thomy; Eiben, Björn; Hawkes, David J; Han, Lianghao

2016-01-01

Breast radiology encompasses the full range of imaging modalities from routine imaging via x-ray mammography, magnetic resonance imaging and ultrasound (both two- and three-dimensional), to more recent technologies such as digital breast tomosynthesis, and dedicated breast imaging systems for positron emission mammography and ultrasound tomography. In addition new and experimental modalities, such as Photoacoustics, Near Infrared Spectroscopy and Electrical Impedance Tomography etc, are emerging. The breast is a highly deformable structure however, and this greatly complicates visual comparison of imaging modalities for the purposes of breast screening, cancer diagnosis (including image guided biopsy), tumour staging, treatment monitoring, surgical planning and simulation of the effects of surgery and wound healing etc. Due primarily to the challenges posed by these gross, non-rigid deformations, development of automated methods which enable registration, and hence fusion, of information within and across breast imaging modalities, and between the images and the physical space of the breast during interventions, remains an active research field which has yet to translate suitable methods into clinical practice. This review describes current research in the field of breast biomechanical modelling and identifies relevant publications where the resulting models have been incorporated into breast image registration and simulation algorithms. Despite these developments there remain a number of issues that limit clinical application of biomechanical modelling. These include the accuracy of constitutive modelling, implementation of representative boundary conditions, failure to meet clinically acceptable levels of computational cost, challenges associated with automating patient-specific model generation (i.e. robust image segmentation and mesh generation) and the complexity of applying biomechanical modelling methods in routine clinical practice. (topical review)

Comparison of Rigid and Adaptive Methods of Propagating Gross Tumor Volume Through Respiratory Phases of Four-Dimensional Computed Tomography Image Data Set

International Nuclear Information System (INIS)

Ezhil, Muthuveni; Choi, Bum; Starkschall, George; Bucci, M. Kara; Vedam, Sastry; Balter, Peter

2008-01-01

Purpose: To compare three different methods of propagating the gross tumor volume (GTV) through the respiratory phases that constitute a four-dimensional computed tomography image data set. Methods and Materials: Four-dimensional computed tomography data sets of 20 patients who had undergone definitive hypofractionated radiotherapy to the lung were acquired. The GTV regions of interest (ROIs) were manually delineated on each phase of the four-dimensional computed tomography data set. The ROI from the end-expiration phase was propagated to the remaining nine phases of respiration using the following three techniques: (1) rigid-image registration using in-house software, (2) rigid image registration using research software from a commercial radiotherapy planning system vendor, and (3) rigid-image registration followed by deformable adaptation originally intended for organ-at-risk delineation using the same software. The internal GTVs generated from the various propagation methods were compared with the manual internal GTV using the normalized Dice similarity coefficient (DSC) index. Results: The normalized DSC index of 1.01 ± 0.06 (SD) for rigid propagation using the in-house software program was identical to the normalized DSC index of 1.01 ± 0.06 for rigid propagation achieved with the vendor's research software. Adaptive propagation yielded poorer results, with a normalized DSC index of 0.89 ± 0.10 (paired t test, p <0.001). Conclusion: Propagation of the GTV ROIs through the respiratory phases using rigid- body registration is an acceptable method within a 1-mm margin of uncertainty. The adaptive organ-at-risk propagation method was not applicable to propagating GTV ROIs, resulting in an unacceptable reduction of the volume and distortion of the ROIs

3D full-field quantification of cell-induced large deformations in fibrillar biomaterials by combining non-rigid image registration with label-free second harmonic generation.

Science.gov (United States)

Jorge-Peñas, Alvaro; Bové, Hannelore; Sanen, Kathleen; Vaeyens, Marie-Mo; Steuwe, Christian; Roeffaers, Maarten; Ameloot, Marcel; Van Oosterwyck, Hans

2017-08-01

To advance our current understanding of cell-matrix mechanics and its importance for biomaterials development, advanced three-dimensional (3D) measurement techniques are necessary. Cell-induced deformations of the surrounding matrix are commonly derived from the displacement of embedded fiducial markers, as part of traction force microscopy (TFM) procedures. However, these fluorescent markers may alter the mechanical properties of the matrix or can be taken up by the embedded cells, and therefore influence cellular behavior and fate. In addition, the currently developed methods for calculating cell-induced deformations are generally limited to relatively small deformations, with displacement magnitudes and strains typically of the order of a few microns and less than 10% respectively. Yet, large, complex deformation fields can be expected from cells exerting tractions in fibrillar biomaterials, like collagen. To circumvent these hurdles, we present a technique for the 3D full-field quantification of large cell-generated deformations in collagen, without the need of fiducial markers. We applied non-rigid, Free Form Deformation (FFD)-based image registration to compute full-field displacements induced by MRC-5 human lung fibroblasts in a collagen type I hydrogel by solely relying on second harmonic generation (SHG) from the collagen fibrils. By executing comparative experiments, we show that comparable displacement fields can be derived from both fibrils and fluorescent beads. SHG-based fibril imaging can circumvent all described disadvantages of using fiducial markers. This approach allows measuring 3D full-field deformations under large displacement (of the order of 10 μm) and strain regimes (up to 40%). As such, it holds great promise for the study of large cell-induced deformations as an inherent component of cell-biomaterial interactions and cell-mediated biomaterial remodeling. Copyright © 2017 Elsevier Ltd. All rights reserved.

Cone beam CT imaging with limited angle of projections and prior knowledge for volumetric verification of non-coplanar beam radiation therapy: a proof of concept study

Science.gov (United States)

Meng, Bowen; Xing, Lei; Han, Bin; Koong, Albert; Chang, Daniel; Cheng, Jason; Li, Ruijiang

2013-11-01

Non-coplanar beams are important for treatment of both cranial and noncranial tumors. Treatment verification of such beams with couch rotation/kicks, however, is challenging, particularly for the application of cone beam CT (CBCT). In this situation, only limited and unconventional imaging angles are feasible to avoid collision between the gantry, couch, patient, and on-board imaging system. The purpose of this work is to develop a CBCT verification strategy for patients undergoing non-coplanar radiation therapy. We propose an image reconstruction scheme that integrates a prior image constrained compressed sensing (PICCS) technique with image registration. Planning CT or CBCT acquired at the neutral position is rotated and translated according to the nominal couch rotation/translation to serve as the initial prior image. Here, the nominal couch movement is chosen to have a rotational error of 5° and translational error of 8 mm from the ground truth in one or more axes or directions. The proposed reconstruction scheme alternates between two major steps. First, an image is reconstructed using the PICCS technique implemented with total-variation minimization and simultaneous algebraic reconstruction. Second, the rotational/translational setup errors are corrected and the prior image is updated by applying rigid image registration between the reconstructed image and the previous prior image. The PICCS algorithm and rigid image registration are alternated iteratively until the registration results fall below a predetermined threshold. The proposed reconstruction algorithm is evaluated with an anthropomorphic digital phantom and physical head phantom. The proposed algorithm provides useful volumetric images for patient setup using projections with an angular range as small as 60°. It reduced the translational setup errors from 8 mm to generally <1 mm and the rotational setup errors from 5° to <1°. Compared with the PICCS algorithm alone, the integration of rigid

Integration of breathing in radiotherapy: contribution of the image deformable registration

International Nuclear Information System (INIS)

Boldea, Vlad

2006-01-01

As taking organ movements and deformations into account in radiotherapy for the treatment of lung cancer is a challenge as it allows the delivered dose to be increased while better sparing surrounding sane tissues, this research thesis addresses non-rigid (or deformable) registration iconic methods applied to thorax X ray computed tomography (X-ray CT) 3D acquisitions. The objective is to extract the information regarding lung and tumour movement and deformation. The author thus reports the development of deformable registration framework with several methods of regularisation of vector fields. Three main studies have been performed and are reported. In the first one, deformable registration allowed the breathe blockage reproducibility to be controlled. Experiments performed on ten patients showed that this blockage is efficient (displacement less than 5 mm), except for three of them with functional anomalies. In a second study, 4D X-ray CT acquisitions (3D X-ray CT images acquired at different moments of the normal breathing cycle) have been analysed to extract and follow thorax movements and deformations in order to take them into account in free breathing and to perform 4D dynamic dosimetric studies. A first 4D X-ray CT image model has been developed from 3D X-ray CT images acquired in breathe blockage at the end of expiration and at the end on inhalation [fr

Fast Rotation-Free Feature-Based Image Registration Using Improved N-SIFT and GMM-Based Parallel Optimization.

Science.gov (United States)

Yu, Dongdong; Yang, Feng; Yang, Caiyun; Leng, Chengcai; Cao, Jian; Wang, Yining; Tian, Jie

2016-08-01

Image registration is a key problem in a variety of applications, such as computer vision, medical image processing, pattern recognition, etc., while the application of registration is limited by time consumption and the accuracy in the case of large pose differences. Aimed at these two kinds of problems, we propose a fast rotation-free feature-based rigid registration method based on our proposed accelerated-NSIFT and GMM registration-based parallel optimization (PO-GMMREG). Our method is accelerated by using the GPU/CUDA programming and preserving only the location information without constructing the descriptor of each interest point, while its robustness to missing correspondences and outliers is improved by converting the interest point matching to Gaussian mixture model alignment. The accuracy in the case of large pose differences is settled by our proposed PO-GMMREG algorithm by constructing a set of initial transformations. Experimental results demonstrate that our proposed algorithm can fast rigidly register 3-D medical images and is reliable for aligning 3-D scans even when they exhibit a poor initialization.

Evaluation of registration methods on thoracic CT

DEFF Research Database (Denmark)

Murphy, K.; van Ginneken, B.; Reinhardt, J.

2011-01-01

method and the evaluation is independent, using the same criteria for all participants. All results are published on the EMPIRE10 website (http://empire10.isi.uu.nl). The challenge remains ongoing and open to new participants. Full results from 24 algorithms have been published at the time of writing......EMPIRE10 (Evaluation of Methods for Pulmonary Image REgistration 2010) is a public platform for fair and meaningful comparison of registration algorithms which are applied to a database of intra-patient thoracic CT image pairs. Evaluation of non-rigid registration techniques is a non trivial task....... This article details the organisation of the challenge, the data and evaluation methods and the outcome of the initial launch with 20 algorithms. The gain in knowledge and future work are discussed....

Registration of terrestrial mobile laser data on 2D or 3D geographic database by use of a non-rigid ICP approach.

Science.gov (United States)

Monnier, F.; Vallet, B.; Paparoditis, N.; Papelard, J.-P.; David, N.

2013-10-01

This article presents a generic and efficient method to register terrestrial mobile data with imperfect location on a geographic database with better overall accuracy but less details. The registration method proposed in this paper is based on a semi-rigid point to plane ICP ("Iterative Closest Point"). The main applications of such registration is to improve existing geographic databases, particularly in terms of accuracy, level of detail and diversity of represented objects. Other applications include fine geometric modelling and fine façade texturing, object extraction such as trees, poles, road signs marks, facilities, vehicles, etc. The geopositionning system of mobile mapping systems is affected by GPS masks that are only partially corrected by an Inertial Navigation System (INS) which can cause an important drift. As this drift varies non-linearly, but slowly in time, it will be modelled by a translation defined as a piecewise linear function of time which variation over time will be minimized (rigidity term). For each iteration of the ICP, the drift is estimated in order to minimise the distance between laser points and planar model primitives (data attachment term). The method has been tested on real data (a scan of the city of Paris of 3.6 million laser points registered on a 3D model of approximately 71,400 triangles).

Daily fraction dose recalculation based on rigid registration using Cone Beam CT

Directory of Open Access Journals (Sweden)

Courtney Bosse

2014-03-01

Full Text Available Purpose: To calculate the daily fraction dose for CBCT recalculations based on rigid registration and compare it to the planned CT doses.Methods: For this study, 30 patients that were previously treated (10 SBRT lung, 10 prostate and 10 abdomen were considered. The daily CBCT images were imported into the Pinnacle treatment planning system from Mosaic. Pinnacle was used to re-contour the regions of interest (ROI for the specific CBCT by copying the contours from the original CT plan, planned by the prescribing physician, onto each daily CBCT and then manually reshaping contours to match the ROIs. A new plan is then created with the re-contoured CBCT as primary image in order to calculate the daily dose delivered to each ROI. The DVH values are then exported into Excel and overlaid onto the original CT DVH to produce a graph.Results: For the SBRT lung patients, we found that there were small daily volume changes in the lungs, trachea and esophagus. For almost all regions of interest we found that the dose received each day was less than the predicted dose of the planned CT while the PTV dose was relatively the same each day. The results for the prostate patients were similar, showing slight differences in the DVH values for different days in the rectum and bladder but similar PTV.Conclusion: By comparing daily fraction dose between the re-contoured CBCT images and the original planned CT show that PTV coverage for both prostate and SBRT, it has been shown that for PTV coverage, a planned CT is adequate. However, there are differences between the dose for the organs surrounding the PTV. The dose difference is less than the planned in most instances.-----------------------Cite this article as: Bosse C, Tuohy R, Mavroidis P, Shi Z, Crownover R, Gutierrez A, Papanikolaou N, Stathakis S. Daily fraction dose recalculation based on rigid registration using Cone Beam CT. Int J Cancer Ther Oncol 2014; 2(2:020217. DOI: 10.14319/ijcto.0202.17

Automatic block-matching registration to improve lung tumor localization during image-guided radiotherapy

Science.gov (United States)

Robertson, Scott Patrick

To improve relatively poor outcomes for locally-advanced lung cancer patients, many current efforts are dedicated to minimizing uncertainties in radiotherapy. This enables the isotoxic delivery of escalated tumor doses, leading to better local tumor control. The current dissertation specifically addresses inter-fractional uncertainties resulting from patient setup variability. An automatic block-matching registration (BMR) algorithm is implemented and evaluated for the purpose of directly localizing advanced-stage lung tumors during image-guided radiation therapy. In this algorithm, small image sub-volumes, termed "blocks", are automatically identified on the tumor surface in an initial planning computed tomography (CT) image. Each block is independently and automatically registered to daily images acquired immediately prior to each treatment fraction. To improve the accuracy and robustness of BMR, this algorithm incorporates multi-resolution pyramid registration, regularization with a median filter, and a new multiple-candidate-registrations technique. The result of block-matching is a sparse displacement vector field that models local tissue deformations near the tumor surface. The distribution of displacement vectors is aggregated to obtain the final tumor registration, corresponding to the treatment couch shift for patient setup correction. Compared to existing rigid and deformable registration algorithms, the final BMR algorithm significantly improves the overlap between target volumes from the planning CT and registered daily images. Furthermore, BMR results in the smallest treatment margins for the given study population. However, despite these improvements, large residual target localization errors were noted, indicating that purely rigid couch shifts cannot correct for all sources of inter-fractional variability. Further reductions in treatment uncertainties may require the combination of high-quality target localization and adaptive radiotherapy.

Geometry planning and image registration in magnetic particle imaging using bimodal fiducial markers

International Nuclear Information System (INIS)

Werner, F.; Hofmann, M.; Them, K.; Knopp, T.; Jung, C.; Salamon, J.; Kaul, M. G.; Mummert, T.; Adam, G.; Ittrich, H.; Werner, R.; Säring, D.; Weber, O. M.

2016-01-01

Purpose: Magnetic particle imaging (MPI) is a quantitative imaging modality that allows the distribution of superparamagnetic nanoparticles to be visualized. Compared to other imaging techniques like x-ray radiography, computed tomography (CT), and magnetic resonance imaging (MRI), MPI only provides a signal from the administered tracer, but no additional morphological information, which complicates geometry planning and the interpretation of MP images. The purpose of the authors’ study was to develop bimodal fiducial markers that can be visualized by MPI and MRI in order to create MP–MR fusion images. Methods: A certain arrangement of three bimodal fiducial markers was developed and used in a combined MRI/MPI phantom and also during in vivo experiments in order to investigate its suitability for geometry planning and image fusion. An algorithm for automated marker extraction in both MR and MP images and rigid registration was established. Results: The developed bimodal fiducial markers can be visualized by MRI and MPI and allow for geometry planning as well as automated registration and fusion of MR–MP images. Conclusions: To date, exact positioning of the object to be imaged within the field of view (FOV) and the assignment of reconstructed MPI signals to corresponding morphological regions has been difficult. The developed bimodal fiducial markers and the automated image registration algorithm help to overcome these difficulties.

Geometry planning and image registration in magnetic particle imaging using bimodal fiducial markers

Energy Technology Data Exchange (ETDEWEB)

Werner, F., E-mail: f.werner@uke.de; Hofmann, M.; Them, K.; Knopp, T. [Section for Biomedical Imaging, University Medical Center Hamburg-Eppendorf, Hamburg 20246, Germany and Institute for Biomedical Imaging, Hamburg University of Technology, Hamburg 21073 (Germany); Jung, C.; Salamon, J.; Kaul, M. G.; Mummert, T.; Adam, G.; Ittrich, H. [Department of Diagnostic and Interventional Radiology, University Medical Center Hamburg-Eppendorf, Hamburg 20246 (Germany); Werner, R.; Säring, D. [Institute for Computational Neuroscience, University Medical Center Hamburg-Eppendorf, Hamburg 20246 (Germany); Weber, O. M. [Philips Medical Systems DMC GmbH, Hamburg 22335 (Germany)

2016-06-15

Purpose: Magnetic particle imaging (MPI) is a quantitative imaging modality that allows the distribution of superparamagnetic nanoparticles to be visualized. Compared to other imaging techniques like x-ray radiography, computed tomography (CT), and magnetic resonance imaging (MRI), MPI only provides a signal from the administered tracer, but no additional morphological information, which complicates geometry planning and the interpretation of MP images. The purpose of the authors’ study was to develop bimodal fiducial markers that can be visualized by MPI and MRI in order to create MP–MR fusion images. Methods: A certain arrangement of three bimodal fiducial markers was developed and used in a combined MRI/MPI phantom and also during in vivo experiments in order to investigate its suitability for geometry planning and image fusion. An algorithm for automated marker extraction in both MR and MP images and rigid registration was established. Results: The developed bimodal fiducial markers can be visualized by MRI and MPI and allow for geometry planning as well as automated registration and fusion of MR–MP images. Conclusions: To date, exact positioning of the object to be imaged within the field of view (FOV) and the assignment of reconstructed MPI signals to corresponding morphological regions has been difficult. The developed bimodal fiducial markers and the automated image registration algorithm help to overcome these difficulties.

Template-based CTA to x-ray angio rigid registration of coronary arteries in frequency domain with automatic x-ray segmentation

Energy Technology Data Exchange (ETDEWEB)

Aksoy, Timur; Unal, Gozde [Sabanci University, Tuzla, Istanbul 34956 (Turkey); Demirci, Stefanie; Navab, Nassir [Computer Aided Medical Procedures (CAMP), Technical University of Munich, Garching, 85748 (Germany); Degertekin, Muzaffer [Yeditepe University Hospital, Istanbul 34752 (Turkey)

2013-10-15

Purpose: A key challenge for image guided coronary interventions is accurate and absolutely robust image registration bringing together preinterventional information extracted from a three-dimensional (3D) patient scan and live interventional image information. In this paper, the authors present a novel scheme for 3D to two-dimensional (2D) rigid registration of coronary arteries extracted from preoperative image scan (3D) and a single segmented intraoperative x-ray angio frame in frequency and spatial domains for real-time angiography interventions by C-arm fluoroscopy.Methods: Most existing rigid registration approaches require a close initialization due to the abundance of local minima and high complexity of search algorithms. The authors' method eliminates this requirement by transforming the projections into translation-invariant Fourier domain for estimating the 3D pose. For 3D rotation recovery, template Digitally Reconstructed Radiographs (DRR) as candidate poses of 3D vessels of segmented computed tomography angiography are produced by rotating the camera (image intensifier) around the DICOM angle values with a specific range as in C-arm setup. The authors have compared the 3D poses of template DRRs with the segmented x-ray after equalizing the scales in three domains, namely, Fourier magnitude, Fourier phase, and Fourier polar. The best rotation pose candidate was chosen by one of the highest similarity measures returned by the methods in these domains. It has been noted in literature that frequency domain methods are robust against noise and occlusion which was also validated by the authors' results. 3D translation of the volume was then recovered by distance-map based BFGS optimization well suited to convex structure of the authors' objective function without local minima due to distance maps. A novel automatic x-ray vessel segmentation was also performed in this study.Results: Final results were evaluated in 2D projection space for

Template-based CTA to x-ray angio rigid registration of coronary arteries in frequency domain with automatic x-ray segmentation

International Nuclear Information System (INIS)

Aksoy, Timur; Unal, Gozde; Demirci, Stefanie; Navab, Nassir; Degertekin, Muzaffer

2013-01-01

Purpose: A key challenge for image guided coronary interventions is accurate and absolutely robust image registration bringing together preinterventional information extracted from a three-dimensional (3D) patient scan and live interventional image information. In this paper, the authors present a novel scheme for 3D to two-dimensional (2D) rigid registration of coronary arteries extracted from preoperative image scan (3D) and a single segmented intraoperative x-ray angio frame in frequency and spatial domains for real-time angiography interventions by C-arm fluoroscopy.Methods: Most existing rigid registration approaches require a close initialization due to the abundance of local minima and high complexity of search algorithms. The authors' method eliminates this requirement by transforming the projections into translation-invariant Fourier domain for estimating the 3D pose. For 3D rotation recovery, template Digitally Reconstructed Radiographs (DRR) as candidate poses of 3D vessels of segmented computed tomography angiography are produced by rotating the camera (image intensifier) around the DICOM angle values with a specific range as in C-arm setup. The authors have compared the 3D poses of template DRRs with the segmented x-ray after equalizing the scales in three domains, namely, Fourier magnitude, Fourier phase, and Fourier polar. The best rotation pose candidate was chosen by one of the highest similarity measures returned by the methods in these domains. It has been noted in literature that frequency domain methods are robust against noise and occlusion which was also validated by the authors' results. 3D translation of the volume was then recovered by distance-map based BFGS optimization well suited to convex structure of the authors' objective function without local minima due to distance maps. A novel automatic x-ray vessel segmentation was also performed in this study.Results: Final results were evaluated in 2D projection space for patient data; and

Distortion Correction in Fetal EPI Using Non-Rigid Registration With a Laplacian Constraint.

Science.gov (United States)

Kuklisova-Murgasova, Maria; Lockwood Estrin, Georgia; Nunes, Rita G; Malik, Shaihan J; Rutherford, Mary A; Rueckert, Daniel; Hajnal, Joseph V

2018-01-01

Geometric distortion induced by the main B0 field disrupts the consistency of fetal echo planar imaging (EPI) data, on which diffusion and functional magnetic resonance imaging is based. In this paper, we present a novel data-driven method for simultaneous motion and distortion correction of fetal EPI. A motion-corrected and reconstructed T2 weighted single shot fast spin echo (ssFSE) volume is used as a model of undistorted fetal brain anatomy. Our algorithm interleaves two registration steps: estimation of fetal motion parameters by aligning EPI slices to the model; and deformable registration of EPI slices to slices simulated from the undistorted model to estimate the distortion field. The deformable registration is regularized by a physically inspired Laplacian constraint, to model distortion induced by a source-free background B0 field. Our experiments show that distortion correction significantly improves consistency of reconstructed EPI volumes with ssFSE volumes. In addition, the estimated distortion fields are consistent with fields calculated from acquired field maps, and the Laplacian constraint is essential for estimation of plausible distortion fields. The EPI volumes reconstructed from different scans of the same subject were more consistent when the proposed method was used in comparison with EPI volumes reconstructed from data distortion corrected using a separately acquired B0 field map.

Mass preserving image registration

DEFF Research Database (Denmark)

Gorbunova, Vladlena; Sporring, Jon; Lo, Pechin Chien Pau

2010-01-01

The paper presents results the mass preserving image registration method in the Evaluation of Methods for Pulmonary Image Registration 2010 (EMPIRE10) Challenge. The mass preserving image registration algorithm was applied to the 20 image pairs. Registration was evaluated using four different...

Automated, non-linear registration between 3-dimensional brain map and medical head image

International Nuclear Information System (INIS)

Mizuta, Shinobu; Urayama, Shin-ichi; Zoroofi, R.A.; Uyama, Chikao

1998-01-01

In this paper, we propose an automated, non-linear registration method between 3-dimensional medical head image and brain map in order to efficiently extract the regions of interest. In our method, input 3-dimensional image is registered into a reference image extracted from a brain map. The problems to be solved are automated, non-linear image matching procedure, and cost function which represents the similarity between two images. Non-linear matching is carried out by dividing the input image into connected partial regions, transforming the partial regions preserving connectivity among the adjacent images, evaluating the image similarity between the transformed regions of the input image and the correspondent regions of the reference image, and iteratively searching the optimal transformation of the partial regions. In order to measure the voxelwise similarity of multi-modal images, a cost function is introduced, which is based on the mutual information. Some experiments using MR images presented the effectiveness of the proposed method. (author)

An image acquisition and registration strategy for the fusion of hyperpolarized helium-3 MRI and x-ray CT images of the lung

Science.gov (United States)

Ireland, Rob H.; Woodhouse, Neil; Hoggard, Nigel; Swinscoe, James A.; Foran, Bernadette H.; Hatton, Matthew Q.; Wild, Jim M.

2008-11-01

The purpose of this ethics committee approved prospective study was to evaluate an image acquisition and registration protocol for hyperpolarized helium-3 magnetic resonance imaging (3He-MRI) and x-ray computed tomography. Nine patients with non-small cell lung cancer (NSCLC) gave written informed consent to undergo a free-breathing CT, an inspiration breath-hold CT and a 3D ventilation 3He-MRI in CT position using an elliptical birdcage radiofrequency (RF) body coil. 3He-MRI to CT image fusion was performed using a rigid registration algorithm which was assessed by two observers using anatomical landmarks and a percentage volume overlap coefficient. Registration of 3He-MRI to breath-hold CT was more accurate than to free-breathing CT; overlap 82.9 ± 4.2% versus 59.8 ± 9.0% (p < 0.001) and mean landmark error 0.75 ± 0.24 cm versus 1.25 ± 0.60 cm (p = 0.002). Image registration is significantly improved by using an imaging protocol that enables both 3He-MRI and CT to be acquired with similar breath holds and body position through the use of a birdcage 3He-MRI body RF coil and an inspiration breath-hold CT. Fusion of 3He-MRI to CT may be useful for the assessment of patients with lung diseases.

Automatic registration of imaging mass spectrometry data to the Allen Brain Atlas transcriptome

Science.gov (United States)

Abdelmoula, Walid M.; Carreira, Ricardo J.; Shyti, Reinald; Balluff, Benjamin; Tolner, Else; van den Maagdenberg, Arn M. J. M.; Lelieveldt, B. P. F.; McDonnell, Liam; Dijkstra, Jouke

2014-03-01

Imaging Mass Spectrometry (IMS) is an emerging molecular imaging technology that provides spatially resolved information on biomolecular structures; each image pixel effectively represents a molecular mass spectrum. By combining the histological images and IMS-images, neuroanatomical structures can be distinguished based on their biomolecular features as opposed to morphological features. The combination of IMS data with spatially resolved gene expression maps of the mouse brain, as provided by the Allen Mouse Brain atlas, would enable comparative studies of spatial metabolic and gene expression patterns in life-sciences research and biomarker discovery. As such, it would be highly desirable to spatially register IMS slices to the Allen Brain Atlas (ABA). In this paper, we propose a multi-step automatic registration pipeline to register ABA histology to IMS- images. Key novelty of the method is the selection of the best reference section from the ABA, based on pre-processed histology sections. First, we extracted a hippocampus-specific geometrical feature from the given experimental histological section to initially localize it among the ABA sections. Then, feature-based linear registration is applied to the initially localized section and its two neighbors in the ABA to select the most similar reference section. A non-rigid registration yields a one-to-one mapping of the experimental IMS slice to the ABA. The pipeline was applied on 6 coronal sections from two mouse brains, showing high anatomical correspondence, demonstrating the feasibility of complementing biomolecule distributions from individual mice with the genome-wide ABA transcriptome.

Efficient nonlinear registration of 3D images using high order co-ordinate transfer functions.

Science.gov (United States)

Barber, D C

1999-01-01

There is an increasing interest in image registration for a variety of medical imaging applications. Image registration is achieved through the use of a co-ordinate transfer function (CTF) which maps voxels in one image to voxels in the other image, including in the general case changes in mapped voxel intensity. If images of the same subject are to be registered the co-ordinate transfer function needs to implement a spatial transformation consisting of a displacement and a rigid rotation. In order to achieve registration a common approach is to choose a suitable quality-of-registration measure and devise a method for the efficient generation of the parameters of the CTF which minimize this measure. For registration of images from different subjects more complex transforms are required. In general function minimization is too slow to allow the use of CTFs with more than a small number of parameters. However, provided the images are from the same modality and the CTF can be expanded in terms of an appropriate set of basis functions this paper will show how relatively complex CTFs can be used for registration. The use of increasingly complex CTFs to minimize the within group standard deviation of a set of normal single photon emission tomography brain images is used to demonstrate the improved registration of images from different subjects using CTFs of increasing complexity.

FZUImageReg: A toolbox for medical image registration and dose fusion in cervical cancer radiotherapy.

Directory of Open Access Journals (Sweden)

Qinquan Gao

Full Text Available The combination external-beam radiotherapy and high-dose-rate brachytherapy is a standard form of treatment for patients with locally advanced uterine cervical cancer. Personalized radiotherapy in cervical cancer requires efficient and accurate dose planning and assessment across these types of treatment. To achieve radiation dose assessment, accurate mapping of the dose distribution from HDR-BT onto EBRT is extremely important. However, few systems can achieve robust dose fusion and determine the accumulated dose distribution during the entire course of treatment. We have therefore developed a toolbox (FZUImageReg, which is a user-friendly dose fusion system based on hybrid image registration for radiation dose assessment in cervical cancer radiotherapy. The main part of the software consists of a collection of medical image registration algorithms and a modular design with a user-friendly interface, which allows users to quickly configure, test, monitor, and compare different registration methods for a specific application. Owing to the large deformation, the direct application of conventional state-of-the-art image registration methods is not sufficient for the accurate alignment of EBRT and HDR-BT images. To solve this problem, a multi-phase non-rigid registration method using local landmark-based free-form deformation is proposed for locally large deformation between EBRT and HDR-BT images, followed by intensity-based free-form deformation. With the transformation, the software also provides a dose mapping function according to the deformation field. The total dose distribution during the entire course of treatment can then be presented. Experimental results clearly show that the proposed system can achieve accurate registration between EBRT and HDR-BT images and provide radiation dose warping and fusion results for dose assessment in cervical cancer radiotherapy in terms of high accuracy and efficiency.

Accuracy of deformable image registration on magnetic resonance images in digital and physical phantoms.

Science.gov (United States)

Ger, Rachel B; Yang, Jinzhong; Ding, Yao; Jacobsen, Megan C; Fuller, Clifton D; Howell, Rebecca M; Li, Heng; Jason Stafford, R; Zhou, Shouhao; Court, Laurence E

2017-10-01

Accurate deformable image registration is necessary for longitudinal studies. The error associated with commercial systems has been evaluated using computed tomography (CT). Several in-house algorithms have been evaluated for use with magnetic resonance imaging (MRI), but there is still relatively little information about MRI deformable image registration. This work presents an evaluation of two deformable image registration systems, one commercial (Velocity) and one in-house (demons-based algorithm), with MRI using two different metrics to quantify the registration error. The registration error was analyzed with synthetic MR images. These images were generated from interpatient and intrapatient variation models trained on 28 patients. Four synthetic post-treatment images were generated for each of four synthetic pretreatment images, resulting in 16 image registrations for both the T1- and T2-weighted images. The synthetic post-treatment images were registered to their corresponding synthetic pretreatment image. The registration error was calculated between the known deformation vector field and the generated deformation vector field from the image registration system. The registration error was also analyzed using a porcine phantom with ten implanted 0.35-mm diameter gold markers. The markers were visible on CT but not MRI. CT, T1-weighted MR, and T2-weighted MR images were taken in four different positions. The markers were contoured on the CT images and rigidly registered to their corresponding MR images. The MR images were deformably registered and the distance between the projected marker location and true marker location was measured as the registration error. The synthetic images were evaluated only on Velocity. Root mean square errors (RMSEs) of 0.76 mm in the left-right (LR) direction, 0.76 mm in the anteroposterior (AP) direction, and 0.69 mm in the superior-inferior (SI) direction were observed for the T1-weighted MR images. RMSEs of 1.1 mm in the LR

SU-E-J-08: A Hybrid Three Dimensional Registration Framework for Image-Guided Accurate Radiotherapy System ARTS-IGRT

International Nuclear Information System (INIS)

Wu, Q; Pei, X; Cao, R; Hu, L; Wu, Y

2014-01-01

Purpose: The purpose of this work was to develop a registration framework and method based on the software platform of ARTS-IGRT and implement in C++ based on ITK libraries to register CT images and CBCT images. ARTS-IGRT was a part of our self-developed accurate radiation planning system ARTS. Methods: Mutual information (MI) registration treated each voxel equally. Actually, different voxels even having same intensity should be treated differently in the registration procedure. According to their importance values calculated from self-information, a similarity measure was proposed which combined the spatial importance of a voxel with MI (S-MI). For lung registration, Firstly, a global alignment method was adopted to minimize the margin error and achieve the alignment of these two images on the whole. The result obtained at the low resolution level was then interpolated to become the initial conditions for the higher resolution computation. Secondly, a new similarity measurement S-MI was established to quantify how close the two input image volumes were to each other. Finally, Demons model was applied to compute the deformable map. Results: Registration tools were tested for head-neck and lung images and the average region was 128*128*49. The rigid registration took approximately 2 min and converged 10% faster than traditional MI algorithm, the accuracy reached 1mm for head-neck images. For lung images, the improved symmetric Demons registration process was completed in an average of 5 min using a 2.4GHz dual core CPU. Conclusion: A registration framework was developed to correct patient's setup according to register the planning CT volume data and the daily reconstructed 3D CBCT data. The experiments showed that the spatial MI algorithm can be adopted for head-neck images. The improved Demons deformable registration was more suitable to lung images, and rigid alignment should be applied before deformable registration to get more accurate result. Supported by

An image acquisition and registration strategy for the fusion of hyperpolarized helium-3 MRI and x-ray CT images of the lung

International Nuclear Information System (INIS)

Ireland, Rob H; Woodhouse, Neil; Hoggard, Nigel; Swinscoe, James A; Foran, Bernadette H; Hatton, Matthew Q; Wild, Jim M

2008-01-01

The purpose of this ethics committee approved prospective study was to evaluate an image acquisition and registration protocol for hyperpolarized helium-3 magnetic resonance imaging ( 3 He-MRI) and x-ray computed tomography. Nine patients with non-small cell lung cancer (NSCLC) gave written informed consent to undergo a free-breathing CT, an inspiration breath-hold CT and a 3D ventilation 3 He-MRI in CT position using an elliptical birdcage radiofrequency (RF) body coil. 3 He-MRI to CT image fusion was performed using a rigid registration algorithm which was assessed by two observers using anatomical landmarks and a percentage volume overlap coefficient. Registration of 3 He-MRI to breath-hold CT was more accurate than to free-breathing CT; overlap 82.9 ± 4.2% versus 59.8 ± 9.0% (p 3 He-MRI and CT to be acquired with similar breath holds and body position through the use of a birdcage 3 He-MRI body RF coil and an inspiration breath-hold CT. Fusion of 3 He-MRI to CT may be useful for the assessment of patients with lung diseases.

2D-3D rigid registration to compensate for prostate motion during 3D TRUS-guided biopsy.

Science.gov (United States)

De Silva, Tharindu; Fenster, Aaron; Cool, Derek W; Gardi, Lori; Romagnoli, Cesare; Samarabandu, Jagath; Ward, Aaron D

2013-02-01

Three-dimensional (3D) transrectal ultrasound (TRUS)-guided systems have been developed to improve targeting accuracy during prostate biopsy. However, prostate motion during the procedure is a potential source of error that can cause target misalignments. The authors present an image-based registration technique to compensate for prostate motion by registering the live two-dimensional (2D) TRUS images acquired during the biopsy procedure to a preacquired 3D TRUS image. The registration must be performed both accurately and quickly in order to be useful during the clinical procedure. The authors implemented an intensity-based 2D-3D rigid registration algorithm optimizing the normalized cross-correlation (NCC) metric using Powell's method. The 2D TRUS images acquired during the procedure prior to biopsy gun firing were registered to the baseline 3D TRUS image acquired at the beginning of the procedure. The accuracy was measured by calculating the target registration error (TRE) using manually identified fiducials within the prostate; these fiducials were used for validation only and were not provided as inputs to the registration algorithm. They also evaluated the accuracy when the registrations were performed continuously throughout the biopsy by acquiring and registering live 2D TRUS images every second. This measured the improvement in accuracy resulting from performing the registration, continuously compensating for motion during the procedure. To further validate the method using a more challenging data set, registrations were performed using 3D TRUS images acquired by intentionally exerting different levels of ultrasound probe pressures in order to measure the performance of our algorithm when the prostate tissue was intentionally deformed. In this data set, biopsy scenarios were simulated by extracting 2D frames from the 3D TRUS images and registering them to the baseline 3D image. A graphics processing unit (GPU)-based implementation was used to improve the

Dimensional Metrology of Non-rigid Parts Without Specialized Inspection Fixtures =

Science.gov (United States)

Sabri, Vahid

Quality control is an important factor for manufacturing companies looking to prosper in an era of globalization, market pressures and technological advances. Functionality and product quality cannot be guaranteed without this important aspect. Manufactured parts have deviations from their nominal (CAD) shape caused by the manufacturing process. Thus, geometric inspection is a very important element in the quality control of mechanical parts. We will focus here on the geometric inspection of non-rigid (flexible) parts which are widely used in the aeronautic and automotive industries. Non-rigid parts can have different forms in a free-state condition compared with their nominal models due to residual stress and gravity loads. To solve this problem, dedicated inspection fixtures are generally used in industry to compensate for the displacement of such parts for simulating the use state in order to perform geometric inspections. These fixtures and the installation and inspection processes are expensive and time-consuming. Our aim in this thesis is therefore to develop an inspection method which eliminates the need for specialized fixtures. This is done by acquiring a point cloud from the part in a free-state condition using a contactless measuring device such as optical scanning and comparing it with the CAD model for the deviation identification. Using a non-rigid registration method and finite element analysis, we numerically inspect the profile of a non-rigid part. To do so, a simulated displacement is performed using an improved definition of displacement boundary conditions for simulating unfixed parts. In addition, we propose a numerical method for dimensional metrology of non-rigid parts in a free-state condition based on the arc length measurement by calculating the geodesic distance using the Fast Marching Method (FMM). In this thesis, we apply our developed methods on industrial non-rigid parts with free-form surfaces simulated with different types of

A method for dynamic subtraction MR imaging of the liver

Directory of Open Access Journals (Sweden)

Setti Ernesto

2006-06-01

Full Text Available Abstract Background Subtraction of Dynamic Contrast-Enhanced 3D Magnetic Resonance (DCE-MR volumes can result in images that depict and accurately characterize a variety of liver lesions. However, the diagnostic utility of subtraction images depends on the extent of co-registration between non-enhanced and enhanced volumes. Movement of liver structures during acquisition must be corrected prior to subtraction. Currently available methods are computer intensive. We report a new method for the dynamic subtraction of MR liver images that does not require excessive computer time. Methods Nineteen consecutive patients (median age 45 years; range 37–67 were evaluated by VIBE T1-weighted sequences (TR 5.2 ms, TE 2.6 ms, flip angle 20°, slice thickness 1.5 mm acquired before and 45s after contrast injection. Acquisition parameters were optimized for best portal system enhancement. Pre and post-contrast liver volumes were realigned using our 3D registration method which combines: (a rigid 3D translation using maximization of normalized mutual information (NMI, and (b fast 2D non-rigid registration which employs a complex discrete wavelet transform algorithm to maximize pixel phase correlation and perform multiresolution analysis. Registration performance was assessed quantitatively by NMI. Results The new registration procedure was able to realign liver structures in all 19 patients. NMI increased by about 8% after rigid registration (native vs. rigid registration 0.073 ± 0.031 vs. 0.078 ± 0.031, n.s., paired t-test and by a further 23% (0.096 ± 0.035 vs. 0.078 ± 0.031, p t-test after non-rigid realignment. The overall average NMI increase was 31%. Conclusion This new method for realigning dynamic contrast-enhanced 3D MR volumes of liver leads to subtraction images that enhance diagnostic possibilities for liver lesions.

Interactive initialization of 2D/3D rigid registration

Energy Technology Data Exchange (ETDEWEB)

Gong, Ren Hui; Güler, Özgür [The Sheikh Zayed Institute for Pediatric Surgical Innovation, Children' s National Medical Center, Washington, DC 20010 (United States); Kürklüoglu, Mustafa [Department of Cardiac Surgery, Children' s National Medical Center, Washington, DC 20010 (United States); Lovejoy, John [Department of Orthopaedic Surgery and Sports Medicine, Children' s National Medical Center, Washington, DC 20010 (United States); Yaniv, Ziv, E-mail: ZYaniv@childrensnational.org [The Sheikh Zayed Institute for Pediatric Surgical Innovation, Children' s National Medical Center, Washington, DC 20010 and Departments of Pediatrics and Radiology, George Washington University, Washington, DC 20037 (United States)

2013-12-15

Purpose: Registration is one of the key technical components in an image-guided navigation system. A large number of 2D/3D registration algorithms have been previously proposed, but have not been able to transition into clinical practice. The authors identify the primary reason for the lack of adoption with the prerequisite for a sufficiently accurate initial transformation, mean target registration error of about 10 mm or less. In this paper, the authors present two interactive initialization approaches that provide the desired accuracy for x-ray/MR and x-ray/CT registration in the operating room setting. Methods: The authors have developed two interactive registration methods based on visual alignment of a preoperative image, MR, or CT to intraoperative x-rays. In the first approach, the operator uses a gesture based interface to align a volume rendering of the preoperative image to multiple x-rays. The second approach uses a tracked tool available as part of a navigation system. Preoperatively, a virtual replica of the tool is positioned next to the anatomical structures visible in the volumetric data. Intraoperatively, the physical tool is positioned in a similar manner and subsequently used to align a volume rendering to the x-ray images using an augmented reality (AR) approach. Both methods were assessed using three publicly available reference data sets for 2D/3D registration evaluation. Results: In the authors' experiments, the authors show that for x-ray/MR registration, the gesture based method resulted in a mean target registration error (mTRE) of 9.3 ± 5.0 mm with an average interaction time of 146.3 ± 73.0 s, and the AR-based method had mTREs of 7.2 ± 3.2 mm with interaction times of 44 ± 32 s. For x-ray/CT registration, the gesture based method resulted in a mTRE of 7.4 ± 5.0 mm with an average interaction time of 132.1 ± 66.4 s, and the AR-based method had mTREs of 8.3 ± 5.0 mm with interaction times of 58 ± 52 s. Conclusions: Based on

Interactive initialization of 2D/3D rigid registration

International Nuclear Information System (INIS)

Gong, Ren Hui; Güler, Özgür; Kürklüoglu, Mustafa; Lovejoy, John; Yaniv, Ziv

2013-01-01

Purpose: Registration is one of the key technical components in an image-guided navigation system. A large number of 2D/3D registration algorithms have been previously proposed, but have not been able to transition into clinical practice. The authors identify the primary reason for the lack of adoption with the prerequisite for a sufficiently accurate initial transformation, mean target registration error of about 10 mm or less. In this paper, the authors present two interactive initialization approaches that provide the desired accuracy for x-ray/MR and x-ray/CT registration in the operating room setting. Methods: The authors have developed two interactive registration methods based on visual alignment of a preoperative image, MR, or CT to intraoperative x-rays. In the first approach, the operator uses a gesture based interface to align a volume rendering of the preoperative image to multiple x-rays. The second approach uses a tracked tool available as part of a navigation system. Preoperatively, a virtual replica of the tool is positioned next to the anatomical structures visible in the volumetric data. Intraoperatively, the physical tool is positioned in a similar manner and subsequently used to align a volume rendering to the x-ray images using an augmented reality (AR) approach. Both methods were assessed using three publicly available reference data sets for 2D/3D registration evaluation. Results: In the authors' experiments, the authors show that for x-ray/MR registration, the gesture based method resulted in a mean target registration error (mTRE) of 9.3 ± 5.0 mm with an average interaction time of 146.3 ± 73.0 s, and the AR-based method had mTREs of 7.2 ± 3.2 mm with interaction times of 44 ± 32 s. For x-ray/CT registration, the gesture based method resulted in a mTRE of 7.4 ± 5.0 mm with an average interaction time of 132.1 ± 66.4 s, and the AR-based method had mTREs of 8.3 ± 5.0 mm with interaction times of 58 ± 52 s. Conclusions: Based on the

Mammogram CAD, hybrid registration and iconic analysis

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Boucher, A.; Cloppet, F.; Vincent, N.

2013-03-01

This paper aims to develop a computer aided diagnosis (CAD) based on a two-step methodology to register and analyze pairs of temporal mammograms. The concept of "medical file", including all the previous medical information on a patient, enables joint analysis of different acquisitions taken at different times, and the detection of significant modifications. The developed registration method aims to superimpose at best the different anatomical structures of the breast. The registration is designed in order to get rid of deformation undergone by the acquisition process while preserving those due to breast changes indicative of malignancy. In order to reach this goal, a referent image is computed from control points based on anatomical features that are extracted automatically. Then the second image of the couple is realigned on the referent image, using a coarse-to-fine approach according to expert knowledge that allows both rigid and non-rigid transforms. The joint analysis detects the evolution between two images representing the same scene. In order to achieve this, it is important to know the registration error limits in order to adapt the observation scale. The approach used in this paper is based on an image sparse representation. Decomposed in regular patterns, the images are analyzed under a new angle. The evolution detection problem has many practical applications, especially in medical images. The CAD is evaluated using recall and precision of differences in mammograms.

Simultaneous 3D–2D image registration and C-arm calibration: Application to endovascular image-guided interventions

Energy Technology Data Exchange (ETDEWEB)

Mitrović, Uroš [Faculty of Electrical Engineering, University of Ljubljana, Tržaška 25, Ljubljana 1000, Slovenia and Cosylab, Control System Laboratory, Teslova ulica 30, Ljubljana 1000 (Slovenia); Pernuš, Franjo [Faculty of Electrical Engineering, University of Ljubljana, Tržaška 25, Ljubljana 1000 (Slovenia); Likar, Boštjan; Špiclin, Žiga, E-mail: ziga.spiclin@fe.uni-lj.si [Faculty of Electrical Engineering, University of Ljubljana, Tržaška 25, Ljubljana 1000, Slovenia and Sensum, Computer Vision Systems, Tehnološki Park 21, Ljubljana 1000 (Slovenia)

2015-11-15

Purpose: Three-dimensional to two-dimensional (3D–2D) image registration is a key to fusion and simultaneous visualization of valuable information contained in 3D pre-interventional and 2D intra-interventional images with the final goal of image guidance of a procedure. In this paper, the authors focus on 3D–2D image registration within the context of intracranial endovascular image-guided interventions (EIGIs), where the 3D and 2D images are generally acquired with the same C-arm system. The accuracy and robustness of any 3D–2D registration method, to be used in a clinical setting, is influenced by (1) the method itself, (2) uncertainty of initial pose of the 3D image from which registration starts, (3) uncertainty of C-arm’s geometry and pose, and (4) the number of 2D intra-interventional images used for registration, which is generally one and at most two. The study of these influences requires rigorous and objective validation of any 3D–2D registration method against a highly accurate reference or “gold standard” registration, performed on clinical image datasets acquired in the context of the intervention. Methods: The registration process is split into two sequential, i.e., initial and final, registration stages. The initial stage is either machine-based or template matching. The latter aims to reduce possibly large in-plane translation errors by matching a projection of the 3D vessel model and 2D image. In the final registration stage, four state-of-the-art intrinsic image-based 3D–2D registration methods, which involve simultaneous refinement of rigid-body and C-arm parameters, are evaluated. For objective validation, the authors acquired an image database of 15 patients undergoing cerebral EIGI, for which accurate gold standard registrations were established by fiducial marker coregistration. Results: Based on target registration error, the obtained success rates of 3D to a single 2D image registration after initial machine-based and

Evaluation of registration strategies for multi-modality images of rat brain slices

International Nuclear Information System (INIS)

Palm, Christoph; Vieten, Andrea; Salber, Dagmar; Pietrzyk, Uwe

2009-01-01

In neuroscience, small-animal studies frequently involve dealing with series of images from multiple modalities such as histology and autoradiography. The consistent and bias-free restacking of multi-modality image series is obligatory as a starting point for subsequent non-rigid registration procedures and for quantitative comparisons with positron emission tomography (PET) and other in vivo data. Up to now, consistency between 2D slices without cross validation using an inherent 3D modality is frequently presumed to be close to the true morphology due to the smooth appearance of the contours of anatomical structures. However, in multi-modality stacks consistency is difficult to assess. In this work, consistency is defined in terms of smoothness of neighboring slices within a single modality and between different modalities. Registration bias denotes the distortion of the registered stack in comparison to the true 3D morphology and shape. Based on these metrics, different restacking strategies of multi-modality rat brain slices are experimentally evaluated. Experiments based on MRI-simulated and real dual-tracer autoradiograms reveal a clear bias of the restacked volume despite quantitatively high consistency and qualitatively smooth brain structures. However, different registration strategies yield different inter-consistency metrics. If no genuine 3D modality is available, the use of the so-called SOP (slice-order preferred) or MOSOP (modality-and-slice-order preferred) strategy is recommended.

Automated Registration of Multimodal Optic Disc Images: Clinical Assessment of Alignment Accuracy.

Science.gov (United States)

Ng, Wai Siene; Legg, Phil; Avadhanam, Venkat; Aye, Kyaw; Evans, Steffan H P; North, Rachel V; Marshall, Andrew D; Rosin, Paul; Morgan, James E

2016-04-01

To determine the accuracy of automated alignment algorithms for the registration of optic disc images obtained by 2 different modalities: fundus photography and scanning laser tomography. Images obtained with the Heidelberg Retina Tomograph II and paired photographic optic disc images of 135 eyes were analyzed. Three state-of-the-art automated registration techniques Regional Mutual Information, rigid Feature Neighbourhood Mutual Information (FNMI), and nonrigid FNMI (NRFNMI) were used to align these image pairs. Alignment of each composite picture was assessed on a 5-point grading scale: "Fail" (no alignment of vessels with no vessel contact), "Weak" (vessels have slight contact), "Good" (vessels with 50% contact), and "Excellent" (complete alignment). Custom software generated an image mosaic in which the modalities were interleaved as a series of alternate 5×5-pixel blocks. These were graded independently by 3 clinically experienced observers. A total of 810 image pairs were assessed. All 3 registration techniques achieved a score of "Good" or better in >95% of the image sets. NRFNMI had the highest percentage of "Excellent" (mean: 99.6%; range, 95.2% to 99.6%), followed by Regional Mutual Information (mean: 81.6%; range, 86.3% to 78.5%) and FNMI (mean: 73.1%; range, 85.2% to 54.4%). Automated registration of optic disc images by different modalities is a feasible option for clinical application. All 3 methods provided useful levels of alignment, but the NRFNMI technique consistently outperformed the others and is recommended as a practical approach to the automated registration of multimodal disc images.

Understanding geological processes: Visualization of rigid and non-rigid transformations

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Shipley, T. F.; Atit, K.; Manduca, C. A.; Ormand, C. J.; Resnick, I.; Tikoff, B.

2012-12-01

Visualizations are used in the geological sciences to support reasoning about structures and events. Research in cognitive sciences offers insights into the range of skills of different users, and ultimately how visualizations might support different users. To understand the range of skills needed to reason about earth processes we have developed a program of research that is grounded in the geosciences' careful description of the spatial and spatiotemporal patterns associated with earth processes. In particular, we are pursuing a research program that identifies specific spatial skills and investigates whether and how they are related to each other. For this study, we focus on a specific question: Is there an important distinction in the geosciences between rigid and non-rigid deformation? To study a general spatial thinking skill we employed displays with non-geological objects that had been altered by rigid change (rotation), and two types of non-rigid change ("brittle" (or discontinuous) and "ductile" (or continuous) deformation). Disciplinary scientists (geosciences and chemistry faculty), and novices (non-science faculty and undergraduate psychology students) answered questions that required them to visualize the appearance of the object before the change. In one study, geologists and chemists were found to be superior to non-science faculty in reasoning about rigid rotations (e.g., what an object would look like from a different perspective). Geologists were superior to chemists in reasoning about brittle deformations (e.g., what an object looked like before it was broken - here the object was a word cut into many fragments displaced in different directions). This finding is consistent with two hypotheses: 1) Experts are good at visualizing the types of changes required for their domain; and 2) Visualization of rigid and non-rigid changes are not the same skill. An additional important finding is that there was a broad range of skill in both rigid and non-rigid

Planning, guidance, and quality assurance of pelvic screw placement using deformable image registration

Science.gov (United States)

Goerres, J.; Uneri, A.; Jacobson, M.; Ramsay, B.; De Silva, T.; Ketcha, M.; Han, R.; Manbachi, A.; Vogt, S.; Kleinszig, G.; Wolinsky, J.-P.; Osgood, G.; Siewerdsen, J. H.

2017-12-01

Percutaneous pelvic screw placement is challenging due to narrow bone corridors surrounded by vulnerable structures and difficult visual interpretation of complex anatomical shapes in 2D x-ray projection images. To address these challenges, a system for planning, guidance, and quality assurance (QA) is presented, providing functionality analogous to surgical navigation, but based on robust 3D-2D image registration techniques using fluoroscopy images already acquired in routine workflow. Two novel aspects of the system are investigated: automatic planning of pelvic screw trajectories and the ability to account for deformation of surgical devices (K-wire deflection). Atlas-based registration is used to calculate a patient-specific plan of screw trajectories in preoperative CT. 3D-2D registration aligns the patient to CT within the projective geometry of intraoperative fluoroscopy. Deformable known-component registration (dKC-Reg) localizes the surgical device, and the combination of plan and device location is used to provide guidance and QA. A leave-one-out analysis evaluated the accuracy of automatic planning, and a cadaver experiment compared the accuracy of dKC-Reg to rigid approaches (e.g. optical tracking). Surgical plans conformed within the bone cortex by 3-4 mm for the narrowest corridor (superior pubic ramus) and  >5 mm for the widest corridor (tear drop). The dKC-Reg algorithm localized the K-wire tip within 1.1 mm and 1.4° and was consistently more accurate than rigid-body tracking (errors up to 9 mm). The system was shown to automatically compute reliable screw trajectories and accurately localize deformed surgical devices (K-wires). Such capability could improve guidance and QA in orthopaedic surgery, where workflow is impeded by manual planning, conventional tool trackers add complexity and cost, rigid tool assumptions are often inaccurate, and qualitative interpretation of complex anatomy from 2D projections is prone to trial

Image registration with uncertainty analysis

Science.gov (United States)

Simonson, Katherine M [Cedar Crest, NM

2011-03-22

In an image registration method, edges are detected in a first image and a second image. A percentage of edge pixels in a subset of the second image that are also edges in the first image shifted by a translation is calculated. A best registration point is calculated based on a maximum percentage of edges matched. In a predefined search region, all registration points other than the best registration point are identified that are not significantly worse than the best registration point according to a predetermined statistical criterion.

A fast, accurate, and automatic 2D-3D image registration for image-guided cranial radiosurgery

International Nuclear Information System (INIS)

Fu Dongshan; Kuduvalli, Gopinath

2008-01-01

The authors developed a fast and accurate two-dimensional (2D)-three-dimensional (3D) image registration method to perform precise initial patient setup and frequent detection and correction for patient movement during image-guided cranial radiosurgery treatment. In this method, an approximate geometric relationship is first established to decompose a 3D rigid transformation in the 3D patient coordinate into in-plane transformations and out-of-plane rotations in two orthogonal 2D projections. Digitally reconstructed radiographs are generated offline from a preoperative computed tomography volume prior to treatment and used as the reference for patient position. A multiphase framework is designed to register the digitally reconstructed radiographs with the x-ray images periodically acquired during patient setup and treatment. The registration in each projection is performed independently; the results in the two projections are then combined and converted to a 3D rigid transformation by 2D-3D geometric backprojection. The in-plane transformation and the out-of-plane rotation are estimated using different search methods, including multiresolution matching, steepest descent minimization, and one-dimensional search. Two similarity measures, optimized pattern intensity and sum of squared difference, are applied at different registration phases to optimize accuracy and computation speed. Various experiments on an anthropomorphic head-and-neck phantom showed that, using fiducial registration as a gold standard, the registration errors were 0.33±0.16 mm (s.d.) in overall translation and 0.29 deg. ±0.11 deg. (s.d.) in overall rotation. The total targeting errors were 0.34±0.16 mm (s.d.), 0.40±0.2 mm (s.d.), and 0.51±0.26 mm (s.d.) for the targets at the distances of 2, 6, and 10 cm from the rotation center, respectively. The computation time was less than 3 s on a computer with an Intel Pentium 3.0 GHz dual processor

Image Registration: A Necessary Evil

Science.gov (United States)

Bell, James; McLachlan, Blair; Hermstad, Dexter; Trosin, Jeff; George, Michael W. (Technical Monitor)

1995-01-01

Registration of test and reference images is a key component of nearly all PSP data reduction techniques. This is done to ensure that a test image pixel viewing a particular point on the model is ratioed by the reference image pixel which views the same point. Typically registration is needed to account for model motion due to differing airloads when the wind-off and wind-on images are taken. Registration is also necessary when two cameras are used for simultaneous acquisition of data from a dual-frequency paint. This presentation will discuss the advantages and disadvantages of several different image registration techniques. In order to do so, it is necessary to propose both an accuracy requirement for image registration and a means for measuring the accuracy of a particular technique. High contrast regions in the unregistered images are most sensitive to registration errors, and it is proposed that these regions be used to establish the error limits for registration. Once this is done, the actual registration error can be determined by locating corresponding points on the test and reference images, and determining how well a particular registration technique matches them. An example of this procedure is shown for three transforms used to register images of a semispan model. Thirty control points were located on the model. A subset of the points were used to determine the coefficients of each registration transform, and the error with which each transform aligned the remaining points was determined. The results indicate the general superiority of a third-order polynomial over other candidate transforms, as well as showing how registration accuracy varies with number of control points. Finally, it is proposed that image registration may eventually be done away with completely. As more accurate image resection techniques and more detailed model surface grids become available, it will be possible to map raw image data onto the model surface accurately. Intensity

Tensor-based morphometry with stationary velocity field diffeomorphic registration: application to ADNI.

Science.gov (United States)

Bossa, Matias; Zacur, Ernesto; Olmos, Salvador

2010-07-01

Tensor-based morphometry (TBM) is an analysis technique where anatomical information is characterized by means of the spatial transformations mapping a customized template with the observed images. Therefore, accurate inter-subject non-rigid registration is an essential prerequisite for both template estimation and image warping. Subsequent statistical analysis on the spatial transformations is performed to highlight voxel-wise differences. Most of previous TBM studies did not explore the influence of the registration parameters, such as the parameters defining the deformation and the regularization models. In this work performance evaluation of TBM using stationary velocity field (SVF) diffeomorphic registration was performed in a subset of subjects from Alzheimer's Disease Neuroimaging Initiative (ADNI) study. A wide range of values of the registration parameters that define the transformation smoothness and the balance between image matching and regularization were explored in the evaluation. The proposed methodology provided brain atrophy maps with very detailed anatomical resolution and with a high significance level compared with results recently published on the same data set using a non-linear elastic registration method. Copyright (c) 2010 Elsevier Inc. All rights reserved.

TU-B-19A-01: Image Registration II: TG132-Quality Assurance for Image Registration

International Nuclear Information System (INIS)

Brock, K; Mutic, S

2014-01-01

AAPM Task Group 132 was charged with a review of the current approaches and solutions for image registration in radiotherapy and to provide recommendations for quality assurance and quality control of these clinical processes. As the results of image registration are always used as the input of another process for planning or delivery, it is important for the user to understand and document the uncertainty associate with the algorithm in general and the Result of a specific registration. The recommendations of this task group, which at the time of abstract submission are currently being reviewed by the AAPM, include the following components. The user should understand the basic image registration techniques and methods of visualizing image fusion. The disclosure of basic components of the image registration by commercial vendors is critical in this respect. The physicists should perform end-to-end tests of imaging, registration, and planning/treatment systems if image registration is performed on a stand-alone system. A comprehensive commissioning process should be performed and documented by the physicist prior to clinical use of the system. As documentation is important to the safe implementation of this process, a request and report system should be integrated into the clinical workflow. Finally, a patient specific QA practice should be established for efficient evaluation of image registration results. The implementation of these recommendations will be described and illustrated during this educational session. Learning Objectives: Highlight the importance of understanding the image registration techniques used in their clinic. Describe the end-to-end tests needed for stand-alone registration systems. Illustrate a comprehensive commissioning program using both phantom data and clinical images. Describe a request and report system to ensure communication and documentation. Demonstrate an clinically-efficient patient QA practice for efficient evaluation of image

The ANACONDA algorithm for deformable image registration in radiotherapy

International Nuclear Information System (INIS)

Weistrand, Ola; Svensson, Stina

2015-01-01

Purpose: The purpose of this work was to describe a versatile algorithm for deformable image registration with applications in radiotherapy and to validate it on thoracic 4DCT data as well as CT/cone beam CT (CBCT) data. Methods: ANAtomically CONstrained Deformation Algorithm (ANACONDA) combines image information (i.e., intensities) with anatomical information as provided by contoured image sets. The registration problem is formulated as a nonlinear optimization problem and solved with an in-house developed solver, tailored to this problem. The objective function, which is minimized during optimization, is a linear combination of four nonlinear terms: 1. image similarity term; 2. grid regularization term, which aims at keeping the deformed image grid smooth and invertible; 3. a shape based regularization term which works to keep the deformation anatomically reasonable when regions of interest are present in the reference image; and 4. a penalty term which is added to the optimization problem when controlling structures are used, aimed at deforming the selected structure in the reference image to the corresponding structure in the target image. Results: To validate ANACONDA, the authors have used 16 publically available thoracic 4DCT data sets for which target registration errors from several algorithms have been reported in the literature. On average for the 16 data sets, the target registration error is 1.17 ± 0.87 mm, Dice similarity coefficient is 0.98 for the two lungs, and image similarity, measured by the correlation coefficient, is 0.95. The authors have also validated ANACONDA using two pelvic cases and one head and neck case with planning CT and daily acquired CBCT. Each image has been contoured by a physician (radiation oncologist) or experienced radiation therapist. The results are an improvement with respect to rigid registration. However, for the head and neck case, the sample set is too small to show statistical significance. Conclusions: ANACONDA

Improving fluid registration through white matter segmentation in a twin study design

Science.gov (United States)

Chou, Yi-Yu; Lepore, Natasha; Brun, Caroline; Barysheva, Marina; McMahon, Katie; de Zubicaray, Greig I.; Wright, Margaret J.; Toga, Arthur W.; Thompson, Paul M.

2010-03-01

Robust and automatic non-rigid registration depends on many parameters that have not yet been systematically explored. Here we determined how tissue classification influences non-linear fluid registration of brain MRI. Twin data is ideal for studying this question, as volumetric correlations between corresponding brain regions that are under genetic control should be higher in monozygotic twins (MZ) who share 100% of their genes when compared to dizygotic twins (DZ) who share half their genes on average. When these substructure volumes are quantified using tensor-based morphometry, improved registration can be defined based on which method gives higher MZ twin correlations when compared to DZs, as registration errors tend to deplete these correlations. In a study of 92 subjects, higher effect sizes were found in cumulative distribution functions derived from statistical maps when performing tissue classification before fluid registration, versus fluidly registering the raw images. This gives empirical evidence in favor of pre-segmenting images for tensor-based morphometry.

Combining variational and model-based techniques to register PET and MR images in hand osteoarthritis

International Nuclear Information System (INIS)

Magee, Derek; Tanner, Steven F; Jeavons, Alan P; Waller, Michael; Tan, Ai Lyn; McGonagle, Dennis

2010-01-01

Co-registration of clinical images acquired using different imaging modalities and equipment is finding increasing use in patient studies. Here we present a method for registering high-resolution positron emission tomography (PET) data of the hand acquired using high-density avalanche chambers with magnetic resonance (MR) images of the finger obtained using a 'microscopy coil'. This allows the identification of the anatomical location of the PET radiotracer and thereby locates areas of active bone metabolism/'turnover'. Image fusion involving data acquired from the hand is demanding because rigid-body transformations cannot be employed to accurately register the images. The non-rigid registration technique that has been implemented in this study uses a variational approach to maximize the mutual information between images acquired using these different imaging modalities. A piecewise model of the fingers is employed to ensure that the methodology is robust and that it generates an accurate registration. Evaluation of the accuracy of the technique is tested using both synthetic data and PET and MR images acquired from patients with osteoarthritis. The method outperforms some established non-rigid registration techniques and results in a mean registration error that is less than approximately 1.5 mm in the vicinity of the finger joints.

Feasibility of a novel deformable image registration technique to facilitate classification, targeting, and monitoring of tumor and normal tissue

International Nuclear Information System (INIS)

Brock, Kristy K.; Dawson, Laura A.; Sharpe, Michael B.; Moseley, Douglas J.; Jaffray, David A.

2006-01-01

Purpose: To investigate the feasibility of a biomechanical-based deformable image registration technique for the integration of multimodality imaging, image guided treatment, and response monitoring. Methods and Materials: A multiorgan deformable image registration technique based on finite element modeling (FEM) and surface projection alignment of selected regions of interest with biomechanical material and interface models has been developed. FEM also provides an inherent method for direct tracking specified regions through treatment and follow-up. Results: The technique was demonstrated on 5 liver cancer patients. Differences of up to 1 cm of motion were seen between the diaphragm and the tumor center of mass after deformable image registration of exhale and inhale CT scans. Spatial differences of 5 mm or more were observed for up to 86% of the surface of the defined tumor after deformable image registration of the computed tomography (CT) and magnetic resonance images. Up to 6.8 mm of motion was observed for the tumor after deformable image registration of the CT and cone-beam CT scan after rigid registration of the liver. Deformable registration of the CT to the follow-up CT allowed a more accurate assessment of tumor response. Conclusions: This biomechanical-based deformable image registration technique incorporates classification, targeting, and monitoring of tumor and normal tissue using one methodology

SU-C-18A-04: 3D Markerless Registration of Lung Based On Coherent Point Drift: Application in Image Guided Radiotherapy

International Nuclear Information System (INIS)

Nasehi Tehrani, J; Wang, J; Guo, X; Yang, Y

2014-01-01

Purpose: This study evaluated a new probabilistic non-rigid registration method called coherent point drift for real time 3D markerless registration of the lung motion during radiotherapy. Method: 4DCT image datasets Dir-lab (www.dir-lab.com) have been used for creating 3D boundary element model of the lungs. For the first step, the 3D surface of the lungs in respiration phases T0 and T50 were segmented and divided into a finite number of linear triangular elements. Each triangle is a two dimensional object which has three vertices (each vertex has three degree of freedom). One of the main features of the lungs motion is velocity coherence so the vertices that creating the mesh of the lungs should also have features and degree of freedom of lung structure. This means that the vertices close to each other tend to move coherently. In the next step, we implemented a probabilistic non-rigid registration method called coherent point drift to calculate nonlinear displacement of vertices between different expiratory phases. Results: The method has been applied to images of 10-patients in Dir-lab dataset. The normal distribution of vertices to the origin for each expiratory stage were calculated. The results shows that the maximum error of registration between different expiratory phases is less than 0.4 mm (0.38 SI, 0.33 mm AP, 0.29 mm RL direction). This method is a reliable method for calculating the vector of displacement, and the degrees of freedom (DOFs) of lung structure in radiotherapy. Conclusions: We evaluated a new 3D registration method for distribution set of vertices inside lungs mesh. In this technique, lungs motion considering velocity coherence are inserted as a penalty in regularization function. The results indicate that high registration accuracy is achievable with CPD. This method is helpful for calculating of displacement vector and analyzing possible physiological and anatomical changes during treatment

Validation of MRI to TRUS registration for high-dose-rate prostate brachytherapy.

Science.gov (United States)

Poulin, Eric; Boudam, Karim; Pinter, Csaba; Kadoury, Samuel; Lasso, Andras; Fichtinger, Gabor; Ménard, Cynthia

The objective of this study was to develop and validate an open-source module for MRI to transrectal ultrasound (TRUS) registration to support tumor-targeted prostate brachytherapy. In this study, 15 patients with prostate cancer lesions visible on multiparametric MRI were selected for the validation. T2-weighted images with 1-mm isotropic voxel size and diffusion weighted images were acquired on a 1.5T Siemens imager. Three-dimensional (3D) TRUS images with 0.5-mm slice thickness were acquired. The investigated registration module was incorporated in the open-source 3D Slicer platform, which can compute rigid and deformable transformations. An extension of 3D Slicer, SlicerRT, allows import of and export to DICOM-RT formats. For validation, similarity indices, prostate volumes, and centroid positions were determined in addition to registration errors for common 3D points identified by an experienced radiation oncologist. The average time to compute the registration was 35 ± 3 s. For the rigid and deformable registration, respectively, Dice similarity coefficients were 0.87 ± 0.05 and 0.93 ± 0.01 while the 95% Hausdorff distances were 4.2 ± 1.0 and 2.2 ± 0.3 mm. MRI volumes obtained after the rigid and deformable registration were not statistically different (p > 0.05) from reference TRUS volumes. For the rigid and deformable registration, respectively, 3D distance errors between reference and registered centroid positions were 2.1 ± 1.0 and 0.4 ± 0.1 mm while registration errors between common points were 3.5 ± 3.2 and 2.3 ± 1.1 mm. Deformable registration was found significantly better (p < 0.05) than rigid registration for all parameters. An open-source MRI to TRUS registration platform was validated for integration in the brachytherapy workflow. Copyright © 2017 American Brachytherapy Society. Published by Elsevier Inc. All rights reserved.

Image to physical space registration of supine breast MRI for image guided breast surgery

Science.gov (United States)

Conley, Rebekah H.; Meszoely, Ingrid M.; Pheiffer, Thomas S.; Weis, Jared A.; Yankeelov, Thomas E.; Miga, Michael I.

2014-03-01

Breast conservation therapy (BCT) is a desirable option for many women diagnosed with early stage breast cancer and involves a lumpectomy followed by radiotherapy. However, approximately 50% of eligible women will elect for mastectomy over BCT despite equal survival benefit (provided margins of excised tissue are cancer free) due to uncertainty in outcome with regards to complete excision of cancerous cells, risk of local recurrence, and cosmesis. Determining surgical margins intraoperatively is difficult and achieving negative margins is not as robust as it needs to be, resulting in high re-operation rates and often mastectomy. Magnetic resonance images (MRI) can provide detailed information about tumor margin extents, however diagnostic images are acquired in a fundamentally different patient presentation than that used in surgery. Therefore, the high quality diagnostic MRIs taken in the prone position with pendant breast are not optimal for use in surgical planning/guidance due to the drastic shape change between preoperative images and the common supine surgical position. This work proposes to investigate the value of supine MRI in an effort to localize tumors intraoperatively using image-guidance. Mock intraoperative setups (realistic patient positioning in non-sterile environment) and preoperative imaging data were collected from a patient scheduled for a lumpectomy. The mock intraoperative data included a tracked laser range scan of the patient's breast surface, tracked center points of MR visible fiducials on the patient's breast, and tracked B-mode ultrasound and strain images. The preoperative data included a supine MRI with visible fiducial markers. Fiducial markers localized in the MRI were rigidly registered to their mock intraoperative counterparts using an optically tracked stylus. The root mean square (RMS) fiducial registration error using the tracked markers was 3.4mm. Following registration, the average closest point distance between the MR

Image-based dose planning of intracavitary brachytherapy: registration of serial-imaging studies using deformable anatomic templates

International Nuclear Information System (INIS)

Christensen, Gary E.; Carlson, Blake; Chao, K.S. Clifford; Yin Pen; Grigsby, Perry W.; Nguyen, Kim; Dempsey, James F; Lerma, Fritz A.; Bae, Kyongtae T.; Vannier, Michael W.; Williamson, Jeffrey F.

2001-01-01

Purpose: To demonstrate that high-dimensional voxel-to-voxel transformations, derived from continuum mechanics models of the underlying pelvic tissues, can be used to register computed tomography (CT) serial examinations into a single anatomic frame of reference for cumulative dose calculations. Methods and Materials: Three patients with locally advanced cervix cancer were treated with CT-compatible intracavitary (ICT) applicators. Each patient underwent five volumetric CT examinations: before initiating treatment, and immediately before and after the first and second ICT insertions, respectively. Each serial examination was rigidly registered to the patient's first ICT examination by aligning the bony anatomy. Detailed nonrigid alignment for organs (or targets) of interest was subsequently achieved by deforming the CT exams as a viscous-fluid, described by the Navier-Stokes equation, until the coincidence with the corresponding targets on CT image was maximized. In cases where ICT insertion induced very large and topologically complex rearrangements of pelvic organs, e.g., extreme uterine canal reorientation following tandem insertion, a viscous-fluid-landmark transformation was used to produce an initial registration. Results: For all three patients, reasonable registrations for organs (or targets) of interest were achieved. Fluid-landmark initialization was required in 4 of the 11 registrations. Relative to the best rigid bony landmark alignment, the viscous-fluid registration resulted in average soft-tissue displacements from 2.8 to 28.1 mm, and improved organ coincidence from the range of 5.2% to 72.2% to the range of 90.6% to 100%. Compared to the viscous-fluid transformation, global registration of bony anatomy mismatched 5% or more of the contoured organ volumes by 15-25 mm. Conclusion: Pelvic soft-tissue structures undergo large deformations and displacements during the external-beam and multiple-ICT course of radiation therapy for locally advanced cervix

3D Rigid Registration by Cylindrical Phase Correlation Method

Czech Academy of Sciences Publication Activity Database

Bican, Jakub; Flusser, Jan

2009-01-01

Roč. 30, č. 10 (2009), s. 914-921 ISSN 0167-8655 R&D Projects: GA MŠk 1M0572; GA ČR GA102/08/1593 Grant - others:GAUK(CZ) 48908 Institutional research plan: CEZ:AV0Z10750506 Keywords : 3D registration * correlation methods * Image registration Subject RIV: BD - Theory of Information Impact factor: 1.303, year: 2009 http://library.utia.cas.cz/separaty/2009/ZOI/bican-3d digit registration by cylindrical phase correlation method.pdf

Feasibility of Multimodal Deformable Registration for Head and Neck Tumor Treatment Planning

Energy Technology Data Exchange (ETDEWEB)

Fortunati, Valerio, E-mail: v.fortunati@erasmusmc.nl [Biomedical Imaging Group Rotterdam, Department of Medical Informatics and Radiology, Erasmus MC University Medical Center, Rotterdam (Netherlands); Verhaart, René F. [Hyperthermia Unit, Department of Radiation Oncology, Erasmus MC University Medical Center Cancer Institute, Rotterdam (Netherlands); Angeloni, Francesco [Istituto di Ricovero e Cura a Carattere Scientifico Foundation SDN for Research and High Education in Nuclear Diagnostics, Naples (Italy); Lugt, Aad van der [Department of Radiology, Erasmus MC University Medical Center, Rotterdam (Netherlands); Niessen, Wiro J. [Biomedical Imaging Group Rotterdam, Department of Medical Informatics and Radiology, Erasmus MC University Medical Center, Rotterdam (Netherlands); Faculty of Applied Sciences, Delft University of Technology, Delft (Netherlands); Veenland, Jifke F. [Biomedical Imaging Group Rotterdam, Department of Medical Informatics and Radiology, Erasmus MC University Medical Center, Rotterdam (Netherlands); Paulides, Margarethus M. [Hyperthermia Unit, Department of Radiation Oncology, Erasmus MC University Medical Center Cancer Institute, Rotterdam (Netherlands); Walsum, Theo van [Biomedical Imaging Group Rotterdam, Department of Medical Informatics and Radiology, Erasmus MC University Medical Center, Rotterdam (Netherlands)

2014-09-01

Purpose: To investigate the feasibility of using deformable registration in clinical practice to fuse MR and CT images of the head and neck for treatment planning. Method and Materials: A state-of-the-art deformable registration algorithm was optimized, evaluated, and compared with rigid registration. The evaluation was based on manually annotated anatomic landmarks and regions of interest in both modalities. We also developed a multiparametric registration approach, which simultaneously aligns T1- and T2-weighted MR sequences to CT. This was evaluated and compared with single-parametric approaches. Results: Our results show that deformable registration yielded a better accuracy than rigid registration, without introducing unrealistic deformations. For deformable registration, an average landmark alignment of approximatively 1.7 mm was obtained. For all the regions of interest excluding the cerebellum and the parotids, deformable registration provided a median modified Hausdorff distance of approximatively 1 mm. Similar accuracies were obtained for the single-parameter and multiparameter approaches. Conclusions: This study demonstrates that deformable registration of head-and-neck CT and MR images is feasible, with overall a significanlty higher accuracy than for rigid registration.

Feasibility of Multimodal Deformable Registration for Head and Neck Tumor Treatment Planning

International Nuclear Information System (INIS)

Fortunati, Valerio; Verhaart, René F.; Angeloni, Francesco; Lugt, Aad van der; Niessen, Wiro J.; Veenland, Jifke F.; Paulides, Margarethus M.; Walsum, Theo van

2014-01-01

Purpose: To investigate the feasibility of using deformable registration in clinical practice to fuse MR and CT images of the head and neck for treatment planning. Method and Materials: A state-of-the-art deformable registration algorithm was optimized, evaluated, and compared with rigid registration. The evaluation was based on manually annotated anatomic landmarks and regions of interest in both modalities. We also developed a multiparametric registration approach, which simultaneously aligns T1- and T2-weighted MR sequences to CT. This was evaluated and compared with single-parametric approaches. Results: Our results show that deformable registration yielded a better accuracy than rigid registration, without introducing unrealistic deformations. For deformable registration, an average landmark alignment of approximatively 1.7 mm was obtained. For all the regions of interest excluding the cerebellum and the parotids, deformable registration provided a median modified Hausdorff distance of approximatively 1 mm. Similar accuracies were obtained for the single-parameter and multiparameter approaches. Conclusions: This study demonstrates that deformable registration of head-and-neck CT and MR images is feasible, with overall a significanlty higher accuracy than for rigid registration

Alternative radiation-free registration technique for image-guided pedicle screw placement in deformed cervico-thoracic segments.

Science.gov (United States)

Kantelhardt, Sven R; Neulen, Axel; Keric, Naureen; Gutenberg, Angelika; Conrad, Jens; Giese, Alf

2017-10-01

Image-guided pedicle screw placement in the cervico-thoracic region is a commonly applied technique. In some patients with deformed cervico-thoracic segments, conventional or 3D fluoroscopy based registration of image-guidance might be difficult or impossible because of the anatomic/pathological conditions. Landmark based registration has been used as an alternative, mostly using separate registration of each vertebra. We here investigated a routine for landmark based registration of rigid spinal segments as single objects, using cranial image-guidance software. Landmark based registration of image-guidance was performed using cranial navigation software. After surgical exposure of the spinous processes, lamina and facet joints and fixation of a reference marker array, up to 26 predefined landmarks were acquired using a pointer. All pedicle screws were implanted using image guidance alone. Following image-guided screw placement all patients underwent postoperative CT scanning. Screw positions as well as intraoperative and clinical parameters were retrospectively analyzed. Thirteen patients received 73 pedicle screws at levels C6 to Th8. Registration of spinal segments, using the cranial image-guidance succeeded in all cases. Pedicle perforations were observed in 11.0%, severe perforations of >2 mm occurred in 5.4%. One patient developed a transient C8 syndrome and had to be revised for deviation of the C7 pedicle screw. No other pedicle screw-related complications were observed. In selected patients suffering from pathologies of the cervico-thoracic region, which impair intraoperative fluoroscopy or 3D C-arm imaging, landmark based registration of image-guidance using cranial software is a feasible, radiation-saving and a safe alternative.

Development of a hardware-based registration system for the multimodal medical images by USB cameras

International Nuclear Information System (INIS)

Iwata, Michiaki; Minato, Kotaro; Watabe, Hiroshi; Koshino, Kazuhiro; Yamamoto, Akihide; Iida, Hidehiro

2009-01-01

There are several medical imaging scanners and each modality has different aspect for visualizing inside of human body. By combining these images, diagnostic accuracy could be improved, and therefore, several attempts for multimodal image registration have been implemented. One popular approach is to use hybrid image scanners such as positron emission tomography (PET)/CT and single photon emission computed tomography (SPECT)/CT. However, these hybrid scanners are expensive and not fully available. We developed multimodal image registration system with universal serial bus (USB) cameras, which is inexpensive and applicable to any combinations of existed conventional imaging scanners. The multiple USB cameras will determine the three dimensional positions of a patient while scanning. Using information of these positions and rigid body transformation, the acquired image is registered to the common coordinate which is shared with another scanner. For each scanner, reference marker is attached on gantry of the scanner. For observing the reference marker's position by the USB cameras, the location of the USB cameras can be arbitrary. In order to validate the system, we scanned a cardiac phantom with different positions by PET and MRI scanners. Using this system, images from PET and MRI were visually aligned, and good correlations between PET and MRI images were obtained after the registration. The results suggest this system can be inexpensively used for multimodal image registrations. (author)

Registration of T2-weighted and diffusion-weighted MR images of the prostate: comparison between manual and landmark-based methods

Science.gov (United States)

Peng, Yahui; Jiang, Yulei; Soylu, Fatma N.; Tomek, Mark; Sensakovic, William; Oto, Aytekin

2012-02-01

Quantitative analysis of multi-parametric magnetic resonance (MR) images of the prostate, including T2-weighted (T2w) and diffusion-weighted (DW) images, requires accurate image registration. We compared two registration methods between T2w and DW images. We collected pre-operative MR images of 124 prostate cancer patients (68 patients scanned with a GE scanner and 56 with Philips scanners). A landmark-based rigid registration was done based on six prostate landmarks in both T2w and DW images identified by a radiologist. Independently, a researcher manually registered the same images. A radiologist visually evaluated the registration results by using a 5-point ordinal scale of 1 (worst) to 5 (best). The Wilcoxon signed-rank test was used to determine whether the radiologist's ratings of the results of the two registration methods were significantly different. Results demonstrated that both methods were accurate: the average ratings were 4.2, 3.3, and 3.8 for GE, Philips, and all images, respectively, for the landmark-based method; and 4.6, 3.7, and 4.2, respectively, for the manual method. The manual registration results were more accurate than the landmark-based registration results (p < 0.0001 for GE, Philips, and all images). Therefore, the manual method produces more accurate registration between T2w and DW images than the landmark-based method.

Registration of angiographic image on real-time fluoroscopic image for image-guided percutaneous coronary intervention.

Science.gov (United States)

Kim, Dongkue; Park, Sangsoo; Jeong, Myung Ho; Ryu, Jeha

2018-02-01

In percutaneous coronary intervention (PCI), cardiologists must study two different X-ray image sources: a fluoroscopic image and an angiogram. Manipulating a guidewire while alternately monitoring the two separate images on separate screens requires a deep understanding of the anatomy of coronary vessels and substantial training. We propose 2D/2D spatiotemporal image registration of the two images in a single image in order to provide cardiologists with enhanced visual guidance in PCI. The proposed 2D/2D spatiotemporal registration method uses a cross-correlation of two ECG series in each image to temporally synchronize two separate images and register an angiographic image onto the fluoroscopic image. A guidewire centerline is then extracted from the fluoroscopic image in real time, and the alignment of the centerline with vessel outlines of the chosen angiographic image is optimized using the iterative closest point algorithm for spatial registration. A proof-of-concept evaluation with a phantom coronary vessel model with engineering students showed an error reduction rate greater than 74% on wrong insertion to nontarget branches compared to the non-registration method and more than 47% reduction in the task completion time in performing guidewire manipulation for very difficult tasks. Evaluation with a small number of experienced doctors shows a potentially significant reduction in both task completion time and error rate for difficult tasks. The total registration time with real procedure X-ray (angiographic and fluoroscopic) images takes [Formula: see text] 60 ms, which is within the fluoroscopic image acquisition rate of 15 Hz. By providing cardiologists with better visual guidance in PCI, the proposed spatiotemporal image registration method is shown to be useful in advancing the guidewire to the coronary vessel branches, especially those difficult to insert into.

SU-E-J-222: Evaluation of Deformable Registration of PET/CT Images for Cervical Cancer Brachytherapy

International Nuclear Information System (INIS)

Liao, Y; Turian, J; Templeton, A; Kiel, K; Chu, J; Kadir, T

2014-01-01

Purpose: PET/CT provides important functional information for radiotherapy targeting of cervical cancer. However, repeated PET/CT procedures for external beam and subsequent brachytherapy expose patients to additional radiation and are not cost effective. Our goal is to investigate the possibility of propagating PET-active volumes for brachytherapy procedures through deformable image registration (DIR) of earlier PET/CT and ultimately to minimize the number of PET/CT image sessions required. Methods: Nine cervical cancer patients each received their brachytherapy preplanning PET/CT at the end of EBRT with a Syed template in place. The planning PET/CT was acquired on the day of brachytherapy treatment with the actual applicator (Syed or Tandem and Ring) and rigidly registered. The PET/CT images were then deformably registered creating a third (deformed) image set for target prediction. Regions of interest with standardized uptake values (SUV) greater than 65% of maximum SUV were contoured as target volumes in all three sets of PET images. The predictive value of the registered images was evaluated by comparing the preplanning and deformed PET volumes with the planning PET volume using Dice's coefficient (DC) and center-of-mass (COM) displacement. Results: The average DCs were 0.12±0.14 and 0.19±0.16 for rigid and deformable predicted target volumes, respectively. The average COM displacements were 1.9±0.9 cm and 1.7±0.7 cm for rigid and deformable registration, respectively. The DCs were improved by deformable registration, however, both were lower than published data for DIR in other modalities and clinical sites. Anatomical changes caused by different brachytherapy applicators could have posed a challenge to the DIR algorithm. The physiological change from interstitial needle placement may also contribute to lower DC. Conclusion: The clinical use of DIR in PET/CT for cervical cancer brachytherapy appears to be limited by applicator choice and requires further

Re-Irradiation of Hepatocellular Carcinoma: Clinical Applicability of Deformable Image Registration.

Science.gov (United States)

Lee, Dong Soo; Woo, Joong Yeol; Kim, Jun Won; Seong, Jinsil

2016-01-01

This study aimed to evaluate whether the deformable image registration (DIR) method is clinically applicable to the safe delivery of re-irradiation in hepatocellular carcinoma (HCC). Between August 2010 and March 2012, 12 eligible HCC patients received re-irradiation using helical tomotherapy. The median total prescribed radiation doses at first irradiation and re-irradiation were 50 Gy (range, 36-60 Gy) and 50 Gy (range, 36-58.42 Gy), respectively. Most re-irradiation therapies (11 of 12) were administered to previously irradiated or marginal areas. Dose summation results were reproduced using DIR by rigid and deformable registration methods, and doses of organs-at-risk (OARs) were evaluated. Treatment outcomes were also assessed. Thirty-six dose summation indices were obtained for three OARs (bowel, duodenum, and stomach doses in each patient). There was no statistical difference between the two different types of DIR methods (rigid and deformable) in terms of calculated summation ΣD (0.1 cc, 1 cc, 2 cc, and max) in each OAR. The median total mean remaining liver doses (M(RLD)) in rigid- and deformable-type registration were not statistically different for all cohorts (p=0.248), although a large difference in M(RLD) was observed when there was a significant difference in spatial liver volume change between radiation intervals. One duodenal ulcer perforation developed 20 months after re-irradiation. Although current dose summation algorithms and uncertainties do not warrant accurate dosimetric results, OARs-based DIR dose summation can be usefully utilized in the re-irradiation of HCC. Appropriate cohort selection, watchful interpretation, and selective use of DIR methods are crucial to enhance the radio-therapeutic ratio.

Comparison of time-series registration methods in breast dynamic infrared imaging

Science.gov (United States)

Riyahi-Alam, S.; Agostini, V.; Molinari, F.; Knaflitz, M.

2015-03-01

Automated motion reduction in dynamic infrared imaging is on demand in clinical applications, since movement disarranges time-temperature series of each pixel, thus originating thermal artifacts that might bias the clinical decision. All previously proposed registration methods are feature based algorithms requiring manual intervention. The aim of this work is to optimize the registration strategy specifically for Breast Dynamic Infrared Imaging and to make it user-independent. We implemented and evaluated 3 different 3D time-series registration methods: 1. Linear affine, 2. Non-linear Bspline, 3. Demons applied to 12 datasets of healthy breast thermal images. The results are evaluated through normalized mutual information with average values of 0.70 ±0.03, 0.74 ±0.03 and 0.81 ±0.09 (out of 1) for Affine, Bspline and Demons registration, respectively, as well as breast boundary overlap and Jacobian determinant of the deformation field. The statistical analysis of the results showed that symmetric diffeomorphic Demons' registration method outperforms also with the best breast alignment and non-negative Jacobian values which guarantee image similarity and anatomical consistency of the transformation, due to homologous forces enforcing the pixel geometric disparities to be shortened on all the frames. We propose Demons' registration as an effective technique for time-series dynamic infrared registration, to stabilize the local temperature oscillation.

THE IMAGE REGISTRATION OF FOURIER-MELLIN BASED ON THE COMBINATION OF PROJECTION AND GRADIENT PREPROCESSING

Directory of Open Access Journals (Sweden)

D. Gao

2017-09-01

Full Text Available Image registration is one of the most important applications in the field of image processing. The method of Fourier Merlin transform, which has the advantages of high precision and good robustness to change in light and shade, partial blocking, noise influence and so on, is widely used. However, not only this method can’t obtain the unique mutual power pulse function for non-parallel image pairs, even part of image pairs also can’t get the mutual power function pulse. In this paper, an image registration method based on Fourier-Mellin transformation in the view of projection-gradient preprocessing is proposed. According to the projection conformational equation, the method calculates the matrix of image projection transformation to correct the tilt image; then, gradient preprocessing and Fourier-Mellin transformation are performed on the corrected image to obtain the registration parameters. Eventually, the experiment results show that the method makes the image registration of Fourier-Mellin transformation not only applicable to the registration of the parallel image pairs, but also to the registration of non-parallel image pairs. What’s more, the better registration effect can be obtained

Behaviors study of image registration algorithms in image guided radiation therapy

International Nuclear Information System (INIS)

Zou Lian; Hou Qing

2008-01-01

Objective: Study the behaviors of image registration algorithms, and analyze the elements which influence the performance of image registrations. Methods: Pre-known corresponding coordinates were appointed for reference image and moving image, and then the influence of region of interest (ROI) selection, transformation function initial parameters and coupled parameter spaces on registration results were studied with a software platform developed in home. Results: Region of interest selection had a manifest influence on registration performance. An improperly chosen ROI resulted in a bad registration. Transformation function initial parameters selection based on pre-known information could improve the accuracy of image registration. Coupled parameter spaces would enhance the dependence of image registration algorithm on ROI selection. Conclusions: It is necessary for clinic IGRT to obtain a ROI selection strategy (depending on specific commercial software) correlated to tumor sites. Three suggestions for image registration technique developers are automatic selection of the initial parameters of transformation function based on pre-known information, developing specific image registration algorithm for specific image feature, and assembling real-time image registration algorithms according to tumor sites selected by software user. (authors)

Large deformation diffeomorphic metric mapping registration of reconstructed 3D histological section images and in vivo MR images

Directory of Open Access Journals (Sweden)

Can Ceritoglu

2010-05-01

Full Text Available Our current understanding of neuroanatomical abnormalities in neuropsychiatric diseases is based largely on magnetic resonance imaging (MRI and post mortem histological analyses of the brain. Further advances in elucidating altered brain structure in these human conditions might emerge from combining MRI and histological methods. We propose a multistage method for registering 3D volumes reconstructed from histological sections to corresponding in vivo MRI volumes from the same subjects: (1 manual segmentation of white matter (WM, gray matter (GM and cerebrospinal fluid (CSF compartments in histological sections, (2 alignment of consecutive histological sections using 2D rigid transformation to construct a 3D histological image volume from the aligned sections, (3 registration of reconstructed 3D histological volumes to the corresponding 3D MRI volumes using 3D affine transformation, (4 intensity normalization of images via histogram matching and (5 registration of the volumes via intensity based Large Deformation Diffeomorphic Metric (LDDMM image matching algorithm. Here we demonstrate the utility of our method in the transfer of cytoarchitectonic information from histological sections to identify regions of interest in MRI scans of nine adult macaque brains for morphometric analyses. LDDMM improved the accuracy of the registration via decreased distances between GM/CSF surfaces after LDDMM (0.39±0.13 mm compared to distances after affine registration (0.76±0.41 mm. Similarly, WM/GM distances decreased to 0.28±0.16 mm after LDDMM compared to 0.54±0.39 mm after affine registration. The multistage registration method may find broad application for mapping histologically based information, e.g., receptor distributions, gene expression, onto MRI volumes.

SU-F-T-421: Dosimetry Change During Radiotherapy and Dosimetry Difference for Rigid and Deformed Registration in the Mid-Thoracic Esophageal Carcinoma

Energy Technology Data Exchange (ETDEWEB)

Tao, C; Liu, T; Chen, J; Zhu, J; Yin, Y [Shandong Cancer Hospital and Institute, Jinan, Shandong (China)

2016-06-15

Purpose: This study aimed to analyze dosimetry changes during radiotherapy for the mid-thoracic esophageal carcinoma, and investigate dosimetry difference between rigid and deformed registration. Methods: Twelve patients with primary middle thoracic esophageal carcinoma were selected randomly. Based on first CT scanning of each patient, plans-o were generated by experience physicists. After 20 fractions treatment, the corresponding plans-re were created with second CT scanning. And then, these two CT images were rigid and deformed registration respectively, and the dose was accumulated plan-o with plan-re. The dosimetry variation of these plans (plan-o: with 30 fractions, plan-rig: the accumulated dose with rigid registration and plan-def: the accumulated dose with deformed registration) were evaluated by paired T-test. Results: The V20 value of total lung were 32.68%, 30.3% and 29.71% for plan-o, plan-rig and plan-def respectively. The mean dose of total lung was 17.19 Gy, 16.67 Gy and 16.51 Gy for plan-o plan-rig and plan-def respectively. There were significant differences between plan-o and plan-rig or plan-def for both V20 and mean dose of total lung (with p= 0.003, p= 0.000 for V20 and p=0.008, p= 0.000 for mean dose respectively). There was no significant difference between plan-rig and plan-def (with p=0.118 for V20 and p=0.384 for mean dose). The max dose of spinal-cord was 41.95 Gy, 41.48 Gy and 41.4 Gy for plan-o, plan-rig and plan-def respectively. There were no significant differences for the max dose of spinal-cord between these plans. Conclusion: The target volume changes and anatomic position displacement of mid-thoracic esophageal carcinoma should not be neglected in clinics. These changes would cause overdose in normal tissue. Therefore, it is necessary to have another CT scanning and re-plan during the mid-thoracic esophageal carcinoma radiotherapy. And the dosimetry difference between rigid and deformed fusions was not found in this study.

Fast and robust multi-atlas segmentation of brain magnetic resonance images

DEFF Research Database (Denmark)

Lötjönen, Jyrki Mp; Wolz, Robin; Koikkalainen, Juha R

2010-01-01

We introduce an optimised pipeline for multi-atlas brain MRI segmentation. Both accuracy and speed of segmentation are considered. We study different similarity measures used in non-rigid registration. We show that intensity differences for intensity normalised images can be used instead of stand......We introduce an optimised pipeline for multi-atlas brain MRI segmentation. Both accuracy and speed of segmentation are considered. We study different similarity measures used in non-rigid registration. We show that intensity differences for intensity normalised images can be used instead...... of standard normalised mutual information in registration without compromising the accuracy but leading to threefold decrease in the computation time. We study and validate also different methods for atlas selection. Finally, we propose two new approaches for combining multi-atlas segmentation and intensity...

Registration of clinical volumes to beams-eye-view images for real-time tracking

Energy Technology Data Exchange (ETDEWEB)

Bryant, Jonathan H.; Rottmann, Joerg; Lewis, John H.; Mishra, Pankaj; Berbeco, Ross I., E-mail: rberbeco@lroc.harvard.edu [Department of Radiation Oncology, Brigham and Women’s Hospital, Dana-Farber Cancer Institute and Harvard Medical School, Boston, Massachusetts 02115 (United States); Keall, Paul J. [Radiation Physics Laboratory, Sydney Medical School, University of Sydney, Sydney, New South Wales 2006 (Australia)

2014-12-15

Purpose: The authors combine the registration of 2D beam’s eye view (BEV) images and 3D planning computed tomography (CT) images, with relative, markerless tumor tracking to provide automatic absolute tracking of physician defined volumes such as the gross tumor volume (GTV). Methods: During treatment of lung SBRT cases, BEV images were continuously acquired with an electronic portal imaging device (EPID) operating in cine mode. For absolute registration of physician-defined volumes, an intensity based 2D/3D registration to the planning CT was performed using the end-of-exhale (EoE) phase of the four dimensional computed tomography (4DCT). The volume was converted from Hounsfield units into electron density by a calibration curve and digitally reconstructed radiographs (DRRs) were generated for each beam geometry. Using normalized cross correlation between the DRR and an EoE BEV image, the best in-plane rigid transformation was found. The transformation was applied to physician-defined contours in the planning CT, mapping them into the EPID image domain. A robust multiregion method of relative markerless lung tumor tracking quantified deviations from the EoE position. Results: The success of 2D/3D registration was demonstrated at the EoE breathing phase. By registering at this phase and then employing a separate technique for relative tracking, the authors are able to successfully track target volumes in the BEV images throughout the entire treatment delivery. Conclusions: Through the combination of EPID/4DCT registration and relative tracking, a necessary step toward the clinical implementation of BEV tracking has been completed. The knowledge of tumor volumes relative to the treatment field is important for future applications like real-time motion management, adaptive radiotherapy, and delivered dose calculations.

Edge-based correlation image registration for multispectral imaging

Science.gov (United States)

Nandy, Prabal [Albuquerque, NM

2009-11-17

Registration information for images of a common target obtained from a plurality of different spectral bands can be obtained by combining edge detection and phase correlation. The images are edge-filtered, and pairs of the edge-filtered images are then phase correlated to produce phase correlation images. The registration information can be determined based on these phase correlation images.

Phantom study and accuracy evaluation of an image-to-world registration approach used with electro-magnetic tracking system for neurosurgery

Science.gov (United States)

Li, Senhu; Sarment, David

2015-12-01

Minimally invasive neurosurgery needs intraoperative imaging updates and high efficient image guide system to facilitate the procedure. An automatic image guided system utilized with a compact and mobile intraoperative CT imager was introduced in this work. A tracking frame that can be easily attached onto the commercially available skull clamp was designed. With known geometry of fiducial and tracking sensor arranged on this rigid frame that was fabricated through high precision 3D printing, not only was an accurate, fully automatic registration method developed in a simple and less-costly approach, but also it helped in estimating the errors from fiducial localization in image space through image processing, and in patient space through the calibration of tracking frame. Our phantom study shows the fiducial registration error as 0.348+/-0.028mm, comparing the manual registration error as 1.976+/-0.778mm. The system in this study provided a robust and accurate image-to-patient registration without interruption of routine surgical workflow and any user interactions involved through the neurosurgery.

Elastic models application for thorax image registration

International Nuclear Information System (INIS)

Correa Prado, Lorena S; Diaz, E Andres Valdez; Romo, Raul

2007-01-01

This work consist of the implementation and evaluation of elastic alignment algorithms of biomedical images, which were taken at thorax level and simulated with the 4D NCAT digital phantom. Radial Basis Functions spatial transformations (RBF), a kind of spline, which allows carrying out not only global rigid deformations but also local elastic ones were applied, using a point-matching method. The applied functions were: Thin Plate Spline (TPS), Multiquadric (MQ) Gaussian and B-Spline, which were evaluated and compared by means of calculating the Target Registration Error and similarity measures between the registered images (the squared sum of intensity differences (SSD) and correlation coefficient (CC)). In order to value the user incurred error in the point-matching and segmentation tasks, two algorithms were also designed that calculate the Fiduciary Localization Error. TPS and MQ were demonstrated to have better performance than the others. It was proved RBF represent an adequate model for approximating the thorax deformable behaviour. Validation algorithms showed the user error was not significant

Free-form image registration of human cochlear μCT data using skeleton similarity as anatomical prior

DEFF Research Database (Denmark)

Kjer, Hans Martin; Fagertun, Jens; Vera, Sergio

2016-01-01

Better understanding of the anatomical variability of the human cochlear is important for the design and function of Cochlear Implants. Proper non-rigid alignment of high-resolution cochlear μCT data is a challenge for the typical cubic B-spline registration model. In this paper we study one way ...

A novel 3D volumetric voxel registration technique for volume-view-guided image registration of multiple imaging modalities

International Nuclear Information System (INIS)

Li Guang; Xie Huchen; Ning, Holly; Capala, Jacek; Arora, Barbara C.; Coleman, C. Norman; Camphausen, Kevin; Miller, Robert W.

2005-01-01

Purpose: To provide more clinically useful image registration with improved accuracy and reduced time, a novel technique of three-dimensional (3D) volumetric voxel registration of multimodality images is developed. Methods and Materials: This technique can register up to four concurrent images from multimodalities with volume view guidance. Various visualization effects can be applied, facilitating global and internal voxel registration. Fourteen computed tomography/magnetic resonance (CT/MR) image sets and two computed tomography/positron emission tomography (CT/PET) image sets are used. For comparison, an automatic registration technique using maximization of mutual information (MMI) and a three-orthogonal-planar (3P) registration technique are used. Results: Visually sensitive registration criteria for CT/MR and CT/PET have been established, including the homogeneity of color distribution. Based on the registration results of 14 CT/MR images, the 3D voxel technique is in excellent agreement with the automatic MMI technique and is indicatory of a global positioning error (defined as the means and standard deviations of the error distribution) using the 3P pixel technique: 1.8 deg ± 1.2 deg in rotation and 2.0 ± 1.3 (voxel unit) in translation. To the best of our knowledge, this is the first time that such positioning error has been addressed. Conclusion: This novel 3D voxel technique establishes volume-view-guided image registration of up to four modalities. It improves registration accuracy with reduced time, compared with the 3P pixel technique. This article suggests that any interactive and automatic registration should be safeguarded using the 3D voxel technique

A three-dimensional correlation method for registration of medical images in radiology

Energy Technology Data Exchange (ETDEWEB)

Georgiou, Michalakis; Sfakianakis, George N [Department of Radiology, University of Miami, Jackson Memorial Hospital, Miami, FL 33136 (United States); Nagel, Joachim H [Institute of Biomedical Engineering, University of Stuttgart, Stuttgart 70174 (Germany)

1999-12-31

The availability of methods to register multi-modality images in order to `fuse` them to correlate their information is increasingly becoming an important requirement for various diagnostic and therapeutic procedures. A variety of image registration methods have been developed but they remain limited to specific clinical applications. Assuming rigid body transformation, two images can be registered if their differences are calculated in terms of translation, rotation and scaling. This paper describes the development and testing of a new correlation based approach for three-dimensional image registration. First, the scaling factors introduced by the imaging devices are calculated and compensated for. Then, the two images become translation invariant by computing their three-dimensional Fourier magnitude spectra. Subsequently, spherical coordinate transformation is performed and then the three-dimensional rotation is computed using a novice approach referred to as {sup p}olar Shells{sup .} The method of polar shells maps the three angles of rotation into one rotation and two translations of a two-dimensional function and then proceeds to calculate them using appropriate transformations based on the Fourier invariance properties. A basic assumption in the method is that the three-dimensional rotation is constrained to one large and two relatively small angles. This assumption is generally satisfied in normal clinical settings. The new three-dimensional image registration method was tested with simulations using computer generated phantom data as well as actual clinical data. Performance analysis and accuracy evaluation of the method using computer simulations yielded errors in the sub-pixel range. (authors) 6 refs., 3 figs.

A three-dimensional correlation method for registration of medical images in radiology

International Nuclear Information System (INIS)

Georgiou, Michalakis; Sfakianakis, George N.; Nagel, Joachim H.

1998-01-01

The availability of methods to register multi-modality images in order to 'fuse' them to correlate their information is increasingly becoming an important requirement for various diagnostic and therapeutic procedures. A variety of image registration methods have been developed but they remain limited to specific clinical applications. Assuming rigid body transformation, two images can be registered if their differences are calculated in terms of translation, rotation and scaling. This paper describes the development and testing of a new correlation based approach for three-dimensional image registration. First, the scaling factors introduced by the imaging devices are calculated and compensated for. Then, the two images become translation invariant by computing their three-dimensional Fourier magnitude spectra. Subsequently, spherical coordinate transformation is performed and then the three-dimensional rotation is computed using a novice approach referred to as p olar Shells . The method of polar shells maps the three angles of rotation into one rotation and two translations of a two-dimensional function and then proceeds to calculate them using appropriate transformations based on the Fourier invariance properties. A basic assumption in the method is that the three-dimensional rotation is constrained to one large and two relatively small angles. This assumption is generally satisfied in normal clinical settings. The new three-dimensional image registration method was tested with simulations using computer generated phantom data as well as actual clinical data. Performance analysis and accuracy evaluation of the method using computer simulations yielded errors in the sub-pixel range. (authors)

Improving oncoplastic breast tumor bed localization for radiotherapy planning using image registration algorithms

Science.gov (United States)

Wodzinski, Marek; Skalski, Andrzej; Ciepiela, Izabela; Kuszewski, Tomasz; Kedzierawski, Piotr; Gajda, Janusz

2018-02-01

Knowledge about tumor bed localization and its shape analysis is a crucial factor for preventing irradiation of healthy tissues during supportive radiotherapy and as a result, cancer recurrence. The localization process is especially hard for tumors placed nearby soft tissues, which undergo complex, nonrigid deformations. Among them, breast cancer can be considered as the most representative example. A natural approach to improving tumor bed localization is the use of image registration algorithms. However, this involves two unusual aspects which are not common in typical medical image registration: the real deformation field is discontinuous, and there is no direct correspondence between the cancer and its bed in the source and the target 3D images respectively. The tumor no longer exists during radiotherapy planning. Therefore, a traditional evaluation approach based on known, smooth deformations and target registration error are not directly applicable. In this work, we propose alternative artificial deformations which model the tumor bed creation process. We perform a comprehensive evaluation of the most commonly used deformable registration algorithms: B-Splines free form deformations (B-Splines FFD), different variants of the Demons and TV-L1 optical flow. The evaluation procedure includes quantitative assessment of the dedicated artificial deformations, target registration error calculation, 3D contour propagation and medical experts visual judgment. The results demonstrate that the currently, practically applied image registration (rigid registration and B-Splines FFD) are not able to correctly reconstruct discontinuous deformation fields. We show that the symmetric Demons provide the most accurate soft tissues alignment in terms of the ability to reconstruct the deformation field, target registration error and relative tumor volume change, while B-Splines FFD and TV-L1 optical flow are not an appropriate choice for the breast tumor bed localization problem

Three dimensional image alignment, registration and fusion

International Nuclear Information System (INIS)

Treves, S.T.; Mitchell, K.D.; Habboush, I.H.

1998-01-01

Combined assessment of three dimensional anatomical and functional images (SPECT, PET, MRI, CT) is useful to determine the nature and extent of lesions in many parts of the body. Physicians principally rely on their spatial sense of mentally re-orient and overlap images obtained with different imaging modalities. Objective methods that enable easy and intuitive image registration can help the physician arrive at more optimal diagnoses and better treatment decisions. This review describes a simple, intuitive and robust image registration approach developed in our laboratory. It differs from most other registration techniques in that it allows the user to incorporate all of the available information within the images in the registration process. This method takes full advantage of the ability of knowledgeable operators to achieve image registration and fusion using an intuitive interactive visual approach. It can register images accurately and quickly without the use of elaborate mathematical modeling or optimization techniques. The method provides the operator with tools to manipulate images in three dimensions, including visual feedback techniques to assess the accuracy of registration (grids, overlays, masks, and fusion of images in different colors). Its application is not limited to brain imaging and can be applied to images from any region in the body. The overall effect is a registration algorithm that is easy to implement and can achieve accuracy on the order of one pixel

Incorporation of a laser range scanner into image-guided liver surgery: Surface acquisition, registration, and tracking

OpenAIRE

Cash, David M.; Sinha, Tuhin K.; Chapman, William C.; Terawaki, Hiromi; Dawant, Benoit M.; Galloway, Robert L.; Miga, Michael I.

2003-01-01

As image guided surgical procedures become increasingly diverse, there will be more scenarios where point-based fiducials cannot be accurately localized for registration and rigid body assumptions no longer hold. As a result, procedures will rely more frequently on anatomical surfaces for the basis of image alignment and will require intraoperative geometric data to measure and compensate for tissue deformation in the organ. In this paper we outline methods for which a laser range scanner may...

Position tracking of moving liver lesion based on real-time registration between 2D ultrasound and 3D preoperative images

International Nuclear Information System (INIS)

Weon, Chijun; Hyun Nam, Woo; Lee, Duhgoon; Ra, Jong Beom; Lee, Jae Young

2015-01-01

Purpose: Registration between 2D ultrasound (US) and 3D preoperative magnetic resonance (MR) (or computed tomography, CT) images has been studied recently for US-guided intervention. However, the existing techniques have some limits, either in the registration speed or the performance. The purpose of this work is to develop a real-time and fully automatic registration system between two intermodal images of the liver, and subsequently an indirect lesion positioning/tracking algorithm based on the registration result, for image-guided interventions. Methods: The proposed position tracking system consists of three stages. In the preoperative stage, the authors acquire several 3D preoperative MR (or CT) images at different respiratory phases. Based on the transformations obtained from nonrigid registration of the acquired 3D images, they then generate a 4D preoperative image along the respiratory phase. In the intraoperative preparatory stage, they properly attach a 3D US transducer to the patient’s body and fix its pose using a holding mechanism. They then acquire a couple of respiratory-controlled 3D US images. Via the rigid registration of these US images to the 3D preoperative images in the 4D image, the pose information of the fixed-pose 3D US transducer is determined with respect to the preoperative image coordinates. As feature(s) to use for the rigid registration, they may choose either internal liver vessels or the inferior vena cava. Since the latter is especially useful in patients with a diffuse liver disease, the authors newly propose using it. In the intraoperative real-time stage, they acquire 2D US images in real-time from the fixed-pose transducer. For each US image, they select candidates for its corresponding 2D preoperative slice from the 4D preoperative MR (or CT) image, based on the predetermined pose information of the transducer. The correct corresponding image is then found among those candidates via real-time 2D registration based on a

Markerless laser registration in image-guided oral and maxillofacial surgery.

Science.gov (United States)

Marmulla, Rüdiger; Lüth, Tim; Mühling, Joachim; Hassfeld, Stefan

2004-07-01

The use of registration markers in computer-assisted surgery is combined with high logistic costs and efforts. Markerless patient registration using laser scan surface registration techniques is a new challenging method. The present study was performed to evaluate the clinical accuracy in finding defined target points within the surgical site after markerless patient registration in image-guided oral and maxillofacial surgery. Twenty consecutive patients with different cranial diseases were scheduled for computer-assisted surgery. Data set alignment between the surgical site and the computed tomography (CT) data set was performed by markerless laser scan surface registration of the patient's face. Intraoral rigidly attached registration markers were used as target points, which had to be detected by an infrared pointer. The Surgical Segment Navigator SSN++ has been used for all procedures. SSN++ is an investigative product based on the SSN system that had previously been developed by the presenting authors with the support of Carl Zeiss (Oberkochen, Germany). SSN++ is connected to a Polaris infrared camera (Northern Digital, Waterloo, Ontario, Canada) and to a Minolta VI 900 3D digitizer (Tokyo, Japan) for high-resolution laser scanning. Minimal differences in shape between the laser scan surface and the surface generated from the CT data set could be detected. Nevertheless, high-resolution laser scan of the skin surface allows for a precise patient registration (mean deviation 1.1 mm, maximum deviation 1.8 mm). Radiation load, logistic costs, and efforts arising from the planning of computer-assisted surgery of the head can be reduced because native (markerless) CT data sets can be used for laser scan-based surface registration.

Fast and accurate registration of cranial CT images with A-mode ultrasound.

Science.gov (United States)

Fieten, Lorenz; Schmieder, Kirsten; Engelhardt, Martin; Pasalic, Lamija; Radermacher, Klaus; Heger, Stefan

2009-05-01

Within the CRANIO project, a navigation module based on preoperative computed tomography (CT) data was developed for Computer and Robot Assisted Neurosurgery. The approach followed for non-invasive user-interactive registration of cranial CT images with the physical operating space consists of surface-based registration following pre-registration based on anatomical landmarks. Surface-based registration relies on bone surface points digitized transcutaneously by means of an optically tracked A-mode ultrasound (US) probe. As probe alignment and thus bone surface point digitization may be time-consuming, we investigated how to obtain high registration accuracy despite inaccurate pre-registration and a limited number of digitized bone surface points. Furthermore, we aimed at efficient man-machine-interaction during the probe alignment process. Finally, we addressed the problem of registration plausibility estimation in our approach. We modified the Iterative Closest Point (ICP) algorithm, presented by Besl and McKay and frequently used for surface-based registration, such that it can escape from local minima of the cost function to be iteratively minimized. The random-based ICP (R-ICP) we developed is less influenced by the quality of the pre-registration as it can escape from local minima close to the starting point for iterative optimization in the 6D domain of rigid transformations. The R-ICP is also better suited to approximate the global minimum as it can escape from local minima in the vicinity of the global minimum, too. Furthermore, we developed both CT-less and CT-based probe alignment tools along with appropriate man-machine strategies for a more time-efficient palpation process. To improve registration reliability, we developed a simple plausibility test based on data readily available after registration. In a cadaver study, where we evaluated the R-ICP algorithm, the probe alignment tools, and the plausibility test, the R-ICP algorithm consistently

Performance evaluation of grid-enabled registration algorithms using bronze-standards

CERN Document Server

Glatard, T; Montagnat, J

2006-01-01

Evaluating registration algorithms is difficult due to the lack of gold standard in most clinical procedures. The bronze standard is a real-data based statistical method providing an alternative registration reference through a computationally intensive image database registration procedure. We propose in this paper an efficient implementation of this method through a grid-interfaced workflow enactor enabling the concurrent processing of hundreds of image registrations in a couple of hours only. The performances of two different grid infrastructures were compared. We computed the accuracy of 4 different rigid registration algorithms on longitudinal MRI images of brain tumors. Results showed an average subvoxel accuracy of 0.4 mm and 0.15 degrees in rotation.

Rigid-body motion correction of the liver in image reconstruction for golden-angle stack-of-stars DCE MRI.

Science.gov (United States)

Johansson, Adam; Balter, James; Cao, Yue

2018-03-01

Respiratory motion can affect pharmacokinetic perfusion parameters quantified from liver dynamic contrast-enhanced MRI. Image registration can be used to align dynamic images after reconstruction. However, intra-image motion blur remains after alignment and can alter the shape of contrast-agent uptake curves. We introduce a method to correct for inter- and intra-image motion during image reconstruction. Sixteen liver dynamic contrast-enhanced MRI examinations of nine subjects were performed using a golden-angle stack-of-stars sequence. For each examination, an image time series with high temporal resolution but severe streak artifacts was reconstructed. Images were aligned using region-limited rigid image registration within a region of interest covering the liver. The transformations resulting from alignment were used to correct raw data for motion by modulating and rotating acquired lines in k-space. The corrected data were then reconstructed using view sharing. Portal-venous input functions extracted from motion-corrected images had significantly greater peak signal enhancements (mean increase: 16%, t-test, P <  0.001) than those from images aligned using image registration after reconstruction. In addition, portal-venous perfusion maps estimated from motion-corrected images showed fewer artifacts close to the edge of the liver. Motion-corrected image reconstruction restores uptake curves distorted by motion. Motion correction also reduces motion artifacts in estimated perfusion parameter maps. Magn Reson Med 79:1345-1353, 2018. © 2017 International Society for Magnetic Resonance in Medicine. © 2017 International Society for Magnetic Resonance in Medicine.

SU-E-J-91: Biomechanical Deformable Image Registration of Longitudinal Lung CT Images

Energy Technology Data Exchange (ETDEWEB)

Cazoulat, G; Owen, D; Matuszak, M; Balter, J; Brock, K [University of Michigan, Ann Arbor, MI (United States)

2015-06-15

Purpose: Spatial correlation of lung tissue across longitudinal images, as the patient responds to treatment, is a critical step in adaptive radiotherapy. The goal of this work is to expand a biomechanical model-based deformable registration algorithm (Morfeus) to achieve accurate registration in the presence of significant anatomical changes. Methods: Four lung cancer patients previously treated with conventionally fractionated radiotherapy that exhibited notable tumor shrinkage during treatment were retrospectively evaluated. Exhale breathhold CT scans were obtained at treatment planning (PCT) and following three weeks (W3CT) of treatment. For each patient, the PCT was registered to the W3CT using Morfeus, a biomechanical model-based deformable registration algorithm, consisting of boundary conditions on the lungs and incorporating a sliding interface between the lung and chest wall. To model the complex response of the lung, an extension to Morfeus has been developed: (i) The vessel tree was segmented by thresholding a vesselness image based on the Hessian matrix’s eigenvalues and the centerline was extracted; (ii) A 3D shape context method was used to find correspondences between the trees of the two images; (ii) Correspondences were used as additional boundary conditions (Morfeus+vBC). An expert independently identified corresponding landmarks well distributed in the lung to compute Target Registration Errors (TRE). Results: The TRE within 15mm of the tumor boundaries (on average 11 landmarks) is: 6.1±1.8, 4.6±1.1 and 3.8±2.3 mm after rigid registration, Morfeus and Morfeus+vBC, respectively. The TRE in the rest of the lung (on average 13 landmarks) is: 6.4±3.9, 4.7±2.2 and 3.6±1.9 mm, which is on the order of the 2mm isotropic dose grid vector (3.5mm). Conclusion: The addition of boundary conditions on the vessels improved the accuracy in modeling the response of the lung and tumor over the course of radiotherapy. Minimizing and modeling these

Comparison of three filters in the solution of the Navier-Stokes equation in registration

DEFF Research Database (Denmark)

Gramkow, Claus; Bro-Nielsen, Morten

1997-01-01

he registration of images is an often encountered problem in e.g. medical imagi ng, satelite imaging, or stereo vision. In most applications a rigid deformation model does not suffce and complex deformations must be estimated. In medical registration Bajcsy et al. [1] have proposed the use...

Digital anthropomorphic phantoms of non-rigid human respiratory and voluntary body motion for investigating motion correction in emission imaging

International Nuclear Information System (INIS)

Könik, Arda; Johnson, Karen L; Dasari, Paul; Pretorius, P H; Dey, Joyoni; King, Michael A; Connolly, Caitlin M; Segars, Paul W; Lindsay, Clifford

2014-01-01

The development of methods for correcting patient motion in emission tomography has been receiving increased attention. Often the performance of these methods is evaluated through simulations using digital anthropomorphic phantoms, such as the commonly used extended cardiac torso (XCAT) phantom, which models both respiratory and cardiac motion based on human studies. However, non-rigid body motion, which is frequently seen in clinical studies, is not present in the standard XCAT phantom. In addition, respiratory motion in the standard phantom is limited to a single generic trend. In this work, to obtain a more realistic representation of motion, we developed a series of individual-specific XCAT phantoms, modeling non-rigid respiratory and non-rigid body motions derived from the magnetic resonance imaging (MRI) acquisitions of volunteers. Acquisitions were performed in the sagittal orientation using the Navigator methodology. Baseline (no motion) acquisitions at end-expiration were obtained at the beginning of each imaging session for each volunteer. For the body motion studies, MRI was again acquired only at end-expiration for five body motion poses (shoulder stretch, shoulder twist, lateral bend, side roll, and axial slide). For the respiratory motion studies, an MRI was acquired during free/regular breathing. The magnetic resonance slices were then retrospectively sorted into 14 amplitude-binned respiratory states, end-expiration, end-inspiration, six intermediary states during inspiration, and six during expiration using the recorded Navigator signal. XCAT phantoms were then generated based on these MRI data by interactive alignment of the organ contours of the XCAT with the MRI slices using a graphical user interface. Thus far we have created five body motion and five respiratory motion XCAT phantoms from the MRI acquisitions of six healthy volunteers (three males and three females). Non-rigid motion exhibited by the volunteers was reflected in both respiratory

Digital anthropomorphic phantoms of non-rigid human respiratory and voluntary body motion for investigating motion correction in emission imaging

Science.gov (United States)

Könik, Arda; Connolly, Caitlin M.; Johnson, Karen L.; Dasari, Paul; Segars, Paul W.; Pretorius, P. H.; Lindsay, Clifford; Dey, Joyoni; King, Michael A.

2014-07-01

The development of methods for correcting patient motion in emission tomography has been receiving increased attention. Often the performance of these methods is evaluated through simulations using digital anthropomorphic phantoms, such as the commonly used extended cardiac torso (XCAT) phantom, which models both respiratory and cardiac motion based on human studies. However, non-rigid body motion, which is frequently seen in clinical studies, is not present in the standard XCAT phantom. In addition, respiratory motion in the standard phantom is limited to a single generic trend. In this work, to obtain a more realistic representation of motion, we developed a series of individual-specific XCAT phantoms, modeling non-rigid respiratory and non-rigid body motions derived from the magnetic resonance imaging (MRI) acquisitions of volunteers. Acquisitions were performed in the sagittal orientation using the Navigator methodology. Baseline (no motion) acquisitions at end-expiration were obtained at the beginning of each imaging session for each volunteer. For the body motion studies, MRI was again acquired only at end-expiration for five body motion poses (shoulder stretch, shoulder twist, lateral bend, side roll, and axial slide). For the respiratory motion studies, an MRI was acquired during free/regular breathing. The magnetic resonance slices were then retrospectively sorted into 14 amplitude-binned respiratory states, end-expiration, end-inspiration, six intermediary states during inspiration, and six during expiration using the recorded Navigator signal. XCAT phantoms were then generated based on these MRI data by interactive alignment of the organ contours of the XCAT with the MRI slices using a graphical user interface. Thus far we have created five body motion and five respiratory motion XCAT phantoms from the MRI acquisitions of six healthy volunteers (three males and three females). Non-rigid motion exhibited by the volunteers was reflected in both respiratory

Convex Hull Aided Registration Method (CHARM).

Science.gov (United States)

Fan, Jingfan; Yang, Jian; Zhao, Yitian; Ai, Danni; Liu, Yonghuai; Wang, Ge; Wang, Yongtian

2017-09-01

Non-rigid registration finds many applications such as photogrammetry, motion tracking, model retrieval, and object recognition. In this paper we propose a novel convex hull aided registration method (CHARM) to match two point sets subject to a non-rigid transformation. First, two convex hulls are extracted from the source and target respectively. Then, all points of the point sets are projected onto the reference plane through each triangular facet of the hulls. From these projections, invariant features are extracted and matched optimally. The matched feature point pairs are mapped back onto the triangular facets of the convex hulls to remove outliers that are outside any relevant triangular facet. The rigid transformation from the source to the target is robustly estimated by the random sample consensus (RANSAC) scheme through minimizing the distance between the matched feature point pairs. Finally, these feature points are utilized as the control points to achieve non-rigid deformation in the form of thin-plate spline of the entire source point set towards the target one. The experimental results based on both synthetic and real data show that the proposed algorithm outperforms several state-of-the-art ones with respect to sampling, rotational angle, and data noise. In addition, the proposed CHARM algorithm also shows higher computational efficiency compared to these methods.

Numerical methods for image registration

CERN Document Server

Modersitzki, Jan

2003-01-01

Based on the author's lecture notes and research, this well-illustrated and comprehensive text is one of the first to provide an introduction to image registration with particular emphasis on numerical methods in medical imaging. Ideal for researchers in industry and academia, it is also a suitable study guide for graduate mathematicians, computer scientists, engineers, medical physicists, and radiologists.Image registration is utilised whenever information obtained from different viewpoints needs to be combined or compared and unwanted distortion needs to be eliminated. For example, CCTV imag

Automated analysis of small animal PET studies through deformable registration to an atlas

International Nuclear Information System (INIS)

Gutierrez, Daniel F.; Zaidi, Habib

2012-01-01

This work aims to develop a methodology for automated atlas-guided analysis of small animal positron emission tomography (PET) data through deformable registration to an anatomical mouse model. A non-rigid registration technique is used to put into correspondence relevant anatomical regions of rodent CT images from combined PET/CT studies to corresponding CT images of the Digimouse anatomical mouse model. The latter provides a pre-segmented atlas consisting of 21 anatomical regions suitable for automated quantitative analysis. Image registration is performed using a package based on the Insight Toolkit allowing the implementation of various image registration algorithms. The optimal parameters obtained for deformable registration were applied to simulated and experimental mouse PET/CT studies. The accuracy of the image registration procedure was assessed by segmenting mouse CT images into seven regions: brain, lungs, heart, kidneys, bladder, skeleton and the rest of the body. This was accomplished prior to image registration using a semi-automated algorithm. Each mouse segmentation was transformed using the parameters obtained during CT to CT image registration. The resulting segmentation was compared with the original Digimouse atlas to quantify image registration accuracy using established metrics such as the Dice coefficient and Hausdorff distance. PET images were then transformed using the same technique and automated quantitative analysis of tracer uptake performed. The Dice coefficient and Hausdorff distance show fair to excellent agreement and a mean registration mismatch distance of about 6 mm. The results demonstrate good quantification accuracy in most of the regions, especially the brain, but not in the bladder, as expected. Normalized mean activity estimates were preserved between the reference and automated quantification techniques with relative errors below 10 % in most of the organs considered. The proposed automated quantification technique is

Interactive, multi-modality image registrations for combined MRI/MRSI-planned HDR prostate brachytherapy

Directory of Open Access Journals (Sweden)

Galen Reed

2011-03-01

Full Text Available Purpose: This study presents the steps and criteria involved in the series of image registrations used clinically during the planning and dose delivery of focal high dose-rate (HDR brachytherapy of the prostate. Material and methods: Three imaging modalities – Magnetic Resonance Imaging (MRI, Magnetic Resonance Spectroscopic Imaging (MRSI, and Computed Tomography (CT – were used at different steps during the process. MRSI is used for identification of dominant intraprosatic lesions (DIL. A series of rigid and nonrigid transformations were applied to the data to correct for endorectal-coil-induced deformations and for alignment with the planning CT. Mutual information was calculated as a morphing metric. An inverse planning optimization algorithm was applied to boost dose to the DIL while providing protection to the urethra, penile bulb, rectum, and bladder. Six prostate cancer patients were treated using this protocol. Results: The morphing algorithm successfully modeled the probe-induced prostatic distortion. Mutual information calculated between the morphed images and images acquired without the endorectal probe showed a significant (p = 0.0071 increase to that calculated between the unmorphed images and images acquired without the endorectal probe. Both mutual information and visual inspection serve as effective diagnostics of image morphing. The entire procedure adds less than thirty minutes to the treatment planning. Conclusion: This work demonstrates the utility of image transformations and registrations to HDR brachytherapy of prostate cancer.

SU-E-J-114: A Practical Hybrid Method for Improving the Quality of CT-CBCT Deformable Image Registration for Head and Neck Radiotherapy

Energy Technology Data Exchange (ETDEWEB)

Liu, C; Kumarasiri, A; Chetvertkov, M; Gordon, J; Chetty, I; Siddiqui, F; Kim, J [Henry Ford Health System, Detroit, MI (United States)

2015-06-15

Purpose: Accurate deformable image registration (DIR) between CT and CBCT in H&N is challenging. In this study, we propose a practical hybrid method that uses not only the pixel intensities but also organ physical properties, structure volume of interest (VOI), and interactive local registrations. Methods: Five oropharyngeal cancer patients were selected retrospectively. For each patient, the planning CT was registered to the last fraction CBCT, where the anatomy difference was largest. A three step registration strategy was tested; Step1) DIR using pixel intensity only, Step2) DIR with additional use of structure VOI and rigidity penalty, and Step3) interactive local correction. For Step1, a public-domain open-source DIR algorithm was used (cubic B-spline, mutual information, steepest gradient optimization, and 4-level multi-resolution). For Step2, rigidity penalty was applied on bony anatomies and brain, and a structure VOI was used to handle the body truncation such as the shoulder cut-off on CBCT. Finally, in Step3, the registrations were reviewed on our in-house developed software and the erroneous areas were corrected via a local registration using level-set motion algorithm. Results: After Step1, there were considerable amount of registration errors in soft tissues and unrealistic stretching in the posterior to the neck and near the shoulder due to body truncation. The brain was also found deformed to a measurable extent near the superior border of CBCT. Such errors could be effectively removed by using a structure VOI and rigidity penalty. The rest of the local soft tissue error could be corrected using the interactive software tool. The estimated interactive correction time was approximately 5 minutes. Conclusion: The DIR using only the image pixel intensity was vulnerable to noise and body truncation. A corrective action was inevitable to achieve good quality of registrations. We found the proposed three-step hybrid method efficient and practical for CT

Preparing diagnostic 3D images for image registration with planning CT images

International Nuclear Information System (INIS)

Tracton, Gregg S.; Miller, Elizabeth P.; Rosenman, Julian; Chang, Sha X.; Sailer, Scott; Boxwala, Azaz; Chaney, Edward L.

1997-01-01

Purpose: Pre-radiotherapy (pre-RT) tomographic images acquired for diagnostic purposes often contain important tumor and/or normal tissue information which is poorly defined or absent in planning CT images. Our two years of clinical experience has shown that computer-assisted 3D registration of pre-RT images with planning CT images often plays an indispensable role in accurate treatment volume definition. Often the only available format of the diagnostic images is film from which the original 3D digital data must be reconstructed. In addition, any digital data, whether reconstructed or not, must be put into a form suitable for incorporation into the treatment planning system. The purpose of this investigation was to identify all problems that must be overcome before this data is suitable for clinical use. Materials and Methods: In the past two years we have 3D-reconstructed 300 diagnostic images from film and digital sources. As a problem was discovered we built a software tool to correct it. In time we collected a large set of such tools and found that they must be applied in a specific order to achieve the correct reconstruction. Finally, a toolkit (ediScan) was built that made all these tools available in the proper manner via a pleasant yet efficient mouse-based user interface. Results: Problems we discovered included different magnifications, shifted display centers, non-parallel image planes, image planes not perpendicular to the long axis of the table-top (shearing), irregularly spaced scans, non contiguous scan volumes, multiple slices per film, different orientations for slice axes (e.g. left-right reversal), slices printed at window settings corresponding to tissues of interest for diagnostic purposes, and printing artifacts. We have learned that the specific steps to correct these problems, in order of application, are: Also, we found that fast feedback and large image capacity (at least 2000 x 2000 12-bit pixels) are essential for practical application

An accelerated image matching technique for UAV orthoimage registration

Science.gov (United States)

Tsai, Chung-Hsien; Lin, Yu-Ching

2017-06-01

Using an Unmanned Aerial Vehicle (UAV) drone with an attached non-metric camera has become a popular low-cost approach for collecting geospatial data. A well-georeferenced orthoimage is a fundamental product for geomatics professionals. To achieve high positioning accuracy of orthoimages, precise sensor position and orientation data, or a number of ground control points (GCPs), are often required. Alternatively, image registration is a solution for improving the accuracy of a UAV orthoimage, as long as a historical reference image is available. This study proposes a registration scheme, including an Accelerated Binary Robust Invariant Scalable Keypoints (ABRISK) algorithm and spatial analysis of corresponding control points for image registration. To determine a match between two input images, feature descriptors from one image are compared with those from another image. A "Sorting Ring" is used to filter out uncorrected feature pairs as early as possible in the stage of matching feature points, to speed up the matching process. The results demonstrate that the proposed ABRISK approach outperforms the vector-based Scale Invariant Feature Transform (SIFT) approach where radiometric variations exist. ABRISK is 19.2 times and 312 times faster than SIFT for image sizes of 1000 × 1000 pixels and 4000 × 4000 pixels, respectively. ABRISK is 4.7 times faster than Binary Robust Invariant Scalable Keypoints (BRISK). Furthermore, the positional accuracy of the UAV orthoimage after applying the proposed image registration scheme is improved by an average of root mean square error (RMSE) of 2.58 m for six test orthoimages whose spatial resolutions vary from 6.7 cm to 10.7 cm.

Automatic intra-modality brain image registration method

International Nuclear Information System (INIS)

Whitaker, J.M.; Ardekani, B.A.; Braun, M.

1996-01-01

Full text: Registration of 3D images of brain of the same or different subjects has potential importance in clinical diagnosis, treatment planning and neurological research. The broad aim of our work is to produce an automatic and robust intra-modality, brain image registration algorithm for intra-subject and inter-subject studies. Our algorithm is composed of two stages. Initial alignment is achieved by finding the values of nine transformation parameters (representing translation, rotation and scale) that minimise the nonoverlapping regions of the head. This is achieved by minimisation of the sum of the exclusive OR of two binary head images, produced using the head extraction procedure described by Ardekani et al. (J Comput Assist Tomogr, 19:613-623, 1995). The initial alignment successfully determines the scale parameters and gross translation and rotation parameters. Fine alignment uses an objective function described for inter-modality registration in Ardekani et al. (ibid.). The algorithm segments one of the images to be aligned into a set of connected components using K-means clustering. Registration is achieved by minimising the K-means variance of the segmentation induced in the other image. Similarity of images of the same modality makes the method attractive for intra-modality registration. A 3D MR image, with voxel dimensions, 2x2x6 mm, was misaligned. The registered image shows visually accurate registration. The average displacement of a pixel from its correct location was measured to be 3.3 mm. The algorithm was tested on intra-subject MR images and was found to produce good qualitative results. Using the data available, the algorithm produced promising qualitative results in intra-subject registration. Further work is necessary in its application to intersubject registration, due to large variability in brain structure between subjects. Clinical evaluation of the algorithm for selected applications is required

Transmission imaging for registration of ictal and interictal single-photon emission tomography, magnetic resonance imaging and electroencephalography

Energy Technology Data Exchange (ETDEWEB)

Sipilae, O. [Epilepsy Unit, Neurology, Hospital for Children and Adolescents, Helsinki University Central Hospital (Finland); Laboratory of Biomedical Engineering, Helsinki University of Technology, P.O. Box 2200, FIN-02015 HUT (Finland); Nikkinen, P.; Liewendahl, K. [Division of Nuclear Medicine, Laboratory Department, Helsinki University Central Hospital (Finland); Savolainen, S. [Division of Nuclear Medicine, Laboratory Department, Helsinki University Central Hospital (Finland); Department of Radiology, Helsinki University Central Hospital (Finland); Granstroem, M.-L.; Gaily, E. [Epilepsy Unit, Neurology, Hospital for Children and Adolescents, Helsinki University Central Hospital (Finland); Poutanen, V.-P. [Department of Radiology, Helsinki University Central Hospital (Finland); Pohjonen, H. [Technology Development Centre, P.O. Box 69, 00101 Helsinki (Finland)

2000-02-01

A method developed for registration of ictal and interictal single-photon emission tomography (SPET), magnetic resonance imaging (MRI) and electroencephalography (EEG) is described. For SPET studies, technetium-99m ethyl cysteinate dimer (ECD) was injected intravenously while the patient was monitored on video-EEG to document the ictal or interictal state. Imaging was performed using a triple-head gamma camera equipped with a transmission imaging device using a gadolinium-153 source. The images (128 x 128 pixels, voxel size 3.7 x 3.7 x 3.6 mm{sup 3}) were reconstructed using an iterative algorithm and postfiltered with a Wiener filter. The gold-plated silver electrodes on the patient's scalp were utilized as markers for registration of the ictal and interictal SPET images, as these metallic markers were clearly seen on the transmission images. Fitting of the marker sets was based on a non-iterative least squares method. The interictal SPET image was subtracted from the ictal image after scaling. The T1-weighted MPRAGE MR images with voxel size of 1.0 x 1.0 x 1.0 mm{sup 3} were obtained with a 1.5-T scanner. For registration of MR and subtraction SPET images, the external marker set of the ictal SPET study was fitted to the surface of the head segmented from MR images. The SPET registration was tested with a phantom experiment. Registration of ictal and interictal SPET in five patient studies resulted in a 2-mm RMS residual of the marker sets. The estimated RMS error of registration in the final result combining locations of the electrodes, subtraction SPET and MR images was 3-5 mm. In conclusion, transmission imaging can be utilized for an accurate and easily implemented registration procedure for ictal and interictal SPET, MRI and EEG. (orig.)

A stereotactic method for the three-dimensional registration of multi-modality biologic images in animals: NMR, PET, histology, and autoradiography

International Nuclear Information System (INIS)

Humm, J.L.; Ballon, D.; Hu, Y.C.; Ruan, S.; Chui, C.; Tulipano, P.K.; Erdi, A.; Koutcher, J.; Zakian, K.; Urano, M.; Zanzonico, P.; Mattis, C.; Dyke, J.; Chen, Y.; Harrington, P.; O'Donoghue, J.A.; Ling, C.C.

2003-01-01

The objective of this work was to develop and then validate a stereotactic fiduciary marker system for tumor xenografts in rodents which could be used to co-register magnetic resonance imaging (MRI), PET, tissue histology, autoradiography, and measurements from physiologic probes. A Teflon TM fiduciary template has been designed which allows the precise insertion of small hollow Teflon rods (0.71 mm diameter) into a tumor. These rods can be visualized by MRI and PET as well as by histology and autoradiography on tissue sections. The methodology has been applied and tested on a rigid phantom, on tissue phantom material, and finally on tumor bearing mice. Image registration has been performed between the MRI and PET images for the rigid Teflon phantom and among MRI, digitized microscopy images of tissue histology, and autoradiograms for both tissue phantom and tumor-bearing mice. A registration accuracy, expressed as the average Euclidean distance between the centers of three fiduciary markers among the registered image sets, of 0.2±0.06 mm was achieved between MRI and microPET image sets of a rigid Teflon phantom. The fiduciary template allows digitized tissue sections to be co-registered with three-dimensional MRI images with an average accuracy of 0.21 and 0.25 mm for the tissue phantoms and tumor xenografts, respectively. Between histology and autoradiograms, it was 0.19 and 0.21 mm for tissue phantoms and tumor xenografts, respectively. The fiduciary marker system provides a coordinate system with which to correlate information from multiple image types, on a voxel-by-voxel basis, with sub-millimeter accuracy--even among imaging modalities with widely disparate spatial resolution and in the absence of identifiable anatomic landmarks

Myocardial strain assessment by cine cardiac magnetic resonance imaging using non-rigid registration.

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Tsadok, Yossi; Friedman, Zvi; Haluska, Brian A; Hoffmann, Rainer; Adam, Dan

2016-05-01

To evaluate a novel post-processing method for assessment of longitudinal mid-myocardial strain in standard cine cardiac magnetic resonance (CMR) imaging sequences. Cine CMR imaging and tagged cardiac magnetic resonance imaging (TMRI) were performed in 15 patients with acute myocardial infarction (AMI) and 15 healthy volunteers served as control group. A second group of 37 post-AMI patients underwent both cine CMR and late gadolinium enhancement (LGE) CMR exams. Speckle tracking echocardiography (STE) was performed in 36 of these patients. Cine CMR, TMRI and STE were analyzed to obtain longitudinal strain. LGE-CMR datasets were analyzed to evaluate scar extent. Comparison of peak systolic strain (PSS) measured from CMR and TMRI yielded a strong correlation (r=0.86, pcine CMR data. The method was found to be highly correlated with strain measurements obtained by TMRI and STE. This tool allows accurate discrimination between different transmurality states of myocardial infarction. Copyright © 2015 Elsevier Inc. All rights reserved.

Objective and expert-independent validation of retinal image registration algorithms by a projective imaging distortion model.

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Lee, Sangyeol; Reinhardt, Joseph M; Cattin, Philippe C; Abràmoff, Michael D

2010-08-01

Fundus camera imaging of the retina is widely used to diagnose and manage ophthalmologic disorders including diabetic retinopathy, glaucoma, and age-related macular degeneration. Retinal images typically have a limited field of view, and multiple images can be joined together using an image registration technique to form a montage with a larger field of view. A variety of methods for retinal image registration have been proposed, but evaluating such methods objectively is difficult due to the lack of a reference standard for the true alignment of the individual images that make up the montage. A method of generating simulated retinal images by modeling the geometric distortions due to the eye geometry and the image acquisition process is described in this paper. We also present a validation process that can be used for any retinal image registration method by tracing through the distortion path and assessing the geometric misalignment in the coordinate system of the reference standard. The proposed method can be used to perform an accuracy evaluation over the whole image, so that distortion in the non-overlapping regions of the montage components can be easily assessed. We demonstrate the technique by generating test image sets with a variety of overlap conditions and compare the accuracy of several retinal image registration models. Copyright 2010 Elsevier B.V. All rights reserved.

Groupwise registration of MR brain images with tumors

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Tang, Zhenyu; Wu, Yihong; Fan, Yong

2017-09-01

A novel groupwise image registration framework is developed for registering MR brain images with tumors. Our method iteratively estimates a normal-appearance counterpart for each tumor image to be registered and constructs a directed graph (digraph) of normal-appearance images to guide the groupwise image registration. Particularly, our method maps each tumor image to its normal appearance counterpart by identifying and inpainting brain tumor regions with intensity information estimated using a low-rank plus sparse matrix decomposition based image representation technique. The estimated normal-appearance images are groupwisely registered to a group center image guided by a digraph of images so that the total length of ‘image registration paths’ to be the minimum, and then the original tumor images are warped to the group center image using the resulting deformation fields. We have evaluated our method based on both simulated and real MR brain tumor images. The registration results were evaluated with overlap measures of corresponding brain regions and average entropy of image intensity information, and Wilcoxon signed rank tests were adopted to compare different methods with respect to their regional overlap measures. Compared with a groupwise image registration method that is applied to normal-appearance images estimated using the traditional low-rank plus sparse matrix decomposition based image inpainting, our method achieved higher image registration accuracy with statistical significance (p  =  7.02  ×  10-9).

Mid-space-independent deformable image registration.

Science.gov (United States)

Aganj, Iman; Iglesias, Juan Eugenio; Reuter, Martin; Sabuncu, Mert Rory; Fischl, Bruce

2017-05-15

Aligning images in a mid-space is a common approach to ensuring that deformable image registration is symmetric - that it does not depend on the arbitrary ordering of the input images. The results are, however, generally dependent on the mathematical definition of the mid-space. In particular, the set of possible solutions is typically restricted by the constraints that are enforced on the transformations to prevent the mid-space from drifting too far from the native image spaces. The use of an implicit atlas has been proposed as an approach to mid-space image registration. In this work, we show that when the atlas is aligned to each image in the native image space, the data term of implicit-atlas-based deformable registration is inherently independent of the mid-space. In addition, we show that the regularization term can be reformulated independently of the mid-space as well. We derive a new symmetric cost function that only depends on the transformation morphing the images to each other, rather than to the atlas. This eliminates the need for anti-drift constraints, thereby expanding the space of allowable deformations. We provide an implementation scheme for the proposed framework, and validate it through diffeomorphic registration experiments on brain magnetic resonance images. Copyright © 2017 Elsevier Inc. All rights reserved.

Registration of deformed multimodality medical images

International Nuclear Information System (INIS)

Moshfeghi, M.; Naidich, D.

1989-01-01

The registration and combination of images from different modalities have several potential applications, such as functional and anatomic studies, 3D radiation treatment planning, surgical planning, and retrospective studies. Image registration algorithms should correct for any local deformations caused by respiration, heart beat, imaging device distortions, and so forth. This paper reports on an elastic matching technique for registering deformed multimodality images. Correspondences between contours in the two images are used to stretch the deformed image toward its goal image. This process is repeated a number of times, with decreasing image stiffness. As the iterations continue, the stretched image better approximates its goal image

Registration of Vibro-acoustography Images and X-ray Mammography.

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Gholam Hosseini, H; Fatemi, M; Alizad, A

2005-01-01

Image registration has been widely used for generating more diagnostic and clinical values in medical imaging. On the other hand, inaccurate image registration and incorrect localization of region of interest risks a potential impact on patients. Vibro-acoustography (VA) is a new imaging modality that has been applied to both medical and industrial imaging. Combining unique diagnostic information of VA with other medical imaging is one of our research interests. In this work, we studied the VA and x-ray image pairs and adopted a flexible control-point selection technique for image registration. A modified second-order polynomial, which leads to a scale/rotation/translation invariant registration, was used. The results of registration were used to spatially transform the breast VA images to map with the x-ray mammography with a registration error of less than 1.65 mm. These two completely different modalities were combined to generate an image including a ratio of each image pixel value. Therefore, the proposed technique allows clinicians to maximize their insight by combining the information from x-ray mammogram and VA modalities into a single image.

Kidney deformation and intraprocedural registration: a study of elements of image-guided kidney surgery.

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Altamar, Hernan O; Ong, Rowena E; Glisson, Courtenay L; Viprakasit, Davis P; Miga, Michael I; Herrell, Stanley Duke; Galloway, Robert L

2011-03-01

Central to any image-guided surgical procedure is the alignment of image and physical coordinate spaces, or registration. We explored the task of registration in the kidney through in vivo and ex vivo porcine animal models and a human study of minimally invasive kidney surgery. A set of (n = 6) ex vivo porcine kidney models was utilized to study the effect of perfusion and loss of turgor caused by incision. Computed tomography (CT) and laser range scanner localizations of the porcine kidneys were performed before and after renal vessel clamping and after capsular incision. The da Vinci robotic surgery system was used for kidney surface acquisition and registration during robot-assisted laparoscopic partial nephrectomy. The surgeon acquired the physical surface data points with a tracked robotic instrument. These data points were aligned to preoperative CT for surface-based registrations. In addition, two biomechanical elastic computer models (isotropic and anisotropic) were constructed to simulate deformations in one of the kidneys to assess predictive capabilities. The mean displacement at the surface fiducials (glass beads) in six porcine kidneys was 4.4 ± 2.1 mm (range 3.4-6.7 mm), with a maximum displacement range of 6.1 to 11.2 mm. Surface-based registrations using the da Vinci robotic instrument in robot-assisted laparoscopic partial nephrectomy yielded mean and standard deviation closest point distances of 1.4 and 1.1 mm. With respect to computer model predictive capability, the target registration error was on average 6.7 mm without using the model and 3.2 mm with using the model. The maximum target error reduced from 11.4 to 6.2 mm. The anisotropic biomechanical model yielded better performance but was not statistically better. An initial point-based alignment followed by an iterative closest point registration is a feasible method of registering preoperative image (CT) space to intraoperative physical (robot) space. Although rigid registration provides

The role of image registration in brain mapping

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Toga, A.W.; Thompson, P.M.

2008-01-01

Image registration is a key step in a great variety of biomedical imaging applications. It provides the ability to geometrically align one dataset with another, and is a prerequisite for all imaging applications that compare datasets across subjects, imaging modalities, or across time. Registration algorithms also enable the pooling and comparison of experimental findings across laboratories, the construction of population-based brain atlases, and the creation of systems to detect group patterns in structural and functional imaging data. We review the major types of registration approaches used in brain imaging today. We focus on their conceptual basis, the underlying mathematics, and their strengths and weaknesses in different contexts. We describe the major goals of registration, including data fusion, quantification of change, automated image segmentation and labeling, shape measurement, and pathology detection. We indicate that registration algorithms have great potential when used in conjunction with a digital brain atlas, which acts as a reference system in which brain images can be compared for statistical analysis. The resulting armory of registration approaches is fundamental to medical image analysis, and in a brain mapping context provides a means to elucidate clinical, demographic, or functional trends in the anatomy or physiology of the brain. PMID:19890483

Pairwise registration of TLS point clouds using covariance descriptors and a non-cooperative game

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Zai, Dawei; Li, Jonathan; Guo, Yulan; Cheng, Ming; Huang, Pengdi; Cao, Xiaofei; Wang, Cheng

2017-12-01

It is challenging to automatically register TLS point clouds with noise, outliers and varying overlap. In this paper, we propose a new method for pairwise registration of TLS point clouds. We first generate covariance matrix descriptors with an adaptive neighborhood size from point clouds to find candidate correspondences, we then construct a non-cooperative game to isolate mutual compatible correspondences, which are considered as true positives. The method was tested on three models acquired by two different TLS systems. Experimental results demonstrate that our proposed adaptive covariance (ACOV) descriptor is invariant to rigid transformation and robust to noise and varying resolutions. The average registration errors achieved on three models are 0.46 cm, 0.32 cm and 1.73 cm, respectively. The computational times cost on these models are about 288 s, 184 s and 903 s, respectively. Besides, our registration framework using ACOV descriptors and a game theoretic method is superior to the state-of-the-art methods in terms of both registration error and computational time. The experiment on a large outdoor scene further demonstrates the feasibility and effectiveness of our proposed pairwise registration framework.

High performance deformable image registration algorithms for manycore processors

CERN Document Server

Shackleford, James; Sharp, Gregory

2013-01-01

High Performance Deformable Image Registration Algorithms for Manycore Processors develops highly data-parallel image registration algorithms suitable for use on modern multi-core architectures, including graphics processing units (GPUs). Focusing on deformable registration, we show how to develop data-parallel versions of the registration algorithm suitable for execution on the GPU. Image registration is the process of aligning two or more images into a common coordinate frame and is a fundamental step to be able to compare or fuse data obtained from different sensor measurements. E

Robust methods for automatic image-to-world registration in cone-beam CT interventional guidance

International Nuclear Information System (INIS)

Dang, H.; Otake, Y.; Schafer, S.; Stayman, J. W.; Kleinszig, G.; Siewerdsen, J. H.

2012-01-01

Purpose: Real-time surgical navigation relies on accurate image-to-world registration to align the coordinate systems of the image and patient. Conventional manual registration can present a workflow bottleneck and is prone to manual error and intraoperator variability. This work reports alternative means of automatic image-to-world registration, each method involving an automatic registration marker (ARM) used in conjunction with C-arm cone-beam CT (CBCT). The first involves a Known-Model registration method in which the ARM is a predefined tool, and the second is a Free-Form method in which the ARM is freely configurable. Methods: Studies were performed using a prototype C-arm for CBCT and a surgical tracking system. A simple ARM was designed with markers comprising a tungsten sphere within infrared reflectors to permit detection of markers in both x-ray projections and by an infrared tracker. The Known-Model method exercised a predefined specification of the ARM in combination with 3D-2D registration to estimate the transformation that yields the optimal match between forward projection of the ARM and the measured projection images. The Free-Form method localizes markers individually in projection data by a robust Hough transform approach extended from previous work, backprojected to 3D image coordinates based on C-arm geometric calibration. Image-domain point sets were transformed to world coordinates by rigid-body point-based registration. The robustness and registration accuracy of each method was tested in comparison to manual registration across a range of body sites (head, thorax, and abdomen) of interest in CBCT-guided surgery, including cases with interventional tools in the radiographic scene. Results: The automatic methods exhibited similar target registration error (TRE) and were comparable or superior to manual registration for placement of the ARM within ∼200 mm of C-arm isocenter. Marker localization in projection data was robust across all

Inter-slice bidirectional registration-based segmentation of the prostate gland in MR and CT image sequences

Energy Technology Data Exchange (ETDEWEB)

Khalvati, Farzad, E-mail: farzad.khalvati@uwaterloo.ca; Tizhoosh, Hamid R. [Department of Systems Design Engineering, University of Waterloo, Waterloo, Ontario N2L 3G1 (Canada); Salmanpour, Aryan; Rahnamayan, Shahryar [Department of Engineering and Applied Science, University of Ontario Institute of Technology, Oshawa, Ontario L1H 7K4 (Canada); Rodrigues, George [Department of Radiation Oncology, London Regional Cancer Program, London, Ontario N6C 2R6, Canada and Department of Epidemiology/Biostatistics, University of Western Ontario, London, Ontario N6A 3K7 (Canada)

2013-12-15

Purpose: Accurate segmentation and volume estimation of the prostate gland in magnetic resonance (MR) and computed tomography (CT) images are necessary steps in diagnosis, treatment, and monitoring of prostate cancer. This paper presents an algorithm for the prostate gland volume estimation based on the semiautomated segmentation of individual slices in T2-weighted MR and CT image sequences. Methods: The proposedInter-Slice Bidirectional Registration-based Segmentation (iBRS) algorithm relies on interslice image registration of volume data to segment the prostate gland without the use of an anatomical atlas. It requires the user to mark only three slices in a given volume dataset, i.e., the first, middle, and last slices. Next, the proposed algorithm uses a registration algorithm to autosegment the remaining slices. We conducted comprehensive experiments to measure the performance of the proposed algorithm using three registration methods (i.e., rigid, affine, and nonrigid techniques). Results: The results with the proposed technique were compared with manual marking using prostate MR and CT images from 117 patients. Manual marking was performed by an expert user for all 117 patients. The median accuracies for individual slices measured using the Dice similarity coefficient (DSC) were 92% and 91% for MR and CT images, respectively. The iBRS algorithm was also evaluated regarding user variability, which confirmed that the algorithm was robust to interuser variability when marking the prostate gland. Conclusions: The proposed algorithm exploits the interslice data redundancy of the images in a volume dataset of MR and CT images and eliminates the need for an atlas, minimizing the computational cost while producing highly accurate results which are robust to interuser variability.

Inter-slice bidirectional registration-based segmentation of the prostate gland in MR and CT image sequences

International Nuclear Information System (INIS)

Khalvati, Farzad; Tizhoosh, Hamid R.; Salmanpour, Aryan; Rahnamayan, Shahryar; Rodrigues, George

2013-01-01

Purpose: Accurate segmentation and volume estimation of the prostate gland in magnetic resonance (MR) and computed tomography (CT) images are necessary steps in diagnosis, treatment, and monitoring of prostate cancer. This paper presents an algorithm for the prostate gland volume estimation based on the semiautomated segmentation of individual slices in T2-weighted MR and CT image sequences. Methods: The proposedInter-Slice Bidirectional Registration-based Segmentation (iBRS) algorithm relies on interslice image registration of volume data to segment the prostate gland without the use of an anatomical atlas. It requires the user to mark only three slices in a given volume dataset, i.e., the first, middle, and last slices. Next, the proposed algorithm uses a registration algorithm to autosegment the remaining slices. We conducted comprehensive experiments to measure the performance of the proposed algorithm using three registration methods (i.e., rigid, affine, and nonrigid techniques). Results: The results with the proposed technique were compared with manual marking using prostate MR and CT images from 117 patients. Manual marking was performed by an expert user for all 117 patients. The median accuracies for individual slices measured using the Dice similarity coefficient (DSC) were 92% and 91% for MR and CT images, respectively. The iBRS algorithm was also evaluated regarding user variability, which confirmed that the algorithm was robust to interuser variability when marking the prostate gland. Conclusions: The proposed algorithm exploits the interslice data redundancy of the images in a volume dataset of MR and CT images and eliminates the need for an atlas, minimizing the computational cost while producing highly accurate results which are robust to interuser variability

Efficient Variational Approaches for Deformable Registration of Images

Directory of Open Access Journals (Sweden)

Mehmet Ali Akinlar

2012-01-01

Full Text Available Dirichlet, anisotropic, and Huber regularization terms are presented for efficient registration of deformable images. Image registration, an ill-posed optimization problem, is solved using a gradient-descent-based method and some fundamental theorems in calculus of variations. Euler-Lagrange equations with homogeneous Neumann boundary conditions are obtained. These equations are discretized by multigrid and finite difference numerical techniques. The method is applied to the registration of brain MR images of size 65×65. Computational results indicate that the presented method is quite fast and efficient in the registration of deformable medical images.

Image registration in gastric emptying studies

International Nuclear Information System (INIS)

Shuter, B.; Cooper, R.G.

1998-01-01

Full text: We have previously shown that image registration, based upon a two-dimensional cross-correlation (CC) of logarithmic Laplacian images (LLI), corrected motion in biliary studies in up to 90% of cases with minimal artifact. We have now applied the same technique to gastric emptying studies (GES). GES were acquired on an LFOV gamma camera over a two-hour period as 20-26 pairs of anterior-posterior frames (30 second duration and 64 x 64 matrix) for both solid and liquid components. All images were manually registered so that the solid contents of the stomach lay within an operator-drawn ROI. The anterior images of the solid component for 30 randomly selected patients were subjected to further image registration using CC of LLI, CC of raw images (Rl) (a common approach to image registration) and CC of Laplacian images (Ll). All images were aligned to the third image of the study, on which an ROI was drawn to outline the stomach. The number of images in which stomach counts appeared outside this ROI were tallied, in the original and all re-registered studies. Maximum displacements in X/Y position between images of studies registered by the LLI and Rl methods were also computed to directly compare positional accuracy. Stomachs partially exceeded the limits of the ROI in 27, 9, 53 and 54 frames (total of 710) in the original, LLI, Rl and Ll studies respectively. There were 4, 1, 6 and 7 studies with misregistered stomachs on more than 2 frames. Frames in seven Rl studies differed from the LLI studies in ) X/Y position by 3 pixels or more. Cross-correlation using LLI was the only method which improved upon the original manual registration. The Rl and Ll methods increased the number of misregistered frames. We conclude that in gastric emptying studies, as in biliary studies, object tracking by CC of LLI is the method of choice for image registration

Deformable image registration using convolutional neural networks

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Eppenhof, Koen A. J.; Lafarge, Maxime W.; Moeskops, Pim; Veta, Mitko; Pluim, Josien P. W.

2018-03-01

Deformable image registration can be time-consuming and often needs extensive parameterization to perform well on a specific application. We present a step towards a registration framework based on a three-dimensional convolutional neural network. The network directly learns transformations between pairs of three-dimensional images. The outputs of the network are three maps for the x, y, and z components of a thin plate spline transformation grid. The network is trained on synthetic random transformations, which are applied to a small set of representative images for the desired application. Training therefore does not require manually annotated ground truth deformation information. The methodology is demonstrated on public data sets of inspiration-expiration lung CT image pairs, which come with annotated corresponding landmarks for evaluation of the registration accuracy. Advantages of this methodology are its fast registration times and its minimal parameterization.

Subspace-Based Holistic Registration for Low-Resolution Facial Images

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Boom BJ

2010-01-01

Full Text Available Subspace-based holistic registration is introduced as an alternative to landmark-based face registration, which has a poor performance on low-resolution images, as obtained in camera surveillance applications. The proposed registration method finds the alignment by maximizing the similarity score between a probe and a gallery image. We use a novel probabilistic framework for both user-independent as well as user-specific face registration. The similarity is calculated using the probability that the face image is correctly aligned in a face subspace, but additionally we take the probability into account that the face is misaligned based on the residual error in the dimensions perpendicular to the face subspace. We perform extensive experiments on the FRGCv2 database to evaluate the impact that the face registration methods have on face recognition. Subspace-based holistic registration on low-resolution images can improve face recognition in comparison with landmark-based registration on high-resolution images. The performance of the tested face recognition methods after subspace-based holistic registration on a low-resolution version of the FRGC database is similar to that after manual registration.

Registration accuracy and quality of real-life images.

Directory of Open Access Journals (Sweden)

Wei-Yen Hsu

Full Text Available BACKGROUND: A common registration problem for the application of consumer device is to align all the acquired image sequences into a complete scene. Image alignment requires a registration algorithm that will compensate as much as possible for geometric variability among images. However, images captured views from a real scene usually produce different distortions. Some are derived from the optic characteristics of image sensors, and others are caused by the specific scenes and objects. METHODOLOGY/PRINCIPAL FINDINGS: An image registration algorithm considering the perspective projection is proposed for the application of consumer devices in this study. It exploits a multiresolution wavelet-based method to extract significant features. An analytic differential approach is then proposed to achieve fast convergence of point matching. Finally, the registration accuracy is further refined to obtain subpixel precision by a feature-based modified Levenberg-Marquardt method. Due to its feature-based and nonlinear characteristic, it converges considerably faster than most other methods. In addition, vignette compensation and color difference adjustment are also performed to further improve the quality of registration results. CONCLUSIONS/SIGNIFICANCE: The performance of the proposed method is evaluated by testing the synthetic and real images acquired by a hand-held digital still camera and in comparison with two registration techniques in terms of the squared sum of intensity differences (SSD and correlation coefficient (CC. The results indicate that the proposed method is promising in registration accuracy and quality, which are statistically significantly better than other two approaches.

Validation of TMJ osteoarthritis synthetic defect database via non-rigid registration

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Paniagua, Beatriz; Pera, Juliette; Budin, Francois; Gomes, Liliane; Styner, Martin; Lucia, Cevidanes; Nguyen, Tung

2015-03-01

Temporomandibular joint (TMJ) disorders are a group of conditions that cause pain and dysfunction in the jaw joint and the muscles controlling jaw movement. However, diagnosis and treatment of these conditions remain controversial. To date, there is no single sign, symptom, or test that can clearly diagnose early stages of osteoarthritis (OA). Instead, the diagnosis is based on a consideration of several factors, including radiological evaluation. The current radiological diagnosis scores of TMJ pathology are subject to misdiagnosis. We believe these scores are limited by the acquisition procedures, such as oblique cuts of the CT and head positioning errors, and can lead to incorrect diagnoses of flattening of the head of the condyle, formation of osteophytes, or condylar pitting. This study consists of creating and validating a methodological framework to simulate defects in CBCT scans of known location and size, in order to create synthetic TMJ OA database. User-generated defects were created using a non-rigid deformation protocol in CBCT. All segmentation evaluation, surface distances and linear distances from the user-generated to the simulated defects showed our methodological framework to be very precise and within a voxel (0.5 mm) of magnitude. A TMJ OA synthetic database will be created next, and evaluated by expert radiologists, and this will serve to evaluate how sensitive the current radiological diagnosis tools are.

Nonrigid registration with tissue-dependent filtering of the deformation field

International Nuclear Information System (INIS)

Staring, Marius; Klein, Stefan; Pluim, Josien P W

2007-01-01

In present-day medical practice it is often necessary to nonrigidly align image data. Current registration algorithms do not generally take the characteristics of tissue into account. Consequently, rigid tissue, such as bone, can be deformed elastically, growth of tumours may be concealed, and contrast-enhanced structures may be reduced in volume. We propose a method to locally adapt the deformation field at structures that must be kept rigid, using a tissue-dependent filtering technique. This adaptive filtering of the deformation field results in locally linear transformations without scaling or shearing. The degree of filtering is related to tissue stiffness: more filtering is applied at stiff tissue locations, less at parts of the image containing nonrigid tissue. The tissue-dependent filter is incorporated in a commonly used registration algorithm, using mutual information as a similarity measure and cubic B-splines to model the deformation field. The new registration algorithm is compared with this popular method. Evaluation of the proposed tissue-dependent filtering is performed on 3D computed tomography (CT) data of the thorax and on 2D digital subtraction angiography (DSA) images. The results show that tissue-dependent filtering of the deformation field leads to improved registration results: tumour volumes and vessel widths are preserved rather than affected

Markerless registration for image guided surgery. Preoperative image, intraoperative video image, and patient

International Nuclear Information System (INIS)

Kihara, Tomohiko; Tanaka, Yuko

1998-01-01

Real-time and volumetric acquisition of X-ray CT, MR, and SPECT is the latest trend of the medical imaging devices. A clinical challenge is to use these multi-modality volumetric information complementary on patient in the entire diagnostic and surgical processes. The intraoperative image and patient integration intents to establish a common reference frame by image in diagnostic and surgical processes. This provides a quantitative measure during surgery, for which we have been relied mostly on doctors' skills and experiences. The intraoperative image and patient integration involves various technologies, however, we think one of the most important elements is the development of markerless registration, which should be efficient and applicable to the preoperative multi-modality data sets, intraoperative image, and patient. We developed a registration system which integrates preoperative multi-modality images, intraoperative video image, and patient. It consists of a real-time registration of video camera for intraoperative use, a markerless surface sampling matching of patient and image, our previous works of markerless multi-modality image registration of X-ray CT, MR, and SPECT, and an image synthesis on video image. We think these techniques can be used in many applications which involve video camera like devices such as video camera, microscope, and image Intensifier. (author)

[Multimodal medical image registration using cubic spline interpolation method].

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He, Yuanlie; Tian, Lianfang; Chen, Ping; Wang, Lifei; Ye, Guangchun; Mao, Zongyuan

2007-12-01

Based on the characteristic of the PET-CT multimodal image series, a novel image registration and fusion method is proposed, in which the cubic spline interpolation method is applied to realize the interpolation of PET-CT image series, then registration is carried out by using mutual information algorithm and finally the improved principal component analysis method is used for the fusion of PET-CT multimodal images to enhance the visual effect of PET image, thus satisfied registration and fusion results are obtained. The cubic spline interpolation method is used for reconstruction to restore the missed information between image slices, which can compensate for the shortage of previous registration methods, improve the accuracy of the registration, and make the fused multimodal images more similar to the real image. Finally, the cubic spline interpolation method has been successfully applied in developing 3D-CRT (3D Conformal Radiation Therapy) system.

A strategy for multimodal deformable image registration to integrate PET/MR into radiotherapy treatment planning

International Nuclear Information System (INIS)

Leibfarth, Sara; Moennich, David; Thorwarth, Daniela; Welz, Stefan; Siegel, Christine; Zips, Daniel; Schwenzer, Nina; Holger Schmidt, Holger

2013-01-01

Background: Combined positron emission tomography (PET)/magnetic resonance imaging (MRI) is highly promising for biologically individualized radiotherapy (RT). Hence, the purpose of this work was to develop an accurate and robust registration strategy to integrate combined PET/MR data into RT treatment planning. Material and methods: Eight patient datasets consisting of an FDG PET/computed tomography (CT) and a subsequently acquired PET/MR of the head and neck (HN) region were available. Registration strategies were developed based on CT and MR data only, whereas the PET components were fused with the resulting deformation field. Following a rigid registration, deformable registration was performed with a transform parametrized by B-splines. Three different optimization metrics were investigated: global mutual information (GMI), GMI combined with a bending energy penalty (BEP) for regularization (GMI + BEP) and localized mutual information with BEP (LMI + BEP). Different quantitative registration quality measures were developed, including volumetric overlap and mean distance measures for structures segmented on CT and MR as well as anatomical landmark distances. Moreover, the local registration quality in the tumor region was assessed by the normalized cross correlation (NCC) of the two PET datasets. Results: LMI + BEP yielded the most robust and accurate registration results. For GMI, GMI + BEP and LMI + BEP, mean landmark distances (standard deviations) were 23.9 mm (15.5 mm), 4.8 mm (4.0 mm) and 3.0 mm (1.0 mm), and mean NCC values (standard deviations) were 0.29 (0.29), 0.84 (0.14) and 0.88 (0.06), respectively. Conclusion: Accurate and robust multimodal deformable image registration of CT and MR in the HN region can be performed using a B-spline parametrized transform and LMI + BEP as optimization metric. With this strategy, biologically individualized RT based on combined PET/MRI in terms of dose painting is possible

Registration of DRRs and portal images for verification of stereotactic body radiotherapy: a feasibility study in lung cancer treatment

International Nuclear Information System (INIS)

Kuenzler, Thomas; Grezdo, Jozef; Bogner, Joachim; Birkfellner, Wolfgang; Georg, Dietmar

2007-01-01

Image guidance has become a pre-requisite for hypofractionated radiotherapy where the applied dose per fraction is increased. Particularly in stereotactic body radiotherapy (SBRT) for lung tumours, one has to account for set-up errors and intrafraction tumour motion. In our feasibility study, we compared digitally reconstructed radiographs (DRRs) of lung lesions with MV portal images (PIs) to obtain the displacement of the tumour before irradiation. The verification of the tumour position was performed by rigid intensity based registration and three different merit functions such as the sum of squared pixel intensity differences, normalized cross correlation and normalized mutual information. The registration process then provided a translation vector that defines the displacement of the target in order to align the tumour with the isocentre. To evaluate the registration algorithms, 163 test images were created and subsequently, a lung phantom containing an 8 cm 3 tumour was built. In a further step, the registration process was applied on patient data, containing 38 tumours in 113 fractions. To potentially improve registration outcome, two filter types (histogram equalization and display equalization) were applied and their impact on the registration process was evaluated. Generated test images showed an increase in successful registrations when applying a histogram equalization filter whereas the lung phantom study proved the accuracy of the selected algorithms, i.e. deviations of the calculated translation vector for all test algorithms were below 1 mm. For clinical patient data, successful registrations occurred in about 59% of anterior-posterior (AP) and 46% of lateral projections, respectively. When patients with a clinical target volume smaller than 10 cm 3 were excluded, successful registrations go up to 90% in AP and 50% in lateral projection. In addition, a reliable identification of the tumour position was found to be difficult for clinical target

Registration of DRRs and portal images for verification of stereotactic body radiotherapy: a feasibility study in lung cancer treatment

Energy Technology Data Exchange (ETDEWEB)

Kuenzler, Thomas [Department of Radiotherapy and Radiobiology, Medical University Vienna, Vienna (Austria); Grezdo, Jozef [Department of Radiotherapy, St Elisabeth Institute of Oncology, Bratislava (Slovakia); Bogner, Joachim [Department of Radiotherapy and Radiobiology, Medical University Vienna, Vienna (Austria); Birkfellner, Wolfgang [Center for Biomedical Engineering and Physics, Medical University Vienna, Vienna (Austria); Georg, Dietmar [Department of Radiotherapy and Radiobiology, Medical University Vienna, Vienna (Austria)

2007-04-21

Image guidance has become a pre-requisite for hypofractionated radiotherapy where the applied dose per fraction is increased. Particularly in stereotactic body radiotherapy (SBRT) for lung tumours, one has to account for set-up errors and intrafraction tumour motion. In our feasibility study, we compared digitally reconstructed radiographs (DRRs) of lung lesions with MV portal images (PIs) to obtain the displacement of the tumour before irradiation. The verification of the tumour position was performed by rigid intensity based registration and three different merit functions such as the sum of squared pixel intensity differences, normalized cross correlation and normalized mutual information. The registration process then provided a translation vector that defines the displacement of the target in order to align the tumour with the isocentre. To evaluate the registration algorithms, 163 test images were created and subsequently, a lung phantom containing an 8 cm{sup 3} tumour was built. In a further step, the registration process was applied on patient data, containing 38 tumours in 113 fractions. To potentially improve registration outcome, two filter types (histogram equalization and display equalization) were applied and their impact on the registration process was evaluated. Generated test images showed an increase in successful registrations when applying a histogram equalization filter whereas the lung phantom study proved the accuracy of the selected algorithms, i.e. deviations of the calculated translation vector for all test algorithms were below 1 mm. For clinical patient data, successful registrations occurred in about 59% of anterior-posterior (AP) and 46% of lateral projections, respectively. When patients with a clinical target volume smaller than 10 cm{sup 3} were excluded, successful registrations go up to 90% in AP and 50% in lateral projection. In addition, a reliable identification of the tumour position was found to be difficult for clinical

Accuracy and Utility of Deformable Image Registration in 68Ga 4D PET/CT Assessment of Pulmonary Perfusion Changes During and After Lung Radiation Therapy

International Nuclear Information System (INIS)

Hardcastle, Nicholas; Hofman, Michael S.; Hicks, Rodney J.; Callahan, Jason; Kron, Tomas; MacManus, Michael P.; Ball, David L.; Jackson, Price; Siva, Shankar

2015-01-01

Purpose: Measuring changes in lung perfusion resulting from radiation therapy dose requires registration of the functional imaging to the radiation therapy treatment planning scan. This study investigates registration accuracy and utility for positron emission tomography (PET)/computed tomography (CT) perfusion imaging in radiation therapy for non–small cell lung cancer. Methods: 68 Ga 4-dimensional PET/CT ventilation-perfusion imaging was performed before, during, and after radiation therapy for 5 patients. Rigid registration and deformable image registration (DIR) using B-splines and Demons algorithms was performed with the CT data to obtain a deformation map between the functional images and planning CT. Contour propagation accuracy and correspondence of anatomic features were used to assess registration accuracy. Wilcoxon signed-rank test was used to determine statistical significance. Changes in lung perfusion resulting from radiation therapy dose were calculated for each registration method for each patient and averaged over all patients. Results: With B-splines/Demons DIR, median distance to agreement between lung contours reduced modestly by 0.9/1.1 mm, 1.3/1.6 mm, and 1.3/1.6 mm for pretreatment, midtreatment, and posttreatment (P<.01 for all), and median Dice score between lung contours improved by 0.04/0.04, 0.05/0.05, and 0.05/0.05 for pretreatment, midtreatment, and posttreatment (P<.001 for all). Distance between anatomic features reduced with DIR by median 2.5 mm and 2.8 for pretreatment and midtreatment time points, respectively (P=.001) and 1.4 mm for posttreatment (P>.2). Poorer posttreatment results were likely caused by posttreatment pneumonitis and tumor regression. Up to 80% standardized uptake value loss in perfusion scans was observed. There was limited change in the loss in lung perfusion between registration methods; however, Demons resulted in larger interpatient variation compared with rigid and B-splines registration. Conclusions

A NEW IMAGE REGISTRATION METHOD FOR GREY IMAGES

Institute of Scientific and Technical Information of China (English)

Nie Xuan; Zhao Rongchun; Jiang Zetao

2004-01-01

The proposed algorithm relies on a group of new formulas for calculating tangent slope so as to address angle feature of edge curves of image. It can utilize tangent angle features to estimate automatically and fully the rotation parameters of geometric transform and enable rough matching of images with huge rotation difference. After angle compensation, it can search for matching point sets by correlation criterion, then calculate parameters of affine transform, enable higher-precision emendation of rotation and transferring. Finally, it fulfills precise matching for images with relax-tense iteration method. Compared with the registration approach based on wavelet direction-angle features, the matching algorithm with tangent feature of image edge is more robust and realizes precise registration of various images. Furthermore, it is also helpful in graphics matching.

elastix: a toolbox for intensity-based medical image registration.

Science.gov (United States)

Klein, Stefan; Staring, Marius; Murphy, Keelin; Viergever, Max A; Pluim, Josien P W

2010-01-01

Medical image registration is an important task in medical image processing. It refers to the process of aligning data sets, possibly from different modalities (e.g., magnetic resonance and computed tomography), different time points (e.g., follow-up scans), and/or different subjects (in case of population studies). A large number of methods for image registration are described in the literature. Unfortunately, there is not one method that works for all applications. We have therefore developed elastix, a publicly available computer program for intensity-based medical image registration. The software consists of a collection of algorithms that are commonly used to solve medical image registration problems. The modular design of elastix allows the user to quickly configure, test, and compare different registration methods for a specific application. The command-line interface enables automated processing of large numbers of data sets, by means of scripting. The usage of elastix for comparing different registration methods is illustrated with three example experiments, in which individual components of the registration method are varied.

Learning-Based Approaches to Deformable Image Registration

NARCIS (Netherlands)

Münzing, SEA

2014-01-01

Accurate registration of images is an important and often crucial step in many areas of image processing and analysis, yet it is only used in a small percentage of possible applications. Automated registration methods are not considered to be sufficiently robust to handle complex deformations and

List-mode-based reconstruction for respiratory motion correction in PET using non-rigid body transformations

International Nuclear Information System (INIS)

Lamare, F; Carbayo, M J Ledesma; Cresson, T; Kontaxakis, G; Santos, A; Rest, C Cheze Le; Reader, A J; Visvikis, D

2007-01-01

Respiratory motion in emission tomography leads to reduced image quality. Developed correction methodology has been concentrating on the use of respiratory synchronized acquisitions leading to gated frames. Such frames, however, are of low signal-to-noise ratio as a result of containing reduced statistics. In this work, we describe the implementation of an elastic transformation within a list-mode-based reconstruction for the correction of respiratory motion over the thorax, allowing the use of all data available throughout a respiratory motion average acquisition. The developed algorithm was evaluated using datasets of the NCAT phantom generated at different points throughout the respiratory cycle. List-mode-data-based PET-simulated frames were subsequently produced by combining the NCAT datasets with Monte Carlo simulation. A non-rigid registration algorithm based on B-spline basis functions was employed to derive transformation parameters accounting for the respiratory motion using the NCAT dynamic CT images. The displacement matrices derived were subsequently applied during the image reconstruction of the original emission list mode data. Two different implementations for the incorporation of the elastic transformations within the one-pass list mode EM (OPL-EM) algorithm were developed and evaluated. The corrected images were compared with those produced using an affine transformation of list mode data prior to reconstruction, as well as with uncorrected respiratory motion average images. Results demonstrate that although both correction techniques considered lead to significant improvements in accounting for respiratory motion artefacts in the lung fields, the elastic-transformation-based correction leads to a more uniform improvement across the lungs for different lesion sizes and locations

Deformable image registration for image guided prostate radiotherapy

International Nuclear Information System (INIS)

Cassetta, Roberto; Riboldi, Marco; Baroni, Guido; Leandro, Kleber; Novaes, Paulo Eduardo; Goncalves, Vinicius; Sakuraba, Roberto; Fattori, Giovanni

2016-01-01

In this study, we present a CT to CBCT deformable registration method based on the ITK library. An algorithm was developed in order to explore the soft tissue information of the CT-CBCT images to perform deformable image registration (DIR), making efforts to overcome the poor signal-to-noise ratio and HU calibration issues that limits CBCT use for treatment planning purposes. Warped CT images and contours were generated and their impact in adaptive radiotherapy was evaluated by DVH analysis for photon and proton treatments. Considerable discrepancies, related to the treatment planning dose distribution, might be found due to changes in patient’s anatomy. (author)

Image Segmentation, Registration, Compression, and Matching

Science.gov (United States)

Yadegar, Jacob; Wei, Hai; Yadegar, Joseph; Ray, Nilanjan; Zabuawala, Sakina

2011-01-01

A novel computational framework was developed of a 2D affine invariant matching exploiting a parameter space. Named as affine invariant parameter space (AIPS), the technique can be applied to many image-processing and computer-vision problems, including image registration, template matching, and object tracking from image sequence. The AIPS is formed by the parameters in an affine combination of a set of feature points in the image plane. In cases where the entire image can be assumed to have undergone a single affine transformation, the new AIPS match metric and matching framework becomes very effective (compared with the state-of-the-art methods at the time of this reporting). No knowledge about scaling or any other transformation parameters need to be known a priori to apply the AIPS framework. An automated suite of software tools has been created to provide accurate image segmentation (for data cleaning) and high-quality 2D image and 3D surface registration (for fusing multi-resolution terrain, image, and map data). These tools are capable of supporting existing GIS toolkits already in the marketplace, and will also be usable in a stand-alone fashion. The toolkit applies novel algorithmic approaches for image segmentation, feature extraction, and registration of 2D imagery and 3D surface data, which supports first-pass, batched, fully automatic feature extraction (for segmentation), and registration. A hierarchical and adaptive approach is taken for achieving automatic feature extraction, segmentation, and registration. Surface registration is the process of aligning two (or more) data sets to a common coordinate system, during which the transformation between their different coordinate systems is determined. Also developed here are a novel, volumetric surface modeling and compression technique that provide both quality-guaranteed mesh surface approximations and compaction of the model sizes by efficiently coding the geometry and connectivity

Fast parallel image registration on CPU and GPU for diagnostic classification of Alzheimer's disease.

Science.gov (United States)

Shamonin, Denis P; Bron, Esther E; Lelieveldt, Boudewijn P F; Smits, Marion; Klein, Stefan; Staring, Marius

2013-01-01

Nonrigid image registration is an important, but time-consuming task in medical image analysis. In typical neuroimaging studies, multiple image registrations are performed, i.e., for atlas-based segmentation or template construction. Faster image registration routines would therefore be beneficial. In this paper we explore acceleration of the image registration package elastix by a combination of several techniques: (i) parallelization on the CPU, to speed up the cost function derivative calculation; (ii) parallelization on the GPU building on and extending the OpenCL framework from ITKv4, to speed up the Gaussian pyramid computation and the image resampling step; (iii) exploitation of certain properties of the B-spline transformation model; (iv) further software optimizations. The accelerated registration tool is employed in a study on diagnostic classification of Alzheimer's disease and cognitively normal controls based on T1-weighted MRI. We selected 299 participants from the publicly available Alzheimer's Disease Neuroimaging Initiative database. Classification is performed with a support vector machine based on gray matter volumes as a marker for atrophy. We evaluated two types of strategies (voxel-wise and region-wise) that heavily rely on nonrigid image registration. Parallelization and optimization resulted in an acceleration factor of 4-5x on an 8-core machine. Using OpenCL a speedup factor of 2 was realized for computation of the Gaussian pyramids, and 15-60 for the resampling step, for larger images. The voxel-wise and the region-wise classification methods had an area under the receiver operator characteristic curve of 88 and 90%, respectively, both for standard and accelerated registration. We conclude that the image registration package elastix was substantially accelerated, with nearly identical results to the non-optimized version. The new functionality will become available in the next release of elastix as open source under the BSD license.

A multicore based parallel image registration method.

Science.gov (United States)

Yang, Lin; Gong, Leiguang; Zhang, Hong; Nosher, John L; Foran, David J

2009-01-01

Image registration is a crucial step for many image-assisted clinical applications such as surgery planning and treatment evaluation. In this paper we proposed a landmark based nonlinear image registration algorithm for matching 2D image pairs. The algorithm was shown to be effective and robust under conditions of large deformations. In landmark based registration, the most important step is establishing the correspondence among the selected landmark points. This usually requires an extensive search which is often computationally expensive. We introduced a nonregular data partition algorithm using the K-means clustering algorithm to group the landmarks based on the number of available processing cores. The step optimizes the memory usage and data transfer. We have tested our method using IBM Cell Broadband Engine (Cell/B.E.) platform.

Biomedical Image Registration

DEFF Research Database (Denmark)

This book constitutes the refereed proceedings of the 8th International Workshop on Biomedical Image Registration, WBIR 2018, held in Leiden, The Netherlands, in June 2018. The 11 full and poster papers included in this volume were carefully reviewed and selected from 17 submitted papers. The pap...

Image registration assessment in radiotherapy image guidance based on control chart monitoring.

Science.gov (United States)

Xia, Wenyao; Breen, Stephen L

2018-04-01

Image guidance with cone beam computed tomography in radiotherapy can guarantee the precision and accuracy of patient positioning prior to treatment delivery. During the image guidance process, operators need to take great effort to evaluate the image guidance quality before correcting a patient's position. This work proposes an image registration assessment method based on control chart monitoring to reduce the effort taken by the operator. According to the control chart plotted by daily registration scores of each patient, the proposed method can quickly detect both alignment errors and image quality inconsistency. Therefore, the proposed method can provide a clear guideline for the operators to identify unacceptable image quality and unacceptable image registration with minimal effort. Experimental results demonstrate that by using control charts from a clinical database of 10 patients undergoing prostate radiotherapy, the proposed method can quickly identify out-of-control signals and find special cause of out-of-control registration events.

Feature-Based Retinal Image Registration Using D-Saddle Feature

Directory of Open Access Journals (Sweden)

Roziana Ramli

2017-01-01

Full Text Available Retinal image registration is important to assist diagnosis and monitor retinal diseases, such as diabetic retinopathy and glaucoma. However, registering retinal images for various registration applications requires the detection and distribution of feature points on the low-quality region that consists of vessels of varying contrast and sizes. A recent feature detector known as Saddle detects feature points on vessels that are poorly distributed and densely positioned on strong contrast vessels. Therefore, we propose a multiresolution difference of Gaussian pyramid with Saddle detector (D-Saddle to detect feature points on the low-quality region that consists of vessels with varying contrast and sizes. D-Saddle is tested on Fundus Image Registration (FIRE Dataset that consists of 134 retinal image pairs. Experimental results show that D-Saddle successfully registered 43% of retinal image pairs with average registration accuracy of 2.329 pixels while a lower success rate is observed in other four state-of-the-art retinal image registration methods GDB-ICP (28%, Harris-PIIFD (4%, H-M (16%, and Saddle (16%. Furthermore, the registration accuracy of D-Saddle has the weakest correlation (Spearman with the intensity uniformity metric among all methods. Finally, the paired t-test shows that D-Saddle significantly improved the overall registration accuracy of the original Saddle.

Automated analysis of small animal PET studies through deformable registration to an atlas

NARCIS (Netherlands)

Gutierrez, Daniel F.; Zaidi, Habib

This work aims to develop a methodology for automated atlas-guided analysis of small animal positron emission tomography (PET) data through deformable registration to an anatomical mouse model. A non-rigid registration technique is used to put into correspondence relevant anatomical regions of

Automated image registration for FDOPA PET studies

International Nuclear Information System (INIS)

Kang-Ping Lin; Sung-Cheng Huang, Dan-Chu Yu; Melega, W.; Barrio, J.R.; Phelps, M.E.

1996-01-01

In this study, various image registration methods are investigated for their suitability for registration of L-6-[18F]-fluoro-DOPA (FDOPA) PET images. Five different optimization criteria including sum of absolute difference (SAD), mean square difference (MSD), cross-correlation coefficient (CC), standard deviation of pixel ratio (SDPR), and stochastic sign change (SSC) were implemented and Powell's algorithm was used to optimize the criteria. The optimization criteria were calculated either unidirectionally (i.e. only evaluating the criteria for comparing the resliced image 1 with the original image 2) or bidirectionally (i.e. averaging the criteria for comparing the resliced image 1 with the original image 2 and those for the sliced image 2 with the original image 1). Monkey FDOPA images taken at various known orientations were used to evaluate the accuracy of different methods. A set of human FDOPA dynamic images was used to investigate the ability of the methods for correcting subject movement. It was found that a large improvement in performance resulted when bidirectional rather than unidirectional criteria were used. Overall, the SAD, MSD and SDPR methods were found to be comparable in performance and were suitable for registering FDOPA images. The MSD method gave more adequate results for frame-to-frame image registration for correcting subject movement during a dynamic FDOPA study. The utility of the registration method is further demonstrated by registering FDOPA images in monkeys before and after amphetamine injection to reveal more clearly the changes in spatial distribution of FDOPA due to the drug intervention. (author)

Gaussian Process Interpolation for Uncertainty Estimation in Image Registration

Science.gov (United States)

Wachinger, Christian; Golland, Polina; Reuter, Martin; Wells, William

2014-01-01

Intensity-based image registration requires resampling images on a common grid to evaluate the similarity function. The uncertainty of interpolation varies across the image, depending on the location of resampled points relative to the base grid. We propose to perform Bayesian inference with Gaussian processes, where the covariance matrix of the Gaussian process posterior distribution estimates the uncertainty in interpolation. The Gaussian process replaces a single image with a distribution over images that we integrate into a generative model for registration. Marginalization over resampled images leads to a new similarity measure that includes the uncertainty of the interpolation. We demonstrate that our approach increases the registration accuracy and propose an efficient approximation scheme that enables seamless integration with existing registration methods. PMID:25333127

Accuracy and Utility of Deformable Image Registration in {sup 68}Ga 4D PET/CT Assessment of Pulmonary Perfusion Changes During and After Lung Radiation Therapy

Energy Technology Data Exchange (ETDEWEB)

Hardcastle, Nicholas, E-mail: nick.hardcastle@gmail.com [Department of Physical Sciences, Peter MacCallum Cancer Centre, East Melbourne (Australia); Centre for Medical Radiation Physics, University of Wollongong, Wollongong (Australia); Hofman, Michael S. [Molecular Imaging, Centre for Cancer Imaging, Peter MacCallum Cancer Centre, Melbourne (Australia); Hicks, Rodney J. [Molecular Imaging, Centre for Cancer Imaging, Peter MacCallum Cancer Centre, Melbourne (Australia); Department of Medicine, University of Melbourne, Melbourne (Australia); Callahan, Jason [Molecular Imaging, Centre for Cancer Imaging, Peter MacCallum Cancer Centre, Melbourne (Australia); Kron, Tomas [Department of Medical Imaging and Radiation Sciences, Monash University, Clayton (Australia); The Sir Peter MacCallum Department of Oncology, Melbourne University, Victoria (Australia); MacManus, Michael P.; Ball, David L. [Division of Radiation Oncology and Cancer Imaging, Peter MacCallum Cancer Centre, East Melbourne (Australia); The Sir Peter MacCallum Department of Oncology, University of Melbourne, Melbourne (Australia); Jackson, Price [Department of Physical Sciences, Peter MacCallum Cancer Centre, East Melbourne (Australia); Siva, Shankar [Division of Radiation Oncology and Cancer Imaging, Peter MacCallum Cancer Centre, East Melbourne (Australia)

2015-09-01

Purpose: Measuring changes in lung perfusion resulting from radiation therapy dose requires registration of the functional imaging to the radiation therapy treatment planning scan. This study investigates registration accuracy and utility for positron emission tomography (PET)/computed tomography (CT) perfusion imaging in radiation therapy for non–small cell lung cancer. Methods: {sup 68}Ga 4-dimensional PET/CT ventilation-perfusion imaging was performed before, during, and after radiation therapy for 5 patients. Rigid registration and deformable image registration (DIR) using B-splines and Demons algorithms was performed with the CT data to obtain a deformation map between the functional images and planning CT. Contour propagation accuracy and correspondence of anatomic features were used to assess registration accuracy. Wilcoxon signed-rank test was used to determine statistical significance. Changes in lung perfusion resulting from radiation therapy dose were calculated for each registration method for each patient and averaged over all patients. Results: With B-splines/Demons DIR, median distance to agreement between lung contours reduced modestly by 0.9/1.1 mm, 1.3/1.6 mm, and 1.3/1.6 mm for pretreatment, midtreatment, and posttreatment (P<.01 for all), and median Dice score between lung contours improved by 0.04/0.04, 0.05/0.05, and 0.05/0.05 for pretreatment, midtreatment, and posttreatment (P<.001 for all). Distance between anatomic features reduced with DIR by median 2.5 mm and 2.8 for pretreatment and midtreatment time points, respectively (P=.001) and 1.4 mm for posttreatment (P>.2). Poorer posttreatment results were likely caused by posttreatment pneumonitis and tumor regression. Up to 80% standardized uptake value loss in perfusion scans was observed. There was limited change in the loss in lung perfusion between registration methods; however, Demons resulted in larger interpatient variation compared with rigid and B-splines registration

Avoiding Stair-Step Artifacts in Image Registration for GOES-R Navigation and Registration Assessment

Science.gov (United States)

Grycewicz, Thomas J.; Tan, Bin; Isaacson, Peter J.; De Luccia, Frank J.; Dellomo, John

2016-01-01

In developing software for independent verification and validation (IVV) of the Image Navigation and Registration (INR) capability for the Geostationary Operational Environmental Satellite R Series (GOES-R) Advanced Baseline Imager (ABI), we have encountered an image registration artifact which limits the accuracy of image offset estimation at the subpixel scale using image correlation. Where the two images to be registered have the same pixel size, subpixel image registration preferentially selects registration values where the image pixel boundaries are close to lined up. Because of the shape of a curve plotting input displacement to estimated offset, we call this a stair-step artifact. When one image is at a higher resolution than the other, the stair-step artifact is minimized by correlating at the higher resolution. For validating ABI image navigation, GOES-R images are correlated with Landsat-based ground truth maps. To create the ground truth map, the Landsat image is first transformed to the perspective seen from the GOES-R satellite, and then is scaled to an appropriate pixel size. Minimizing processing time motivates choosing the map pixels to be the same size as the GOES-R pixels. At this pixel size image processing of the shift estimate is efficient, but the stair-step artifact is present. If the map pixel is very small, stair-step is not a problem, but image correlation is computation-intensive. This paper describes simulation-based selection of the scale for truth maps for registering GOES-R ABI images.

3D Registration of mpMRI for Assessment of Prostate Cancer Focal Therapy.

Science.gov (United States)

Orczyk, Clément; Rosenkrantz, Andrew B; Mikheev, Artem; Villers, Arnauld; Bernaudin, Myriam; Taneja, Samir S; Valable, Samuel; Rusinek, Henry

2017-12-01

This study aimed to assess a novel method of three-dimensional (3D) co-registration of prostate magnetic resonance imaging (MRI) examinations performed before and after prostate cancer focal therapy. We developed a software platform for automatic 3D deformable co-registration of prostate MRI at different time points and applied this method to 10 patients who underwent focal ablative therapy. MRI examinations were performed preoperatively, as well as 1 week and 6 months post treatment. Rigid registration served as reference for assessing co-registration accuracy and precision. Segmentation of preoperative and postoperative prostate revealed a significant postoperative volume decrease of the gland that averaged 6.49 cc (P = .017). Applying deformable transformation based on mutual information from 120 pairs of MRI slices, we refined by 2.9 mm (max. 6.25 mm) the alignment of the ablation zone, segmented from contrast-enhanced images on the 1-week postoperative examination, to the 6-month postoperative T2-weighted images. This represented a 500% improvement over the rigid approach (P = .001), corrected by volume. The dissimilarity by Dice index of the mapped ablation zone using deformable transformation vs rigid control was significantly (P = .04) higher at the ablation site than in the whole gland. Our findings illustrate our method's ability to correct for deformation at the ablation site. The preliminary analysis suggests that deformable transformation computed from mutual information of preoperative and follow-up MRI is accurate in co-registration of MRI examinations performed before and after focal therapy. The ability to localize the previously ablated tissue in 3D space may improve targeting for image-guided follow-up biopsy within focal therapy protocols. Copyright © 2017 The Association of University Radiologists. Published by Elsevier Inc. All rights reserved.

Improving the image quality of contrast-enhanced MR angiography by automated image registration: A prospective study in peripheral arterial disease of the lower extremities

International Nuclear Information System (INIS)

Menke, Jan

2010-01-01

Objective: If a patient has moved during digital subtraction angiography (DSA), manual pixel shift can improve the image quality. This study investigated whether such image registration can also improve the quality of contrast-enhanced magnetic resonance angiography (MRA) in patients with peripheral arterial disease of the lower extremities. Materials and methods: 404 leg MRAs of patients likely to have peripheral artery disease were included in this prospective study. The standard non-registered MRAs were compared to automatically linear, affine and warp registered MRAs by four image quality parameters, including the vessel detection probability (VDP) in maximum intensity projection (MIP) images and contrast-to-noise ratios (CNR). The different registration types were compared by analysis of variance. Results: All studied image quality parameters showed similar trends. Generally, registration improved the leg MRA quality significantly (P < 0.05). The 12% of lower legs with a body shift of 1 mm or more showed the highest gain in image quality when using linear registration instead of no registration, with an average VDP gain of 20-49%. Warp registration improved the image quality slightly further. Conclusion: Automated image registration can improve the MRA image quality especially in the lower legs, which is comparable to the effect of pixel shift in DSA.

On the nature of data collection for soft-tissue image-to-physical organ registration: a noise characterization study

Science.gov (United States)

Collins, Jarrod A.; Heiselman, Jon S.; Weis, Jared A.; Clements, Logan W.; Simpson, Amber L.; Jarnagin, William R.; Miga, Michael I.

2017-03-01

In image-guided liver surgery (IGLS), sparse representations of the anterior organ surface may be collected intraoperatively to drive image-to-physical space registration. Soft tissue deformation represents a significant source of error for IGLS techniques. This work investigates the impact of surface data quality on current surface based IGLS registration methods. In this work, we characterize the robustness of our IGLS registration methods to noise in organ surface digitization. We study this within a novel human-to-phantom data framework that allows a rapid evaluation of clinically realistic data and noise patterns on a fully characterized hepatic deformation phantom. Additionally, we implement a surface data resampling strategy that is designed to decrease the impact of differences in surface acquisition. For this analysis, n=5 cases of clinical intraoperative data consisting of organ surface and salient feature digitizations from open liver resection were collected and analyzed within our human-to-phantom validation framework. As expected, results indicate that increasing levels of noise in surface acquisition cause registration fidelity to deteriorate. With respect to rigid registration using the raw and resampled data at clinically realistic levels of noise (i.e. a magnitude of 1.5 mm), resampling improved TRE by 21%. In terms of nonrigid registration, registrations using resampled data outperformed the raw data result by 14% at clinically realistic levels and were less susceptible to noise across the range of noise investigated. These results demonstrate the types of analyses our novel human-to-phantom validation framework can provide and indicate the considerable benefits of resampling strategies.

GPU accelerated generation of digitally reconstructed radiographs for 2-D/3-D image registration.

Science.gov (United States)

Dorgham, Osama M; Laycock, Stephen D; Fisher, Mark H

2012-09-01

Recent advances in programming languages for graphics processing units (GPUs) provide developers with a convenient way of implementing applications which can be executed on the CPU and GPU interchangeably. GPUs are becoming relatively cheap, powerful, and widely available hardware components, which can be used to perform intensive calculations. The last decade of hardware performance developments shows that GPU-based computation is progressing significantly faster than CPU-based computation, particularly if one considers the execution of highly parallelisable algorithms. Future predictions illustrate that this trend is likely to continue. In this paper, we introduce a way of accelerating 2-D/3-D image registration by developing a hybrid system which executes on the CPU and utilizes the GPU for parallelizing the generation of digitally reconstructed radiographs (DRRs). Based on the advancements of the GPU over the CPU, it is timely to exploit the benefits of many-core GPU technology by developing algorithms for DRR generation. Although some previous work has investigated the rendering of DRRs using the GPU, this paper investigates approximations which reduce the computational overhead while still maintaining a quality consistent with that needed for 2-D/3-D registration with sufficient accuracy to be clinically acceptable in certain applications of radiation oncology. Furthermore, by comparing implementations of 2-D/3-D registration on the CPU and GPU, we investigate current performance and propose an optimal framework for PC implementations addressing the rigid registration problem. Using this framework, we are able to render DRR images from a 256×256×133 CT volume in ~24 ms using an NVidia GeForce 8800 GTX and in ~2 ms using NVidia GeForce GTX 580. In addition to applications requiring fast automatic patient setup, these levels of performance suggest image-guided radiation therapy at video frame rates is technically feasible using relatively low cost PC

PCANet-Based Structural Representation for Nonrigid Multimodal Medical Image Registration

Directory of Open Access Journals (Sweden)

Xingxing Zhu

2018-05-01

Full Text Available Nonrigid multimodal image registration remains a challenging task in medical image processing and analysis. The structural representation (SR-based registration methods have attracted much attention recently. However, the existing SR methods cannot provide satisfactory registration accuracy due to the utilization of hand-designed features for structural representation. To address this problem, the structural representation method based on the improved version of the simple deep learning network named PCANet is proposed for medical image registration. In the proposed method, PCANet is firstly trained on numerous medical images to learn convolution kernels for this network. Then, a pair of input medical images to be registered is processed by the learned PCANet. The features extracted by various layers in the PCANet are fused to produce multilevel features. The structural representation images are constructed for two input images based on nonlinear transformation of these multilevel features. The Euclidean distance between structural representation images is calculated and used as the similarity metrics. The objective function defined by the similarity metrics is optimized by L-BFGS method to obtain parameters of the free-form deformation (FFD model. Extensive experiments on simulated and real multimodal image datasets show that compared with the state-of-the-art registration methods, such as modality-independent neighborhood descriptor (MIND, normalized mutual information (NMI, Weber local descriptor (WLD, and the sum of squared differences on entropy images (ESSD, the proposed method provides better registration performance in terms of target registration error (TRE and subjective human vision.

Analysis of Point Based Image Registration Errors With Applications in Single Molecule Microscopy.

Science.gov (United States)

Cohen, E A K; Ober, R J

2013-12-15

We present an asymptotic treatment of errors involved in point-based image registration where control point (CP) localization is subject to heteroscedastic noise; a suitable model for image registration in fluorescence microscopy. Assuming an affine transform, CPs are used to solve a multivariate regression problem. With measurement errors existing for both sets of CPs this is an errors-in-variable problem and linear least squares is inappropriate; the correct method being generalized least squares. To allow for point dependent errors the equivalence of a generalized maximum likelihood and heteroscedastic generalized least squares model is achieved allowing previously published asymptotic results to be extended to image registration. For a particularly useful model of heteroscedastic noise where covariance matrices are scalar multiples of a known matrix (including the case where covariance matrices are multiples of the identity) we provide closed form solutions to estimators and derive their distribution. We consider the target registration error (TRE) and define a new measure called the localization registration error (LRE) believed to be useful, especially in microscopy registration experiments. Assuming Gaussianity of the CP localization errors, it is shown that the asymptotic distribution for the TRE and LRE are themselves Gaussian and the parameterized distributions are derived. Results are successfully applied to registration in single molecule microscopy to derive the key dependence of the TRE and LRE variance on the number of CPs and their associated photon counts. Simulations show asymptotic results are robust for low CP numbers and non-Gaussianity. The method presented here is shown to outperform GLS on real imaging data.

Development of the image registration program for portal and DRR images in radiation therapy

International Nuclear Information System (INIS)

Watanabe, Hiroyuki; Ito, Takeshi; Nakazeko, Kazuma; Tachibana, Atsuhi; Hashimoto, Takeyuki; Shinohara, Hiroyuki

2012-01-01

In this article, the authors propose an image registration program of portal images and digitally reconstructed radiography (DRR) images used as simulation images for external beam radiation therapy planning. First, the center of the radiation field in a portal image taken using a computed radiograhy cassette is matched to the center of the portal image. Then scale points projected on a DRR image and the portal image are deleted, and the portal image with the radiation field is extracted. Registration of the DRR and portal images is performed using mutual information as the registration criterion. It was found that the absolute displacement misregistrations in two directions (x, y) were 1.2±0.7 mm and 0.5±0.3 mm, respectively, and rotation disagreement about the z axis 0.3±0.3deg. It was concluded the proposed method was applicable to image registration of portal and DRR images in radiation therapy. (author)

Image registration method for medical image sequences

Science.gov (United States)

Gee, Timothy F.; Goddard, James S.

2013-03-26

Image registration of low contrast image sequences is provided. In one aspect, a desired region of an image is automatically segmented and only the desired region is registered. Active contours and adaptive thresholding of intensity or edge information may be used to segment the desired regions. A transform function is defined to register the segmented region, and sub-pixel information may be determined using one or more interpolation methods.

The influence of the image registration method on the adaptive radiotherapy. A proof of the principle in a selected case of prostate IMRT.

Science.gov (United States)

Berenguer, Roberto; de la Vara, Victoria; Lopez-Honrubia, Veronica; Nuñez, Ana Teresa; Rivera, Miguel; Villas, Maria Victoria; Sabater, Sebastia

2018-01-01

To analyse the influence of the image registration method on the adaptive radiotherapy of an IMRT prostate treatment, and to compare the dose accumulation according to 3 different image registration methods with the planned dose. The IMRT prostate patient was CT imaged 3 times throughout his treatment. The prostate, PTV, rectum and bladder were segmented on each CT. A Rigid, a deformable (DIR) B-spline and a DIR with landmarks registration algorithms were employed. The difference between the accumulated doses and planned doses were evaluated by the gamma index. The Dice coefficient and Hausdorff distance was used to evaluate the overlap between volumes, to quantify the quality of the registration. When comparing adaptive vs no adaptive RT, the gamma index calculation showed large differences depending on the image registration method (as much as 87.6% in the case of DIR B-spline). The quality of the registration was evaluated using an index such as the Dice coefficient. This showed that the best result was obtained with DIR with landmarks compared with the rest and it was always above 0.77, reported as a recommended minimum value for prostate studies in a multi-centre review. Apart from showing the importance of the application of an adaptive RT protocol in a particular treatment, this work shows that the election of the registration method is decisive in the result of the adaptive radiotherapy and dose accumulation. Copyright © 2017 Associazione Italiana di Fisica Medica. Published by Elsevier Ltd. All rights reserved.

An evaluation of canonical forms for non-rigid 3D shape retrieval

OpenAIRE

Pickup, David; Liu, Juncheng; Sun, Xianfang; Rosin, Paul L.; Martin, Ralph R.; Cheng, Zhiquan; Lian, Zhouhui; Nie, Sipin; Jin, Longcun; Shamai, Gil; Sahillioğlu, Yusuf; Kavan, Ladislav

2018-01-01

Canonical forms attempt to factor out a non-rigid shape’s pose, giving a pose-neutral shape. This opens up the\\ud possibility of using methods originally designed for rigid shape retrieval for the task of non-rigid shape retrieval.\\ud We extend our recent benchmark for testing canonical form algorithms. Our new benchmark is used to evaluate a\\ud greater number of state-of-the-art canonical forms, on five recent non-rigid retrieval datasets, within two different\\ud retrieval frameworks. A tota...

TU-AB-202-07: A Novel Method for Registration of Mid-Treatment PET/CT Images Under Conditions of Tumor Regression for Patients with Locally Advanced Lung Cancers

Energy Technology Data Exchange (ETDEWEB)

Sharifi, Hoda [Department of Radiation Oncology, Henry Ford Health System, Detroit, MI (United States); Department of Physics, Oakland University, Rochester, MI (United States); Zhang, Hong; Jin, Jian-Yyue; Kong, Feng-Ming [Department of Radiation Oncology, GRU Cancer Center, Augusta GA (United States); Chetty, Indrin J [Department of Radiation Oncology, Henry Ford Health System, Detroit, MI (United States); Zhong, Hualiang

2016-06-15

Purpose: In PET-guided adaptive radiotherapy (RT), changes in the metabolic activity at individual voxels cannot be derived until the duringtreatment CT images are appropriately registered to pre-treatment CT images. However, deformable image registration (DIR) usually does not preserve tumor volume. This may induce errors when comparing to the target. The aim of this study was to develop a DIR-integrated mechanical modeling technique to track radiation-induced metabolic changes on PET images. Methods: Three patients with non-small cell lung cancer (NSCLC) were treated with adaptive radiotherapy under RTOG 1106. Two PET/CT image sets were acquired 2 weeks before RT and 18 fractions after the start of treatment. DIR was performed to register the during-RT CT to the pre-RT CT using a B-spline algorithm and the resultant displacements in the region of tumor were remodeled using a hybrid finite element method (FEM). Gross tumor volume (GTV) was delineated on the during-RT PET/CT image sets and deformed using the 3D deformation vector fields generated by the CT-based registrations. Metabolic tumor volume (MTV) was calculated using the pre- and during–RT image set. The quality of the PET mapping was evaluated based on the constancy of the mapped MTV and landmark comparison. Results: The B-spline-based registrations changed MTVs by 7.3%, 4.6% and −5.9% for the 3 patients and the correspondent changes for the hybrid FEM method −2.9%, 1% and 6.3%, respectively. Landmark comparisons were used to evaluate the Rigid, B-Spline, and hybrid FEM registrations with the mean errors of 10.1 ± 1.6 mm, 4.4 ± 0.4 mm, and 3.6 ± 0.4 mm for three patients. The hybrid FEM method outperforms the B-Spline-only registration for patients with tumor regression Conclusion: The hybrid FEM modeling technique improves the B-Spline registrations in tumor regions. This technique may help compare metabolic activities between two PET/CT images with regressing tumors. The author gratefully

Increased Sensitivity to Pathological Brain Changes Using Co-registration of Magnetic Resonance Imaging Scans

Energy Technology Data Exchange (ETDEWEB)

Burdett, J.; Stevens, J.; Flugel, D.; Williams, E.; Duncan, J.S.; Lemieux, L. [National Society for Epilepsy, Chalfont St Peter (United Kingdom). The MRI Unit

2006-12-15

Purpose: To compare automatic software-based co-registration of serial magnetic resonance imaging (MRI) scans with conventional visual comparison, by expert neuroradiologists.Material and Methods: Sixty-four patients who were referred to our epilepsy MRI unit for cerebral imaging were identified as having potentially, non- or slow-growing lesions or cerebral atrophy and followed with sequential scans over a period of up to 8 years, resulting in a total of 92 pairs of scans. Scans were categorized as showing either lesions or atrophy. Each pair of scans was reviewed twice for the presence of change, with and without co-registration, performed using automated software. Results: Co-registration and visual reporting without co-registration were discordant in the lesions group in nine out of 69 datasets (13%), and in 16 out of 23 pairs of scans in the atrophy group (69%). The most common cause of discordance was visual reporting not detecting changes apparent by co-registration. In three cases, changes detected visually were not detected following co-registration. Conclusion: In the group of patients studied, co-registration was more sensitive for detecting changes than visual comparison, particularly with respect to atrophic changes of the brain. With the increasing availability of sophisticated independent consoles attached to MRI scanners that may be used for image co-registration, we propose that serial T1-weighted volumetric MRI brain co-registration should be considered for integration into routine clinical practice to assess patients with suspected progressive disease.

Increased Sensitivity to Pathological Brain Changes Using Co-registration of Magnetic Resonance Imaging Scans

International Nuclear Information System (INIS)

Burdett, J.; Stevens, J.; Flugel, D.; Williams, E.; Duncan, J.S.; Lemieux, L.

2006-01-01

Purpose: To compare automatic software-based co-registration of serial magnetic resonance imaging (MRI) scans with conventional visual comparison, by expert neuroradiologists.Material and Methods: Sixty-four patients who were referred to our epilepsy MRI unit for cerebral imaging were identified as having potentially, non- or slow-growing lesions or cerebral atrophy and followed with sequential scans over a period of up to 8 years, resulting in a total of 92 pairs of scans. Scans were categorized as showing either lesions or atrophy. Each pair of scans was reviewed twice for the presence of change, with and without co-registration, performed using automated software. Results: Co-registration and visual reporting without co-registration were discordant in the lesions group in nine out of 69 datasets (13%), and in 16 out of 23 pairs of scans in the atrophy group (69%). The most common cause of discordance was visual reporting not detecting changes apparent by co-registration. In three cases, changes detected visually were not detected following co-registration. Conclusion: In the group of patients studied, co-registration was more sensitive for detecting changes than visual comparison, particularly with respect to atrophic changes of the brain. With the increasing availability of sophisticated independent consoles attached to MRI scanners that may be used for image co-registration, we propose that serial T1-weighted volumetric MRI brain co-registration should be considered for integration into routine clinical practice to assess patients with suspected progressive disease

3D/3D registration of coronary CTA and biplane XA reconstructions for improved image guidance

Energy Technology Data Exchange (ETDEWEB)

Dibildox, Gerardo, E-mail: g.dibildox@erasmusmc.nl; Baka, Nora; Walsum, Theo van [Biomedical Imaging Group Rotterdam, Departments of Radiology and Medical Informatics, Erasmus Medical Center, 3015 GE Rotterdam (Netherlands); Punt, Mark; Aben, Jean-Paul [Pie Medical Imaging, 6227 AJ Maastricht (Netherlands); Schultz, Carl [Department of Cardiology, Erasmus Medical Center, 3015 GE Rotterdam (Netherlands); Niessen, Wiro [Quantitative Imaging Group, Faculty of Applied Sciences, Delft University of Technology, 2628 CJ Delft, The Netherlands and Biomedical Imaging Group Rotterdam, Departments of Radiology and Medical Informatics, Erasmus Medical Center, 3015 GE Rotterdam (Netherlands)

2014-09-15

Purpose: The authors aim to improve image guidance during percutaneous coronary interventions of chronic total occlusions (CTO) by providing information obtained from computed tomography angiography (CTA) to the cardiac interventionist. To this end, the authors investigate a method to register a 3D CTA model to biplane reconstructions. Methods: The authors developed a method for registering preoperative coronary CTA with intraoperative biplane x-ray angiography (XA) images via 3D models of the coronary arteries. The models are extracted from the CTA and biplane XA images, and are temporally aligned based on CTA reconstruction phase and XA ECG signals. Rigid spatial alignment is achieved with a robust probabilistic point set registration approach using Gaussian mixture models (GMMs). This approach is extended by including orientation in the Gaussian mixtures and by weighting bifurcation points. The method is evaluated on retrospectively acquired coronary CTA datasets of 23 CTO patients for which biplane XA images are available. Results: The Gaussian mixture model approach achieved a median registration accuracy of 1.7 mm. The extended GMM approach including orientation was not significantly different (P > 0.1) but did improve robustness with regards to the initialization of the 3D models. Conclusions: The authors demonstrated that the GMM approach can effectively be applied to register CTA to biplane XA images for the purpose of improving image guidance in percutaneous coronary interventions.

Quicksilver: Fast predictive image registration - A deep learning approach.

Science.gov (United States)

Yang, Xiao; Kwitt, Roland; Styner, Martin; Niethammer, Marc

2017-09-01

This paper introduces Quicksilver, a fast deformable image registration method. Quicksilver registration for image-pairs works by patch-wise prediction of a deformation model based directly on image appearance. A deep encoder-decoder network is used as the prediction model. While the prediction strategy is general, we focus on predictions for the Large Deformation Diffeomorphic Metric Mapping (LDDMM) model. Specifically, we predict the momentum-parameterization of LDDMM, which facilitates a patch-wise prediction strategy while maintaining the theoretical properties of LDDMM, such as guaranteed diffeomorphic mappings for sufficiently strong regularization. We also provide a probabilistic version of our prediction network which can be sampled during the testing time to calculate uncertainties in the predicted deformations. Finally, we introduce a new correction network which greatly increases the prediction accuracy of an already existing prediction network. We show experimental results for uni-modal atlas-to-image as well as uni-/multi-modal image-to-image registrations. These experiments demonstrate that our method accurately predicts registrations obtained by numerical optimization, is very fast, achieves state-of-the-art registration results on four standard validation datasets, and can jointly learn an image similarity measure. Quicksilver is freely available as an open-source software. Copyright © 2017 Elsevier Inc. All rights reserved.

Multimodal image registration based on binary gradient angle descriptor.

Science.gov (United States)

Jiang, Dongsheng; Shi, Yonghong; Yao, Demin; Fan, Yifeng; Wang, Manning; Song, Zhijian

2017-12-01

Multimodal image registration plays an important role in image-guided interventions/therapy and atlas building, and it is still a challenging task due to the complex intensity variations in different modalities. The paper addresses the problem and proposes a simple, compact, fast and generally applicable modality-independent binary gradient angle descriptor (BGA) based on the rationale of gradient orientation alignment. The BGA can be easily calculated at each voxel by coding the quadrant in which a local gradient vector falls, and it has an extremely low computational complexity, requiring only three convolutions, two multiplication operations and two comparison operations. Meanwhile, the binarized encoding of the gradient orientation makes the BGA more resistant to image degradations compared with conventional gradient orientation methods. The BGA can extract similar feature descriptors for different modalities and enable the use of simple similarity measures, which makes it applicable within a wide range of optimization frameworks. The results for pairwise multimodal and monomodal registrations between various images (T1, T2, PD, T1c, Flair) consistently show that the BGA significantly outperforms localized mutual information. The experimental results also confirm that the BGA can be a reliable alternative to the sum of absolute difference in monomodal image registration. The BGA can also achieve an accuracy of [Formula: see text], similar to that of the SSC, for the deformable registration of inhale and exhale CT scans. Specifically, for the highly challenging deformable registration of preoperative MRI and 3D intraoperative ultrasound images, the BGA achieves a similar registration accuracy of [Formula: see text] compared with state-of-the-art approaches, with a computation time of 18.3 s per case. The BGA improves the registration performance in terms of both accuracy and time efficiency. With further acceleration, the framework has the potential for

WE-H-202-04: Advanced Medical Image Registration Techniques

International Nuclear Information System (INIS)

Christensen, G.

2016-01-01

Deformable image registration has now been commercially available for several years, with solid performance in a number of sites and for several applications including contour and dose mapping. However, more complex applications have arisen, such as assessing response to radiation therapy over time, registering images pre- and post-surgery, and auto-segmentation from atlases. These applications require innovative registration algorithms to achieve accurate alignment. The goal of this session is to highlight emerging registration technology and these new applications. The state of the art in image registration will be presented from an engineering perspective. Translational clinical applications will also be discussed to tie these new registration approaches together with imaging and radiation therapy applications in specific diseases such as cervical and lung cancers. Learning Objectives: To understand developing techniques and algorithms in deformable image registration that are likely to translate into clinical tools in the near future. To understand emerging imaging and radiation therapy clinical applications that require such new registration algorithms. Research supported in part by the National Institutes of Health under award numbers P01CA059827, R01CA166119, and R01CA166703. Disclosures: Phillips Medical systems (Hugo), Roger Koch (Christensen) support, Varian Medical Systems (Brock), licensing agreements from Raysearch (Brock) and Varian (Hugo).; K. Brock, Licensing Agreement - RaySearch Laboratories. Research Funding - Varian Medical Systems; G. Hugo, Research grant from National Institutes of Health, award number R01CA166119.; G. Christensen, Research support from NIH grants CA166119 and CA166703 and a gift from Roger Koch. There are no conflicts of interest.

WE-H-202-04: Advanced Medical Image Registration Techniques

Energy Technology Data Exchange (ETDEWEB)

Christensen, G. [University of Iowa (United States)

2016-06-15

Deformable image registration has now been commercially available for several years, with solid performance in a number of sites and for several applications including contour and dose mapping. However, more complex applications have arisen, such as assessing response to radiation therapy over time, registering images pre- and post-surgery, and auto-segmentation from atlases. These applications require innovative registration algorithms to achieve accurate alignment. The goal of this session is to highlight emerging registration technology and these new applications. The state of the art in image registration will be presented from an engineering perspective. Translational clinical applications will also be discussed to tie these new registration approaches together with imaging and radiation therapy applications in specific diseases such as cervical and lung cancers. Learning Objectives: To understand developing techniques and algorithms in deformable image registration that are likely to translate into clinical tools in the near future. To understand emerging imaging and radiation therapy clinical applications that require such new registration algorithms. Research supported in part by the National Institutes of Health under award numbers P01CA059827, R01CA166119, and R01CA166703. Disclosures: Phillips Medical systems (Hugo), Roger Koch (Christensen) support, Varian Medical Systems (Brock), licensing agreements from Raysearch (Brock) and Varian (Hugo).; K. Brock, Licensing Agreement - RaySearch Laboratories. Research Funding - Varian Medical Systems; G. Hugo, Research grant from National Institutes of Health, award number R01CA166119.; G. Christensen, Research support from NIH grants CA166119 and CA166703 and a gift from Roger Koch. There are no conflicts of interest.

Image Registration for PET/CT and CT Images with Particle Swarm Optimization

International Nuclear Information System (INIS)

Lee, Hak Jae; Kim, Yong Kwon; Lee, Ki Sung; Choi, Jong Hak; Kim, Chang Kyun; Moon, Guk Hyun; Joo, Sung Kwan; Kim, Kyeong Min; Cheon, Gi Jeong

2009-01-01

Image registration is a fundamental task in image processing used to match two or more images. It gives new information to the radiologists by matching images from different modalities. The objective of this study is to develop 2D image registration algorithm for PET/CT and CT images acquired by different systems at different times. We matched two CT images first (one from standalone CT and the other from PET/CT) that contain affluent anatomical information. Then, we geometrically transformed PET image according to the results of transformation parameters calculated by the previous step. We have used Affine transform to match the target and reference images. For the similarity measure, mutual information was explored. Use of particle swarm algorithm optimized the performance by finding the best matched parameter set within a reasonable amount of time. The results show good agreements of the images between PET/CT and CT. We expect the proposed algorithm can be used not only for PET/CT and CT image registration but also for different multi-modality imaging systems such as SPECT/CT, MRI/PET and so on.

WE-AB-BRA-08: Correction of Patient Motion in C-Arm Cone-Beam CT Using 3D-2D Registration

International Nuclear Information System (INIS)

Ouadah, S; Jacobson, M; Stayman, JW; Siewerdsen, JH; Ehtiati, T

2016-01-01

Purpose: Intraoperative C-arm cone-beam CT (CBCT) is subject to artifacts arising from patient motion during the fairly long (∼5–20 s) scan times. We present a fiducial free method to mitigate motion artifacts using 3D-2D image registration that simultaneously corrects residual errors in geometric calibration. Methods: A 3D-2D registration process was used to register each projection to DRRs computed from the 3D image by maximizing gradient orientation (GO) using the CMA-ES optimizer. The resulting rigid 6 DOF transforms were applied to the system projection matrices, and a 3D image was reconstructed via model-based image reconstruction (MBIR, which accommodates the resulting noncircular orbit). Experiments were conducted using a Zeego robotic C-arm (20 s, 200°, 496 projections) to image a head phantom undergoing various types of motion: 1) 5° lateral motion; 2) 15° lateral motion; and 3) 5° lateral motion with 10 mm periodic inferior-superior motion. Images were reconstructed using a penalized likelihood (PL) objective function, and structural similarity (SSIM) was measured for axial slices of the reconstructed images. A motion-free image was acquired using the same protocol for comparison. Results: There was significant improvement (p 0.99, indicating near identity to the motion-free reference. The point spread function (PSF) measured from a wire in the phantom was restored to that of the reference in each case. Conclusion: The 3D-2D registration method provides a robust framework for mitigation of motion artifacts and is expected to hold for applications in the head, pelvis, and extremities with reasonably constrained operative setup. Further improvement can be achieved by incorporating multiple rigid components and non-rigid deformation within the framework. The method is highly parallelizable and could in principle be run with every acquisition. Research supported by National Institutes of Health Grant No. R01-EB-017226 and academic

Fiducial registration error as a statistical process control metric in image-guided radiotherapy with prostatic markers

International Nuclear Information System (INIS)

Ung, M.N.; Wee, Leonard

2010-01-01

Full text: Portal imaging of implanted fiducial markers has been in use for image-guided radiotherapy (TORT) of prostate cancer, with ample attention to localization accuracy and organ motion. The geometric uncertainties in point-based rigid-body (PBRB) image registration during localization of prostate fiducial markers can be quantified in terms of a fiducial registration error (FRE). Statistical process control charts for individual patients can be designed to identify potentially significant deviation of FRE from expected behaviour. In this study, the aim was to retrospectively apply statistical process control methods to FREs in 34 individuals to identify parameters that may impact on the process stability in image-based localization. A robust procedure for estimating control parameters, control lim its and fixed tolerance levels from a small number of initial observations has been proposed and discussed. Four distinct types of qualitative control chart behavior have been observed. Probable clinical factors leading to IORT process instability are discussed in light of the control chart behaviour. Control charts have been shown to be a useful decision-making tool for detecting potentially out-of control processes on an individual basis. It can sensitively identify potential problems that warrant more detailed investigation in the 10RT of prostate cancer.

A coarse-to-fine scheme for groupwise registration of multisensor images

Directory of Open Access Journals (Sweden)

Yinghao Li

2016-11-01

Full Text Available Ensemble registration is concerned with a group of images that need to be registered simultaneously. It is challenging but important for many image analysis tasks such as vehicle detection and medical image fusion. To solve this problem effectively, a novel coarse-to-fine scheme for groupwise image registration is proposed. First, in the coarse registration step, unregistered images are divided into reference image set and float image set. The images of the two sets are registered based on segmented region matching. The coarse registration results are used as an initial solution for the next step. Then, in the fine registration step, a Gaussian mixture model with a local template is used to model the joint intensity of coarse-registered images. Meanwhile, a minimum message length criterion-based method is employed to determine the unknown number of mixing components. Based on this mixture model, a maximum likelihood framework is used to register a group of images. To evaluate the performance of the proposed approach, some representative groupwise registration approaches are compared on different image data sets. The experimental results show that the proposed approach has improved performance compared to conventional approaches.

Supervised local error estimation for nonlinear image registration using convolutional neural networks

NARCIS (Netherlands)

Eppenhof, Koen A.J.; Pluim, Josien P.W.; Styner, M.A.; Angelini, E.D.

2017-01-01

Error estimation in medical image registration is valuable when validating, comparing, or combining registration methods. To validate a nonlinear image registration method, ideally the registration error should be known for the entire image domain. We propose a supervised method for the estimation

An efficient similarity measure technique for medical image registration

Indian Academy of Sciences (India)

In this paper, an efficient similarity measure technique is proposed for medical image registration. The proposed approach is based on the Gerschgorin circles theorem. In this approach, image registration is carried out by considering Gerschgorin bounds of a covariance matrix of two compared images with normalized ...

Multi-band Image Registration Method Based on Fourier Transform

Institute of Scientific and Technical Information of China (English)

庹红娅; 刘允才

2004-01-01

This paper presented a registration method based on Fourier transform for multi-band images which is involved in translation and small rotation. Although different band images differ a lot in the intensity and features,they contain certain common information which we can exploit. A model was given that the multi-band images have linear correlations under the least-square sense. It is proved that the coefficients have no effect on the registration progress if two images have linear correlations. Finally, the steps of the registration method were proposed. The experiments show that the model is reasonable and the results are satisfying.

Evaluation of whole-body MR to CT deformable image registration

NARCIS (Netherlands)

Akbarzadeh, A.; Gutierrez, D.; Baskin, A.; Ay, M. R.; Ahmadian, A.; Alam, N. Riahi; Loevblad, K. O.; Zaidi, H.

2013-01-01

Multimodality image registration plays a crucial role in various clinical and research applications. The aim of this study is to present an optimized MR to CT whole-body deformable image registration algorithm and its validation using clinical studies. A 3D intermodality registration technique based

Registration of eye reflection and scene images using an aspherical eye model.

Science.gov (United States)

Nakazawa, Atsushi; Nitschke, Christian; Nishida, Toyoaki

2016-11-01

This paper introduces an image registration algorithm between an eye reflection and a scene image. Although there are currently a large number of image registration algorithms, this task remains difficult due to nonlinear distortions at the eye surface and large amounts of noise, such as iris texture, eyelids, eyelashes, and their shadows. To overcome this issue, we developed an image registration method combining an aspherical eye model that simulates nonlinear distortions considering eye geometry and a two-step iterative registration strategy that obtains dense correspondence of the feature points to achieve accurate image registrations for the entire image region. We obtained a database of eye reflection and scene images featuring four subjects in indoor and outdoor scenes and compared the registration performance with different asphericity conditions. Results showed that the proposed approach can perform accurate registration with an average accuracy of 1.05 deg by using the aspherical cornea model. This work is relevant for eye image analysis in general, enabling novel applications and scenarios.

Improved image registration by sparse patch-based deformation estimation.

Science.gov (United States)

Kim, Minjeong; Wu, Guorong; Wang, Qian; Lee, Seong-Whan; Shen, Dinggang

2015-01-15

Despite intensive efforts for decades, deformable image registration is still a challenging problem due to the potential large anatomical differences across individual images, which limits the registration performance. Fortunately, this issue could be alleviated if a good initial deformation can be provided for the two images under registration, which are often termed as the moving subject and the fixed template, respectively. In this work, we present a novel patch-based initial deformation prediction framework for improving the performance of existing registration algorithms. Our main idea is to estimate the initial deformation between subject and template in a patch-wise fashion by using the sparse representation technique. We argue that two image patches should follow the same deformation toward the template image if their patch-wise appearance patterns are similar. To this end, our framework consists of two stages, i.e., the training stage and the application stage. In the training stage, we register all training images to the pre-selected template, such that the deformation of each training image with respect to the template is known. In the application stage, we apply the following four steps to efficiently calculate the initial deformation field for the new test subject: (1) We pick a small number of key points in the distinctive regions of the test subject; (2) for each key point, we extract a local patch and form a coupled appearance-deformation dictionary from training images where each dictionary atom consists of the image intensity patch as well as their respective local deformations; (3) a small set of training image patches in the coupled dictionary are selected to represent the image patch of each subject key point by sparse representation. Then, we can predict the initial deformation for each subject key point by propagating the pre-estimated deformations on the selected training patches with the same sparse representation coefficients; and (4) we

Learning-based deformable image registration for infant MR images in the first year of life.

Science.gov (United States)

Hu, Shunbo; Wei, Lifang; Gao, Yaozong; Guo, Yanrong; Wu, Guorong; Shen, Dinggang

2017-01-01

Many brain development studies have been devoted to investigate dynamic structural and functional changes in the first year of life. To quantitatively measure brain development in such a dynamic period, accurate image registration for different infant subjects with possible large age gap is of high demand. Although many state-of-the-art image registration methods have been proposed for young and elderly brain images, very few registration methods work for infant brain images acquired in the first year of life, because of (a) large anatomical changes due to fast brain development and (b) dynamic appearance changes due to white-matter myelination. To address these two difficulties, we propose a learning-based registration method to not only align the anatomical structures but also alleviate the appearance differences between two arbitrary infant MR images (with large age gap) by leveraging the regression forest to predict both the initial displacement vector and appearance changes. Specifically, in the training stage, two regression models are trained separately, with (a) one model learning the relationship between local image appearance (of one development phase) and its displacement toward the template (of another development phase) and (b) another model learning the local appearance changes between the two brain development phases. Then, in the testing stage, to register a new infant image to the template, we first predict both its voxel-wise displacement and appearance changes by the two learned regression models. Since such initializations can alleviate significant appearance and shape differences between new infant image and the template, it is easy to just use a conventional registration method to refine the remaining registration. We apply our proposed registration method to align 24 infant subjects at five different time points (i.e., 2-week-old, 3-month-old, 6-month-old, 9-month-old, and 12-month-old), and achieve more accurate and robust registration

Fractional Regularization Term for Variational Image Registration

Directory of Open Access Journals (Sweden)

Rafael Verdú-Monedero

2009-01-01

Full Text Available Image registration is a widely used task of image analysis with applications in many fields. Its classical formulation and current improvements are given in the spatial domain. In this paper a regularization term based on fractional order derivatives is formulated. This term is defined and implemented in the frequency domain by translating the energy functional into the frequency domain and obtaining the Euler-Lagrange equations which minimize it. The new regularization term leads to a simple formulation and design, being applicable to higher dimensions by using the corresponding multidimensional Fourier transform. The proposed regularization term allows for a real gradual transition from a diffusion registration to a curvature registration which is best suited to some applications and it is not possible in the spatial domain. Results with 3D actual images show the validity of this approach.

Deformable image registration using convolutional neural networks

NARCIS (Netherlands)

Eppenhof, Koen A.J.; Lafarge, Maxime W.; Moeskops, Pim; Veta, Mitko; Pluim, Josien P.W.

2018-01-01

Deformable image registration can be time-consuming and often needs extensive parameterization to perform well on a specific application. We present a step towards a registration framework based on a three-dimensional convolutional neural network. The network directly learns transformations between

Image Registration of Cone-Beam Computer Tomography and Preprocedural Computer Tomography Aids in Localization of Adrenal Veins and Decreasing Radiation Dose in Adrenal Vein Sampling

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Busser, Wendy M. H., E-mail: wendy.busser@radboudumc.nl; Arntz, Mark J.; Jenniskens, Sjoerd F. M. [Radboud University Medical Center, Department of Radiology, Section of Interventional Radiology (Netherlands); Deinum, Jaap [Radboud University Medical Center, Department of General Internal Medicine (Netherlands); Hoogeveen, Yvonne L.; Lange, Frank de; Schultze Kool, Leo J. [Radboud University Medical Center, Department of Radiology, Section of Interventional Radiology (Netherlands)

2015-08-15

PurposeWe assessed whether image registration of cone-beam computed tomography (CT) (CBCT) and contrast-enhanced CT (CE-CT) images indicating the locations of the adrenal veins can aid in increasing the success rate of first-attempts adrenal vein sampling (AVS) and therefore decreasing patient radiation dose.Materials and Methods CBCT scans were acquired in the interventional suite (Philips Allura Xper FD20) and rigidly registered to the vertebra in previously acquired CE-CT. Adrenal vein locations were marked on the CT image and superimposed with live fluoroscopy and digital-subtraction angiography (DSA) to guide the AVS. Seventeen first attempts at AVS were performed with image registration and retrospectively compared with 15 first attempts without image registration performed earlier by the same 2 interventional radiologists. First-attempt AVS was considered successful when both adrenal vein samples showed representative cortisol levels. Sampling time, dose-area product (DAP), number of DSA runs, fluoroscopy time, and skin dose were recorded.ResultsWithout image registration, the first attempt at sampling was successful in 8 of 15 procedures indicating a success rate of 53.3 %. This increased to 76.5 % (13 of 17) by adding CBCT and CE-CT image registration to AVS procedures (p = 0.266). DAP values (p = 0.001) and DSA runs (p = 0.026) decreased significantly by adding image registration guidance. Sampling and fluoroscopy times and skin dose showed no significant changes.ConclusionGuidance based on registration of CBCT and previously acquired diagnostic CE-CT can aid in enhancing localization of the adrenal veins thereby increasing the success rate of first-attempt AVS with a significant decrease in the number of used DSA runs and, consequently, radiation dose required.

Monoplane 3D-2D registration of cerebral angiograms based on multi-objective stratified optimization

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Aksoy, T.; Špiclin, Ž.; Pernuš, F.; Unal, G.

2017-12-01

Registration of 3D pre-interventional to 2D intra-interventional medical images has an increasingly important role in surgical planning, navigation and treatment, because it enables the physician to co-locate depth information given by pre-interventional 3D images with the live information in intra-interventional 2D images such as x-ray. Most tasks during image-guided interventions are carried out under a monoplane x-ray, which is a highly ill-posed problem for state-of-the-art 3D to 2D registration methods. To address the problem of rigid 3D-2D monoplane registration we propose a novel multi-objective stratified parameter optimization, wherein a small set of high-magnitude intensity gradients are matched between the 3D and 2D images. The stratified parameter optimization matches rotation templates to depth templates, first sampled from projected 3D gradients and second from the 2D image gradients, so as to recover 3D rigid-body rotations and out-of-plane translation. The objective for matching was the gradient magnitude correlation coefficient, which is invariant to in-plane translation. The in-plane translations are then found by locating the maximum of the gradient phase correlation between the best matching pair of rotation and depth templates. On twenty pairs of 3D and 2D images of ten patients undergoing cerebral endovascular image-guided intervention the 3D to monoplane 2D registration experiments were setup with a rather high range of initial mean target registration error from 0 to 100 mm. The proposed method effectively reduced the registration error to below 2 mm, which was further refined by a fast iterative method and resulted in a high final registration accuracy (0.40 mm) and high success rate (> 96%). Taking into account a fast execution time below 10 s, the observed performance of the proposed method shows a high potential for application into clinical image-guidance systems.

An atlas-based multimodal registration method for 2D images with discrepancy structures.

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Lv, Wenchao; Chen, Houjin; Peng, Yahui; Li, Yanfeng; Li, Jupeng

2018-06-04

An atlas-based multimodal registration method for 2-dimension images with discrepancy structures was proposed in this paper. Atlas was utilized for complementing the discrepancy structure information in multimodal medical images. The scheme includes three steps: floating image to atlas registration, atlas to reference image registration, and field-based deformation. To evaluate the performance, a frame model, a brain model, and clinical images were employed in registration experiments. We measured the registration performance by the squared sum of intensity differences. Results indicate that this method is robust and performs better than the direct registration for multimodal images with discrepancy structures. We conclude that the proposed method is suitable for multimodal images with discrepancy structures. Graphical Abstract An Atlas-based multimodal registration method schematic diagram.

3-D brain image registration using optimal morphological processing

International Nuclear Information System (INIS)

Loncaric, S.; Dhawan, A.P.

1994-01-01

The three-dimensional (3-D) registration of Magnetic Resonance (MR) and Positron Emission Tomographic (PET) images of the brain is important for analysis of the human brain and its diseases. A procedure for optimization of (3-D) morphological structuring elements, based on a genetic algorithm, is presented in the paper. The registration of the MR and PET images is done by means of a registration procedure in two major phases. In the first phase, the Iterative Principal Axis Transform (IPAR) is used for initial registration. In the second phase, the optimal shape description method based on the Morphological Signature Transform (MST) is used for final registration. The morphological processing is used to improve the accuracy of the basic IPAR method. The brain ventricle is used as a landmark for MST registration. A near-optimal structuring element obtained by means of a genetic algorithm is used in MST to describe the shape of the ventricle. The method has been tested on the set of brain images demonstrating the feasibility of approach. (author). 11 refs., 3 figs

Intensity-based hierarchical elastic registration using approximating splines.

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Serifovic-Trbalic, Amira; Demirovic, Damir; Cattin, Philippe C

2014-01-01

We introduce a new hierarchical approach for elastic medical image registration using approximating splines. In order to obtain the dense deformation field, we employ Gaussian elastic body splines (GEBS) that incorporate anisotropic landmark errors and rotation information. Since the GEBS approach is based on a physical model in form of analytical solutions of the Navier equation, it can very well cope with the local as well as global deformations present in the images by varying the standard deviation of the Gaussian forces. The proposed GEBS approximating model is integrated into the elastic hierarchical image registration framework, which decomposes a nonrigid registration problem into numerous local rigid transformations. The approximating GEBS registration scheme incorporates anisotropic landmark errors as well as rotation information. The anisotropic landmark localization uncertainties can be estimated directly from the image data, and in this case, they represent the minimal stochastic localization error, i.e., the Cramér-Rao bound. The rotation information of each landmark obtained from the hierarchical procedure is transposed in an additional angular landmark, doubling the number of landmarks in the GEBS model. The modified hierarchical registration using the approximating GEBS model is applied to register 161 image pairs from a digital mammogram database. The obtained results are very encouraging, and the proposed approach significantly improved all registrations comparing the mean-square error in relation to approximating TPS with the rotation information. On artificially deformed breast images, the newly proposed method performed better than the state-of-the-art registration algorithm introduced by Rueckert et al. (IEEE Trans Med Imaging 18:712-721, 1999). The average error per breast tissue pixel was less than 2.23 pixels compared to 2.46 pixels for Rueckert's method. The proposed hierarchical elastic image registration approach incorporates the GEBS

On combining algorithms for deformable image registration

NARCIS (Netherlands)

Muenzing, S.E.A.; Ginneken, van B.; Pluim, J.P.W.; Dawant, B.M.

2012-01-01

We propose a meta-algorithm for registration improvement by combining deformable image registrations (MetaReg). It is inspired by a well-established method from machine learning, the combination of classifiers. MetaReg consists of two main components: (1) A strategy for composing an improved

Parallel Processing and Bio-inspired Computing for Biomedical Image Registration

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Silviu Ioan Bejinariu

2014-07-01

Full Text Available Image Registration (IR is an optimization problem computing optimal parameters of a geometric transform used to overlay one or more source images to a given model by maximizing a similarity measure. In this paper the use of bio-inspired optimization algorithms in image registration is analyzed. Results obtained by means of three different algorithms are compared: Bacterial Foraging Optimization Algorithm (BFOA, Genetic Algorithm (GA and Clonal Selection Algorithm (CSA. Depending on the images type, the registration may be: area based, which is slow but more precise, and features based, which is faster. In this paper a feature based approach based on the Scale Invariant Feature Transform (SIFT is proposed. Finally, results obtained using sequential and parallel implementations on multi-core systems for area based and features based image registration are compared.

Frameless image registration of X-ray CT and SPECT by volume matching

International Nuclear Information System (INIS)

Tanaka, Yuko; Kihara, Tomohiko; Yui, Nobuharu; Kinoshita, Fujimi; Kamimura, Yoshitsugu; Yamada, Yoshifumi.

1998-01-01

Image registration of functional (SPECT) and morphological (X-ray CT/MRI) images is studied in order to improve the accuracy and the quantity of the image diagnosis. We have developed a new frameless registration method of X-ray CT and SPECT image using transmission CT image acquired for absorption correction of SPECT images. This is the automated registration method and calculates the transformation matrix between the two coordinate systems of image data by the optimization method. This registration method is based on the similar physical property of X-ray CT and transmission CT image. The three-dimensional overlap of the bone region is used for image matching. We verified by a phantom test that it can provide a good result of within two millimeters error. We also evaluated visually the accuracy of the registration method by the application study of SPECT, X-ray CT, and transmission CT head images. This method can be carried out accurately without any frames. We expect this registration method becomes an efficient tool to improve image diagnosis and medical treatment. (author)

Conventional 3D staging PET/CT in CT simulation for lung cancer: impact of rigid and deformable target volume alignments for radiotherapy treatment planning.

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Hanna, G G; Van Sörnsen De Koste, J R; Carson, K J; O'Sullivan, J M; Hounsell, A R; Senan, S

2011-10-01

Positron emission tomography (PET)/CT scans can improve target definition in radiotherapy for non-small cell lung cancer (NSCLC). As staging PET/CT scans are increasingly available, we evaluated different methods for co-registration of staging PET/CT data to radiotherapy simulation (RTP) scans. 10 patients underwent staging PET/CT followed by RTP PET/CT. On both scans, gross tumour volumes (GTVs) were delineated using CT (GTV(CT)) and PET display settings. Four PET-based contours (manual delineation, two threshold methods and a source-to-background ratio method) were delineated. The CT component of the staging scan was co-registered using both rigid and deformable techniques to the CT component of RTP PET/CT. Subsequently rigid registration and deformation warps were used to transfer PET and CT contours from the staging scan to the RTP scan. Dice's similarity coefficient (DSC) was used to assess the registration accuracy of staging-based GTVs following both registration methods with the GTVs delineated on the RTP PET/CT scan. When the GTV(CT) delineated on the staging scan after both rigid registration and deformation was compared with the GTV(CT)on the RTP scan, a significant improvement in overlap (registration) using deformation was observed (mean DSC 0.66 for rigid registration and 0.82 for deformable registration, p = 0.008). A similar comparison for PET contours revealed no significant improvement in overlap with the use of deformable registration. No consistent improvements in similarity measures were observed when deformable registration was used for transferring PET-based contours from a staging PET/CT. This suggests that currently the use of rigid registration remains the most appropriate method for RTP in NSCLC.

A new robust markerless method for automatic image-to-patient registration in image-guided neurosurgery system.

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Liu, Yinlong; Song, Zhijian; Wang, Manning

2017-12-01

Compared with the traditional point-based registration in the image-guided neurosurgery system, surface-based registration is preferable because it does not use fiducial markers before image scanning and does not require image acquisition dedicated for navigation purposes. However, most existing surface-based registration methods must include a manual step for coarse registration, which increases the registration time and elicits some inconvenience and uncertainty. A new automatic surface-based registration method is proposed, which applies 3D surface feature description and matching algorithm to obtain point correspondences for coarse registration and uses the iterative closest point (ICP) algorithm in the last step to obtain an image-to-patient registration. Both phantom and clinical data were used to execute automatic registrations and target registration error (TRE) calculated to verify the practicality and robustness of the proposed method. In phantom experiments, the registration accuracy was stable across different downsampling resolutions (18-26 mm) and different support radii (2-6 mm). In clinical experiments, the mean TREs of two patients by registering full head surfaces were 1.30 mm and 1.85 mm. This study introduced a new robust automatic surface-based registration method based on 3D feature matching. The method achieved sufficient registration accuracy with different real-world surface regions in phantom and clinical experiments.

SU-E-J-237: Image Feature Based DRR and Portal Image Registration

Energy Technology Data Exchange (ETDEWEB)

Wang, X; Chang, J [NY Weill Cornell Medical Ctr, NY (United States)

2014-06-01

Purpose: Two-dimensional (2D) matching of the kV X-ray and digitally reconstructed radiography (DRR) images is an important setup technique for image-guided radiotherapy (IGRT). In our clinics, mutual information based methods are used for this purpose on commercial linear accelerators, but with often needs for manual corrections. This work proved the feasibility that feature based image transform can be used to register kV and DRR images. Methods: The scale invariant feature transform (SIFT) method was implemented to detect the matching image details (or key points) between the kV and DRR images. These key points represent high image intensity gradients, and thus the scale invariant features. Due to the poor image contrast from our kV image, direct application of the SIFT method yielded many detection errors. To assist the finding of key points, the center coordinates of the kV and DRR images were read from the DICOM header, and the two groups of key points with similar relative positions to their corresponding centers were paired up. Using these points, a rigid transform (with scaling, horizontal and vertical shifts) was estimated. We also artificially introduced vertical and horizontal shifts to test the accuracy of our registration method on anterior-posterior (AP) and lateral pelvic images. Results: The results provided a satisfactory overlay of the transformed kV onto the DRR image. The introduced vs. detected shifts were fit into a linear regression. In the AP image experiments, linear regression analysis showed a slope of 1.15 and 0.98 with an R2 of 0.89 and 0.99 for the horizontal and vertical shifts, respectively. The results are 1.2 and 1.3 with R2 of 0.72 and 0.82 for the lateral image shifts. Conclusion: This work provided an alternative technique for kV to DRR alignment. Further improvements in the estimation accuracy and image contrast tolerance are underway.

A ROBUST REGISTRATION ALGORITHM FOR POINT CLOUDS FROM UAV IMAGES FOR CHANGE DETECTION

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A. Al-Rawabdeh

2016-06-01

Full Text Available Landslides are among the major threats to urban landscape and manmade infrastructure. They often cause economic losses, property damages, and loss of lives. Temporal monitoring data of landslides from different epochs empowers the evaluation of landslide progression. Alignment of overlapping surfaces from two or more epochs is crucial for the proper analysis of landslide dynamics. The traditional methods for point-cloud-based landslide monitoring rely on using a variation of the Iterative Closest Point (ICP registration procedure to align any reconstructed surfaces from different epochs to a common reference frame. However, sometimes the ICP-based registration can fail or may not provide sufficient accuracy. For example, point clouds from different epochs might fit to local minima due to lack of geometrical variability within the data. Also, manual interaction is required to exclude any non-stable areas from the registration process. In this paper, a robust image-based registration method is introduced for the simultaneous evaluation of all registration parameters. This includes the Interior Orientation Parameters (IOPs of the camera and the Exterior Orientation Parameters (EOPs of the involved images from all available observation epochs via a bundle block adjustment with self-calibration. Next, a semi-global dense matching technique is implemented to generate dense 3D point clouds for each epoch using the images captured in a particular epoch separately. The normal distances between any two consecutive point clouds can then be readily computed, because the point clouds are already effectively co-registered. A low-cost DJI Phantom II Unmanned Aerial Vehicle (UAV was customised and used in this research for temporal data collection over an active soil creep area in Lethbridge, Alberta, Canada. The customisation included adding a GPS logger and a Large-Field-Of-View (LFOV action camera which facilitated capturing high-resolution geo-tagged images

a Robust Registration Algorithm for Point Clouds from Uav Images for Change Detection

Science.gov (United States)

Al-Rawabdeh, A.; Al-Gurrani, H.; Al-Durgham, K.; Detchev, I.; He, F.; El-Sheimy, N.; Habib, A.

2016-06-01

Landslides are among the major threats to urban landscape and manmade infrastructure. They often cause economic losses, property damages, and loss of lives. Temporal monitoring data of landslides from different epochs empowers the evaluation of landslide progression. Alignment of overlapping surfaces from two or more epochs is crucial for the proper analysis of landslide dynamics. The traditional methods for point-cloud-based landslide monitoring rely on using a variation of the Iterative Closest Point (ICP) registration procedure to align any reconstructed surfaces from different epochs to a common reference frame. However, sometimes the ICP-based registration can fail or may not provide sufficient accuracy. For example, point clouds from different epochs might fit to local minima due to lack of geometrical variability within the data. Also, manual interaction is required to exclude any non-stable areas from the registration process. In this paper, a robust image-based registration method is introduced for the simultaneous evaluation of all registration parameters. This includes the Interior Orientation Parameters (IOPs) of the camera and the Exterior Orientation Parameters (EOPs) of the involved images from all available observation epochs via a bundle block adjustment with self-calibration. Next, a semi-global dense matching technique is implemented to generate dense 3D point clouds for each epoch using the images captured in a particular epoch separately. The normal distances between any two consecutive point clouds can then be readily computed, because the point clouds are already effectively co-registered. A low-cost DJI Phantom II Unmanned Aerial Vehicle (UAV) was customised and used in this research for temporal data collection over an active soil creep area in Lethbridge, Alberta, Canada. The customisation included adding a GPS logger and a Large-Field-Of-View (LFOV) action camera which facilitated capturing high-resolution geo-tagged images in two epochs

WE-AB-BRA-08: Correction of Patient Motion in C-Arm Cone-Beam CT Using 3D-2D Registration

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Ouadah, S; Jacobson, M; Stayman, JW; Siewerdsen, JH [Johns Hopkins University, Baltimore, MD (United States); Ehtiati, T [Siemens Medical Solutions USA, Inc., Hoffman Estates, IL (United States)

2016-06-15

Purpose: Intraoperative C-arm cone-beam CT (CBCT) is subject to artifacts arising from patient motion during the fairly long (∼5–20 s) scan times. We present a fiducial free method to mitigate motion artifacts using 3D-2D image registration that simultaneously corrects residual errors in geometric calibration. Methods: A 3D-2D registration process was used to register each projection to DRRs computed from the 3D image by maximizing gradient orientation (GO) using the CMA-ES optimizer. The resulting rigid 6 DOF transforms were applied to the system projection matrices, and a 3D image was reconstructed via model-based image reconstruction (MBIR, which accommodates the resulting noncircular orbit). Experiments were conducted using a Zeego robotic C-arm (20 s, 200°, 496 projections) to image a head phantom undergoing various types of motion: 1) 5° lateral motion; 2) 15° lateral motion; and 3) 5° lateral motion with 10 mm periodic inferior-superior motion. Images were reconstructed using a penalized likelihood (PL) objective function, and structural similarity (SSIM) was measured for axial slices of the reconstructed images. A motion-free image was acquired using the same protocol for comparison. Results: There was significant improvement (p < 0.001) in the SSIM of the motion-corrected (MC) images compared to uncorrected images. The SSIM in MC-PL images was >0.99, indicating near identity to the motion-free reference. The point spread function (PSF) measured from a wire in the phantom was restored to that of the reference in each case. Conclusion: The 3D-2D registration method provides a robust framework for mitigation of motion artifacts and is expected to hold for applications in the head, pelvis, and extremities with reasonably constrained operative setup. Further improvement can be achieved by incorporating multiple rigid components and non-rigid deformation within the framework. The method is highly parallelizable and could in principle be run with every

S-HAMMER: hierarchical attribute-guided, symmetric diffeomorphic registration for MR brain images.

Science.gov (United States)

Wu, Guorong; Kim, Minjeong; Wang, Qian; Shen, Dinggang

2014-03-01

Deformable registration has been widely used in neuroscience studies for spatial normalization of brain images onto the standard space. Because of possible large anatomical differences across different individual brains, registration performance could be limited when trying to estimate a single directed deformation pathway, i.e., either from template to subject or from subject to template. Symmetric image registration, however, offers an effective way to simultaneously deform template and subject images toward each other until they meet at the middle point. Although some intensity-based registration algorithms have nicely incorporated this concept of symmetric deformation, the pointwise intensity matching between two images may not necessarily imply the matching of correct anatomical correspondences. Based on HAMMER registration algorithm (Shen and Davatzikos, [2002]: IEEE Trans Med Imaging 21:1421-1439), we integrate the strategies of hierarchical attribute matching and symmetric diffeomorphic deformation to build a new symmetric-diffeomorphic HAMMER registration algorithm, called as S-HAMMER. The performance of S-HAMMER has been extensively compared with 14 state-of-the-art nonrigid registration algorithms evaluated in (Klein et al., [2009]: NeuroImage 46:786-802) by using real brain images in LPBA40, IBSR18, CUMC12, and MGH10 datasets. In addition, the registration performance of S-HAMMER, by comparison with other methods, is also demonstrated on both elderly MR brain images (>70 years old) and the simulated brain images with ground-truth deformation fields. In all experiments, our proposed method achieves the best registration performance over all other registration methods, indicating the high applicability of our method in future neuroscience and clinical applications. Copyright © 2013 Wiley Periodicals, Inc.

Image Registration Algorithm Based on Parallax Constraint and Clustering Analysis

Science.gov (United States)

Wang, Zhe; Dong, Min; Mu, Xiaomin; Wang, Song

2018-01-01

To resolve the problem of slow computation speed and low matching accuracy in image registration, a new image registration algorithm based on parallax constraint and clustering analysis is proposed. Firstly, Harris corner detection algorithm is used to extract the feature points of two images. Secondly, use Normalized Cross Correlation (NCC) function to perform the approximate matching of feature points, and the initial feature pair is obtained. Then, according to the parallax constraint condition, the initial feature pair is preprocessed by K-means clustering algorithm, which is used to remove the feature point pairs with obvious errors in the approximate matching process. Finally, adopt Random Sample Consensus (RANSAC) algorithm to optimize the feature points to obtain the final feature point matching result, and the fast and accurate image registration is realized. The experimental results show that the image registration algorithm proposed in this paper can improve the accuracy of the image matching while ensuring the real-time performance of the algorithm.

Validation of an elastic registration technique to estimate anatomical lung modification in Non-Small-Cell Lung Cancer Tomotherapy

International Nuclear Information System (INIS)

Faggiano, Elena; Cattaneo, Giovanni M; Ciavarro, Cristina; Dell'Oca, Italo; Persano, Diego; Calandrino, Riccardo; Rizzo, Giovanna

2011-01-01

The study of lung parenchyma anatomical modification is useful to estimate dose discrepancies during the radiation treatment of Non-Small-Cell Lung Cancer (NSCLC) patients. We propose and validate a method, based on free-form deformation and mutual information, to elastically register planning kVCT with daily MVCT images, to estimate lung parenchyma modification during Tomotherapy. We analyzed 15 registrations between the planning kVCT and 3 MVCT images for each of the 5 NSCLC patients. Image registration accuracy was evaluated by visual inspection and, quantitatively, by Correlation Coefficients (CC) and Target Registration Errors (TRE). Finally, a lung volume correspondence analysis was performed to specifically evaluate registration accuracy in lungs. Results showed that elastic registration was always satisfactory, both qualitatively and quantitatively: TRE after elastic registration (average value of 3.6 mm) remained comparable and often smaller than voxel resolution. Lung volume variations were well estimated by elastic registration (average volume and centroid errors of 1.78% and 0.87 mm, respectively). Our results demonstrate that this method is able to estimate lung deformations in thorax MVCT, with an accuracy within 3.6 mm comparable or smaller than the voxel dimension of the kVCT and MVCT images. It could be used to estimate lung parenchyma dose variations in thoracic Tomotherapy

Entropy-Based Block Processing for Satellite Image Registration

Directory of Open Access Journals (Sweden)

Ikhyun Lee

2012-11-01

Full Text Available Image registration is an important task in many computer vision applications such as fusion systems, 3D shape recovery and earth observation. Particularly, registering satellite images is challenging and time-consuming due to limited resources and large image size. In such scenario, state-of-the-art image registration methods such as scale-invariant feature transform (SIFT may not be suitable due to high processing time. In this paper, we propose an algorithm based on block processing via entropy to register satellite images. The performance of the proposed method is evaluated using different real images. The comparative analysis shows that it not only reduces the processing time but also enhances the accuracy.

The Insight ToolKit Image Registration Framework

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Brian eAvants

2014-04-01

Full Text Available Publicly available scientific resources help establish evaluation standards, provide a platform for teaching and improve reproducibility. Version 4 of the Insight ToolKit ( ITK4 seeks to es- tablish new standards in publicly available image registration methodology. ITK4 makes severaladvances in comparison to previous versions of ITK. ITK4 supports both multivariate images and objective functions; it also unifies high-dimensional (deformation field and low-dimensional (affine transformations with metrics that are reusable across transform types and with com- posite transforms that allow arbitrary series of geometric mappings to be chained together seamlessly. Metrics and optimizers take advantage of multi-core resources, when available.Furthermore, ITK4 reduces the parameter optimization burden via principled heuristics that automatically set scaling across disparate parameter types (rotations versus translations. A related approach also constrains steps sizes for gradient-based optimizers. The result is that tuning for different metrics and/or image pairs is rarely necessary allowing the researcher tomore easily focus on design/comparison of registration strategies. In total, the ITK4 contribu- tion is intended as a structure to support reproducible research practices, will provide a more extensive foundation against which to evaluate new work in image registration and also enable application level programmers a broad suite of tools on which to build. Finally, we contextu- alize this work with a reference registration evaluation study with application to pediatric brainlabeling.

SU-C-18A-02: Image-Based Camera Tracking: Towards Registration of Endoscopic Video to CT

International Nuclear Information System (INIS)

Ingram, S; Rao, A; Wendt, R; Castillo, R; Court, L; Yang, J; Beadle, B

2014-01-01

Purpose: Endoscopic examinations are routinely performed on head and neck and esophageal cancer patients. However, these images are underutilized for radiation therapy because there is currently no way to register them to a CT of the patient. The purpose of this work is to develop a method to track the motion of an endoscope within a structure using images from standard clinical equipment. This method will be incorporated into a broader endoscopy/CT registration framework. Methods: We developed a software algorithm to track the motion of an endoscope within an arbitrary structure. We computed frame-to-frame rotation and translation of the camera by tracking surface points across the video sequence and utilizing two-camera epipolar geometry. The resulting 3D camera path was used to recover the surrounding structure via triangulation methods. We tested this algorithm on a rigid cylindrical phantom with a pattern spray-painted on the inside. We did not constrain the motion of the endoscope while recording, and we did not constrain our measurements using the known structure of the phantom. Results: Our software algorithm can successfully track the general motion of the endoscope as it moves through the phantom. However, our preliminary data do not show a high degree of accuracy in the triangulation of 3D point locations. More rigorous data will be presented at the annual meeting. Conclusion: Image-based camera tracking is a promising method for endoscopy/CT image registration, and it requires only standard clinical equipment. It is one of two major components needed to achieve endoscopy/CT registration, the second of which is tying the camera path to absolute patient geometry. In addition to this second component, future work will focus on validating our camera tracking algorithm in the presence of clinical imaging features such as patient motion, erratic camera motion, and dynamic scene illumination

The plant virus microscope image registration method based on mismatches removing.

Science.gov (United States)

Wei, Lifang; Zhou, Shucheng; Dong, Heng; Mao, Qianzhuo; Lin, Jiaxiang; Chen, Riqing

2016-01-01

The electron microscopy is one of the major means to observe the virus. The view of virus microscope images is limited by making specimen and the size of the camera's view field. To solve this problem, the virus sample is produced into multi-slice for information fusion and image registration techniques are applied to obtain large field and whole sections. Image registration techniques have been developed in the past decades for increasing the camera's field of view. Nevertheless, these approaches typically work in batch mode and rely on motorized microscopes. Alternatively, the methods are conceived just to provide visually pleasant registration for high overlap ratio image sequence. This work presents a method for virus microscope image registration acquired with detailed visual information and subpixel accuracy, even when overlap ratio of image sequence is 10% or less. The method proposed focus on the correspondence set and interimage transformation. A mismatch removal strategy is proposed by the spatial consistency and the components of keypoint to enrich the correspondence set. And the translation model parameter as well as tonal inhomogeneities is corrected by the hierarchical estimation and model select. In the experiments performed, we tested different registration approaches and virus images, confirming that the translation model is not always stationary, despite the fact that the images of the sample come from the same sequence. The mismatch removal strategy makes building registration of virus microscope images at subpixel accuracy easier and optional parameters for building registration according to the hierarchical estimation and model select strategies make the proposed method high precision and reliable for low overlap ratio image sequence. Copyright © 2015 Elsevier Ltd. All rights reserved.

Patient-Specific Biomechanical Model as Whole-Body CT Image Registration Tool

OpenAIRE

Li, Mao; Miller, Karol; Joldes, Grand Roman; Doyle, Barry; Garlapati, Revanth Reddy; Kikinis, Ron; Wittek, Adam

2015-01-01

Whole-body computed tomography (CT) image registration is important for cancer diagnosis, therapy planning and treatment. Such registration requires accounting for large differences between source and target images caused by deformations of soft organs/tissues and articulated motion of skeletal structures. The registration algorithms relying solely on image processing methods exhibit deficiencies in accounting for such deformations and motion. We propose to predict the deformations and moveme...

Robust linear registration of CT images using random regression forests

Science.gov (United States)

Konukoglu, Ender; Criminisi, Antonio; Pathak, Sayan; Robertson, Duncan; White, Steve; Haynor, David; Siddiqui, Khan

2011-03-01

Global linear registration is a necessary first step for many different tasks in medical image analysis. Comparing longitudinal studies1, cross-modality fusion2, and many other applications depend heavily on the success of the automatic registration. The robustness and efficiency of this step is crucial as it affects all subsequent operations. Most common techniques cast the linear registration problem as the minimization of a global energy function based on the image intensities. Although these algorithms have proved useful, their robustness in fully automated scenarios is still an open question. In fact, the optimization step often gets caught in local minima yielding unsatisfactory results. Recent algorithms constrain the space of registration parameters by exploiting implicit or explicit organ segmentations, thus increasing robustness4,5. In this work we propose a novel robust algorithm for automatic global linear image registration. Our method uses random regression forests to estimate posterior probability distributions for the locations of anatomical structures - represented as axis aligned bounding boxes6. These posterior distributions are later integrated in a global linear registration algorithm. The biggest advantage of our algorithm is that it does not require pre-defined segmentations or regions. Yet it yields robust registration results. We compare the robustness of our algorithm with that of the state of the art Elastix toolbox7. Validation is performed via 1464 pair-wise registrations in a database of very diverse 3D CT images. We show that our method decreases the "failure" rate of the global linear registration from 12.5% (Elastix) to only 1.9%.

Open source deformable image registration system for treatment planning and recurrence CT scans. Validation in the head and neck region

International Nuclear Information System (INIS)

Zukauskaite, Ruta; Brink, Carsten; Hansen, Christian Roenn; Bertelsen, Anders; Johansen, Joergen; Eriksen, Jesper Grau; Grau, Cai

2016-01-01

Clinical application of deformable registration (DIR) of medical images remains limited due to sparse validation of DIR methods in specific situations, e. g. in case of cancer recurrences. In this study the accuracy of DIR for registration of planning CT (pCT) and recurrence CT (rCT) images of head and neck squamous cell carcinoma (HNSCC) patients was evaluated. Twenty patients treated with definitive IMRT for HNSCC in 2010-2012 were included. For each patient, a pCT and an rCT scan were used. Median interval between the scans was 8.5 months. One observer manually contoured eight anatomical regions-of-interest (ROI) twice on pCT and once on rCT. pCT and rCT images were deformably registered using the open source software elastix. Mean surface distance (MSD) and Dice similarity coefficient (DSC) between contours were used for validation of DIR. A measure for delineation uncertainty was estimated by assessing MSD from the re-delineations of the same ROI on pCT. DIR and manual contouring uncertainties were correlated with tissue volume and rigidity. MSD varied 1-3 mm for different ROIs for DIR and 1-1.5 mm for re-delineated ROIs performed on pCT. DSC for DIR varied between 0.58 and 0.79 for soft tissues and was 0.79 or higher for bony structures, and correlated with the volumes of ROIs (r = 0.5, p < 0.001) and tissue rigidity (r = 0.54, p < 0.001). DIR using elastix in HNSCC on planning and recurrence CT scans is feasible; an uncertainty of the method is close to the voxel size length of the planning CT images. (orig.) [de

Multimodality Registration without a Dedicated Multimodality Scanner

Directory of Open Access Journals (Sweden)

Bradley J. Beattie

2007-03-01

Full Text Available Multimodality scanners that allow the acquisition of both functional and structural image sets on a single system have recently become available for animal research use. Although the resultant registered functional/structural image sets can greatly enhance the interpretability of the functional data, the cost of multimodality systems can be prohibitive, and they are often limited to two modalities, which generally do not include magnetic resonance imaging. Using a thin plastic wrap to immobilize and fix a mouse or other small animal atop a removable bed, we are able to calculate registrations between all combinations of four different small animal imaging scanners (positron emission tomography, single-photon emission computed tomography, magnetic resonance, and computed tomography [CT] at our disposal, effectively equivalent to a quadruple-modality scanner. A comparison of serially acquired CT images, with intervening acquisitions on other scanners, demonstrates the ability of the proposed procedures to maintain the rigidity of an anesthetized mouse during transport between scanners. Movement of the bony structures of the mouse was estimated to be 0.62 mm. Soft tissue movement was predominantly the result of the filling (or emptying of the urinary bladder and thus largely constrained to this region. Phantom studies estimate the registration errors for all registration types to be less than 0.5 mm. Functional images using tracers targeted to known structures verify the accuracy of the functional to structural registrations. The procedures are easy to perform and produce robust and accurate results that rival those of dedicated multimodality scanners, but with more flexible registration combinations and while avoiding the expense and redundancy of multimodality systems.

Use of articulated registration for response assessment of individual metastatic bone lesions

International Nuclear Information System (INIS)

Yip, Stephen; Jeraj, Robert

2014-01-01

Accurate skeleton registration is necessary to match corresponding metastatic bone lesions for response assessment over multiple scans. In articulated registration (ART), whole-body skeletons are registered by auto-segmenting individual bones, then rigidly aligning them. Performance and robustness of the ART in lesion matching were evaluated and compared to other commonly used registration techniques. Sixteen prostate cancer patients were treated either with molecular targeted therapy or chemotherapy. Ten out of the 16 patients underwent the double baseline whole-body [F-18]NaF PET/CT scans for test-retest (TRT) evaluation. Twelve of the 16 patients underwent pre- and mid-treatment [F-18]NaF PET/CT scans. Skeletons at different time points were registered using ART, rigid, and deformable (DR) registration algorithms. The corresponding lesions were contoured and identified on successive PET images based on including the voxels with the standardized uptake value over 15. Each algorithm was evaluated for its ability to accurately align corresponding lesions via skeleton registration. A lesion matching score (MS) was measured for each lesion, which quantified the per cent overlap between the lesion's two corresponding contours. Three separate sensitivity studies were conducted to investigate the robustness of ART in matching: sensitivity of lesion matching to various contouring threshold levels, effects of imperfections in the bone auto-segmentation and sensitivity of mis-registration. The performance of ART (MS = 82% for both datasets, p ≪ 0.001) in lesion matching was significantly better than rigid (MS TRT  = 53%, MS Response  = 46%) and DR (MS TRT  = 46%, MS Response  = 45%) algorithms. Neither varying threshold levels for lesion contouring nor imperfect bone segmentation had significant (p∼0.10) impact on the ART matching performance as the MS remained unchanged. Despite the mis-registration reduced MS for ART, as low as 67% (p ≪ 0.001), the

Overcoming Registration Uncertainty in Image Super-Resolution: Maximize or Marginalize?

Directory of Open Access Journals (Sweden)

Andrew Zisserman

2007-01-01

Full Text Available In multiple-image super-resolution, a high-resolution image is estimated from a number of lower-resolution images. This usually involves computing the parameters of a generative imaging model (such as geometric and photometric registration, and blur and obtaining a MAP estimate by minimizing a cost function including an appropriate prior. Two alternative approaches are examined. First, both registrations and the super-resolution image are found simultaneously using a joint MAP optimization. Second, we perform Bayesian integration over the unknown image registration parameters, deriving a cost function whose only variables of interest are the pixel values of the super-resolution image. We also introduce a scheme to learn the parameters of the image prior as part of the super-resolution algorithm. We show examples on a number of real sequences including multiple stills, digital video, and DVDs of movies.

A block matching-based registration algorithm for localization of locally advanced lung tumors

Energy Technology Data Exchange (ETDEWEB)

Robertson, Scott P.; Weiss, Elisabeth; Hugo, Geoffrey D., E-mail: gdhugo@vcu.edu [Department of Radiation Oncology, Virginia Commonwealth University, Richmond, Virginia, 23298 (United States)

2014-04-15

Purpose: To implement and evaluate a block matching-based registration (BMR) algorithm for locally advanced lung tumor localization during image-guided radiotherapy. Methods: Small (1 cm{sup 3}), nonoverlapping image subvolumes (“blocks”) were automatically identified on the planning image to cover the tumor surface using a measure of the local intensity gradient. Blocks were independently and automatically registered to the on-treatment image using a rigid transform. To improve speed and robustness, registrations were performed iteratively from coarse to fine image resolution. At each resolution, all block displacements having a near-maximum similarity score were stored. From this list, a single displacement vector for each block was iteratively selected which maximized the consistency of displacement vectors across immediately neighboring blocks. These selected displacements were regularized using a median filter before proceeding to registrations at finer image resolutions. After evaluating all image resolutions, the global rigid transform of the on-treatment image was computed using a Procrustes analysis, providing the couch shift for patient setup correction. This algorithm was evaluated for 18 locally advanced lung cancer patients, each with 4–7 weekly on-treatment computed tomography scans having physician-delineated gross tumor volumes. Volume overlap (VO) and border displacement errors (BDE) were calculated relative to the nominal physician-identified targets to establish residual error after registration. Results: Implementation of multiresolution registration improved block matching accuracy by 39% compared to registration using only the full resolution images. By also considering multiple potential displacements per block, initial errors were reduced by 65%. Using the final implementation of the BMR algorithm, VO was significantly improved from 77% ± 21% (range: 0%–100%) in the initial bony alignment to 91% ± 8% (range: 56%–100%;p < 0

A block matching-based registration algorithm for localization of locally advanced lung tumors

International Nuclear Information System (INIS)

Robertson, Scott P.; Weiss, Elisabeth; Hugo, Geoffrey D.

2014-01-01

Purpose: To implement and evaluate a block matching-based registration (BMR) algorithm for locally advanced lung tumor localization during image-guided radiotherapy. Methods: Small (1 cm 3 ), nonoverlapping image subvolumes (“blocks”) were automatically identified on the planning image to cover the tumor surface using a measure of the local intensity gradient. Blocks were independently and automatically registered to the on-treatment image using a rigid transform. To improve speed and robustness, registrations were performed iteratively from coarse to fine image resolution. At each resolution, all block displacements having a near-maximum similarity score were stored. From this list, a single displacement vector for each block was iteratively selected which maximized the consistency of displacement vectors across immediately neighboring blocks. These selected displacements were regularized using a median filter before proceeding to registrations at finer image resolutions. After evaluating all image resolutions, the global rigid transform of the on-treatment image was computed using a Procrustes analysis, providing the couch shift for patient setup correction. This algorithm was evaluated for 18 locally advanced lung cancer patients, each with 4–7 weekly on-treatment computed tomography scans having physician-delineated gross tumor volumes. Volume overlap (VO) and border displacement errors (BDE) were calculated relative to the nominal physician-identified targets to establish residual error after registration. Results: Implementation of multiresolution registration improved block matching accuracy by 39% compared to registration using only the full resolution images. By also considering multiple potential displacements per block, initial errors were reduced by 65%. Using the final implementation of the BMR algorithm, VO was significantly improved from 77% ± 21% (range: 0%–100%) in the initial bony alignment to 91% ± 8% (range: 56%–100%;p < 0.001). Left

Incorporation of a laser range scanner into image-guided liver surgery: Surface acquisition, registration, and tracking

International Nuclear Information System (INIS)

Cash, David M.; Sinha, Tuhin K.; Chapman, William C.; Terawaki, Hiromi; Dawant, Benoit M.; Galloway, Robert L.; Miga, Michael I.

2003-01-01

As image guided surgical procedures become increasingly diverse, there will be more scenarios where point-based fiducials cannot be accurately localized for registration and rigid body assumptions no longer hold. As a result, procedures will rely more frequently on anatomical surfaces for the basis of image alignment and will require intraoperative geometric data to measure and compensate for tissue deformation in the organ. In this paper we outline methods for which a laser range scanner may be used to accomplish these tasks intraoperatively. A laser range scanner based on the optical principle of triangulation acquires a dense set of three-dimensional point data in a very rapid, noncontact fashion. Phantom studies were performed to test the ability to link range scan data with traditional modes of image-guided surgery data through localization, registration, and tracking in physical space. The experiments demonstrate that the scanner is capable of localizing point-based fiducials to within 0.2 mm and capable of achieving point and surface based registrations with target registration error of less than 2.0 mm. Tracking points in physical space with the range scanning system yields an error of 1.4±0.8 mm. Surface deformation studies were performed with the range scanner in order to determine if this device was capable of acquiring enough information for compensation algorithms. In the surface deformation studies, the range scanner was able to detect changes in surface shape due to deformation comparable to those detected by tomographic image studies. Use of the range scanner has been approved for clinical trials, and an initial intraoperative range scan experiment is presented. In all of these studies, the primary source of error in range scan data is deterministically related to the position and orientation of the surface within the scanner's field of view. However, this systematic error can be corrected, allowing the range scanner to provide a rapid, robust

Mapping of the prostate in endorectal coil-based MRI/MRSI and CT: A deformable registration and validation study

International Nuclear Information System (INIS)

Lian, J.; Xing, L.; Hunjan, S.; Dumoulin, C.; Levin, J.; Lo, A.; Watkins, R.; Rohling, K.; Giaquinto, R.; Kim, D.; Spielman, D.; Daniel, B.

2004-01-01

The endorectal coil is being increasingly used in magnetic resonance imaging (MRI) and MR spectroscopic imaging (MRSI) to obtain anatomic and metabolic images of the prostate with high signal-to-noise ratio (SNR). In practice, however, the use of endorectal probe inevitably distorts the prostate and other soft tissue organs, making the analysis and the use of the acquired image data in treatment planning difficult. The purpose of this work is to develop a deformable image registration algorithm to map the MRI/MRSI information obtained using an endorectal probe onto CT images and to verify the accuracy of the registration by phantom and patient studies. A mapping procedure involved using a thin plate spline (TPS) transformation was implemented to establish voxel-to-voxel correspondence between a reference image and a floating image with deformation. An elastic phantom with a number of implanted fiducial markers was designed for the validation of the quality of the registration. Radiographic images of the phantom were obtained before and after a series of intentionally introduced distortions. After mapping the distorted phantom to the original one, the displacements of the implanted markers were measured with respect to their ideal positions and the mean error was calculated. In patient studies, CT images of three prostate patients were acquired, followed by 3 Tesla (3 T) MR images with a rigid endorectal coil. Registration quality was estimated by the centroid position displacement and image coincidence index (CI). Phantom and patient studies show that TPS-based registration has achieved significantly higher accuracy than the previously reported method based on a rigid-body transformation and scaling. The technique should be useful to map the MR spectroscopic dataset acquired with ER probe onto the treatment planning CT dataset to guide radiotherapy planning

Image/patient registration from (partial) projection data by the Fourier phase matching method

International Nuclear Information System (INIS)

Weiguo Lu; You, J.

1999-01-01

A technique for 2D or 3D image/patient registration, PFPM (projection based Fourier phase matching method), is proposed. This technique provides image/patient registration directly from sequential tomographic projection data. The method can also deal with image files by generating 2D Radon transforms slice by slice. The registration in projection space is done by calculating a Fourier invariant (FI) descriptor for each one-dimensional projection datum, and then registering the FI descriptor by the Fourier phase matching (FPM) method. The algorithm has been tested on both synthetic and experimental data. When dealing with translated, rotated and uniformly scaled 2D image registration, the performance of the PFPM method is comparable to that of the IFPM (image based Fourier phase matching) method in robustness, efficiency, insensitivity to the offset between images, and registration time. The advantages of the former are that subpixel resolution is feasible, and it is more insensitive to image noise due to the averaging effect of the projection acquisition. Furthermore, the PFPM method offers the ability to generalize to 3D image/patient registration and to register partial projection data. By applying patient registration directly from tomographic projection data, image reconstruction is not needed in the therapy set-up verification, thus reducing computational time and artefacts. In addition, real time registration is feasible. Registration from partial projection data meets the geometry and dose requirements in many application cases and makes dynamic set-up verification possible in tomotherapy. (author)

Automatic image fusion of real-time ultrasound with computed tomography images: a prospective comparison between two auto-registration methods.

Science.gov (United States)

Cha, Dong Ik; Lee, Min Woo; Kim, Ah Yeong; Kang, Tae Wook; Oh, Young-Taek; Jeong, Ja-Yeon; Chang, Jung-Woo; Ryu, Jiwon; Lee, Kyong Joon; Kim, Jaeil; Bang, Won-Chul; Shin, Dong Kuk; Choi, Sung Jin; Koh, Dalkwon; Seo, Bong Koo; Kim, Kyunga

2017-11-01

Background A major drawback of conventional manual image fusion is that the process may be complex, especially for less-experienced operators. Recently, two automatic image fusion techniques called Positioning and Sweeping auto-registration have been developed. Purpose To compare the accuracy and required time for image fusion of real-time ultrasonography (US) and computed tomography (CT) images between Positioning and Sweeping auto-registration. Material and Methods Eighteen consecutive patients referred for planning US for radiofrequency ablation or biopsy for focal hepatic lesions were enrolled. Image fusion using both auto-registration methods was performed for each patient. Registration error, time required for image fusion, and number of point locks used were compared using the Wilcoxon signed rank test. Results Image fusion was successful in all patients. Positioning auto-registration was significantly faster than Sweeping auto-registration for both initial (median, 11 s [range, 3-16 s] vs. 32 s [range, 21-38 s]; P auto-registration was significantly higher for initial image fusion (median, 38.8 mm [range, 16.0-84.6 mm] vs. 18.2 mm [6.7-73.4 mm]; P = 0.029), but not for complete image fusion (median, 4.75 mm [range, 1.7-9.9 mm] vs. 5.8 mm [range, 2.0-13.0 mm]; P = 0.338]. Number of point locks required to refine the initially fused images was significantly higher with Positioning auto-registration (median, 2 [range, 2-3] vs. 1 [range, 1-2]; P = 0.012]. Conclusion Positioning auto-registration offers faster image fusion between real-time US and pre-procedural CT images than Sweeping auto-registration. The final registration error is similar between the two methods.

Integration of PET-CT and cone-beam CT for image-guided radiotherapy with high image quality and registration accuracy

Science.gov (United States)

Wu, T.-H.; Liang, C.-H.; Wu, J.-K.; Lien, C.-Y.; Yang, B.-H.; Huang, Y.-H.; Lee, J. J. S.

2009-07-01

Hybrid positron emission tomography-computed tomography (PET-CT) system enhances better differentiation of tissue uptake of 18F-fluorodeoxyglucose (18F-FDG) and provides much more diagnostic value in the non-small-cell lung cancer and nasopharyngeal carcinoma (NPC). In PET-CT, high quality CT images not only offer diagnostic value on anatomic delineation of the tissues but also shorten the acquisition time for attenuation correction (AC) compared with PET-alone imaging. The linear accelerators equipped with the X-ray cone-beam computed tomography (CBCT) imaging system for image-guided radiotherapy (IGRT) provides excellent verification on position setup error. The purposes of our study were to optimize the CT acquisition protocols of PET-CT and to integrate the PET-CT and CBCT for IGRT. The CT imaging parameters were modified in PET-CT for increasing the image quality in order to enhance the diagnostic value on tumour delineation. Reproducibility and registration accuracy via bone co-registration algorithm between the PET-CT and CBCT were evaluated by using a head phantom to simulate a head and neck treatment condition. Dose measurement in computed tomography dose index (CTDI) was also estimated. Optimization of the CT acquisition protocols of PET-CT was feasible in this study. Co-registration accuracy between CBCT and PET-CT on axial and helical modes was in the range of 1.06 to 2.08 and 0.99 to 2.05 mm, respectively. In our result, it revealed that the accuracy of the co-registration with CBCT on helical mode was more accurate than that on axial mode. Radiation doses in CTDI were 4.76 to 18.5 mGy and 4.83 to 18.79 mGy on axial and helical modes, respectively. Registration between PET-CT and CBCT is a state-of-the-art registration technology which could provide much information on diagnosis and accurate tumour contouring on radiotherapy while implementing radiotherapy procedures. This novelty technology of PET-CT and cone-beam CT integration for IGRT may have a

Integration of PET-CT and cone-beam CT for image-guided radiotherapy with high image quality and registration accuracy

International Nuclear Information System (INIS)

Wu, T-H; Liang, C-H; Wu, J-K; Lien, C-Y; Yang, B-H; Lee, J J S; Huang, Y-H

2009-01-01

Hybrid positron emission tomography-computed tomography (PET-CT) system enhances better differentiation of tissue uptake of 18 F-fluorodeoxyglucose ( 18 F-FDG) and provides much more diagnostic value in the non-small-cell lung cancer and nasopharyngeal carcinoma (NPC). In PET-CT, high quality CT images not only offer diagnostic value on anatomic delineation of the tissues but also shorten the acquisition time for attenuation correction (AC) compared with PET-alone imaging. The linear accelerators equipped with the X-ray cone-beam computed tomography (CBCT) imaging system for image-guided radiotherapy (IGRT) provides excellent verification on position setup error. The purposes of our study were to optimize the CT acquisition protocols of PET-CT and to integrate the PET-CT and CBCT for IGRT. The CT imaging parameters were modified in PET-CT for increasing the image quality in order to enhance the diagnostic value on tumour delineation. Reproducibility and registration accuracy via bone co-registration algorithm between the PET-CT and CBCT were evaluated by using a head phantom to simulate a head and neck treatment condition. Dose measurement in computed tomography dose index (CTDI) was also estimated. Optimization of the CT acquisition protocols of PET-CT was feasible in this study. Co-registration accuracy between CBCT and PET-CT on axial and helical modes was in the range of 1.06 to 2.08 and 0.99 to 2.05 mm, respectively. In our result, it revealed that the accuracy of the co-registration with CBCT on helical mode was more accurate than that on axial mode. Radiation doses in CTDI were 4.76 to 18.5 mGy and 4.83 to 18.79 mGy on axial and helical modes, respectively. Registration between PET-CT and CBCT is a state-of-the-art registration technology which could provide much information on diagnosis and accurate tumour contouring on radiotherapy while implementing radiotherapy procedures. This novelty technology of PET-CT and cone-beam CT integration for IGRT may have a

Quality Assurance of Serial 3D Image Registration, Fusion, and Segmentation

International Nuclear Information System (INIS)

Sharpe, Michael; Brock, Kristy K.

2008-01-01

Radiotherapy relies on images to plan, guide, and assess treatment. Image registration, fusion, and segmentation are integral to these processes; specifically for aiding anatomic delineation, assessing organ motion, and aligning targets with treatment beams in image-guided radiation therapy (IGRT). Future developments in image registration will also improve estimations of the actual dose delivered and quantitative assessment in patient follow-up exams. This article summarizes common and emerging technologies and reviews the role of image registration, fusion, and segmentation in radiotherapy processes. The current quality assurance practices are summarized, and implications for clinical procedures are discussed

Historical Image Registration and Land-Use Land-Cover Change Analysis

Directory of Open Access Journals (Sweden)

Fang-Ju Jao

2014-12-01

Full Text Available Historical aerial images are important to retain past ground surface information. The land-use land-cover change in the past can be identified using historical aerial images. Automatic historical image registration and stitching is essential because the historical image pose information was usually lost. In this study, the Scale Invariant Feature Transform (SIFT algorithm was used for feature extraction. Subsequently, the present study used the automatic affine transformation algorithm for historical image registration, based on SIFT features and control points. This study automatically determined image affine parameters and simultaneously transformed from an image coordinate system to a ground coordinate system. After historical aerial image registration, the land-use land-cover change was analyzed between two different years (1947 and 1975 at the Tseng Wen River estuary. Results show that sandbars and water zones were transformed into a large number of fish ponds between 1947 and 1975.

Automated Registration Of Images From Multiple Sensors

Science.gov (United States)

Rignot, Eric J. M.; Kwok, Ronald; Curlander, John C.; Pang, Shirley S. N.

1994-01-01

Images of terrain scanned in common by multiple Earth-orbiting remote sensors registered automatically with each other and, where possible, on geographic coordinate grid. Simulated image of terrain viewed by sensor computed from ancillary data, viewing geometry, and mathematical model of physics of imaging. In proposed registration algorithm, simulated and actual sensor images matched by area-correlation technique.

CO-REGISTRATION AIRBORNE LIDAR POINT CLOUD DATA AND SYNCHRONOUS DIGITAL IMAGE REGISTRATION BASED ON COMBINED ADJUSTMENT

Directory of Open Access Journals (Sweden)

Z. H. Yang

2016-06-01

Full Text Available Aim at the problem of co-registration airborne laser point cloud data with the synchronous digital image, this paper proposed a registration method based on combined adjustment. By integrating tie point, point cloud data with elevation constraint pseudo observations, using the principle of least-squares adjustment to solve the corrections of exterior orientation elements of each image, high-precision registration results can be obtained. In order to ensure the reliability of the tie point, and the effectiveness of pseudo observations, this paper proposed a point cloud data constrain SIFT matching and optimizing method, can ensure that the tie points are located on flat terrain area. Experiments with the airborne laser point cloud data and its synchronous digital image, there are about 43 pixels error in image space using the original POS data. If only considering the bore-sight of POS system, there are still 1.3 pixels error in image space. The proposed method regards the corrections of the exterior orientation elements of each image as unknowns and the errors are reduced to 0.15 pixels.

Multimodal Registration of gated cardiac PET, CT and MR sequences

International Nuclear Information System (INIS)

Baty, X.

2007-07-01

The research described in this manuscript deals with the multimodal registration of cardiac images from Magnetic Resonance Imaging (MRI), Position Emission Tomography (PET) and Computerized Tomography (CT). All these modalities are gated to the Electrocardiogram (ECG) and provide information to evaluate cardiac function, and to diagnose and to follow-up cardiovascular pathologies. PET imaging allows the evaluation of ventricular function and MRI is a gold standard for the study of the left ventricular function. The goal of our registration process is to merge functional (from PET) and anatomical images (from CT and MRI). Our process is adapted to the modalities used and is divided in two steps: (i) a global rigid 3-dimensional model-based ICP (Iterative Closest Point) registration between CT and MR data and (ii) an iconic 2-dimensional registration based on Free Form Deformations and Mutual Information. This last step presents an original contribution by using a composite image of CT (which presents epicardic contours) and PET (where endocardic contours are partially visible) data to make mutual information more accurate in representing the similarity with the MR data. To speed up the whole process, we also present a transformation initialization scheme using displacement field obtained form MR data only. The obtained results have been evaluated by experts. (author)

Determination of optimal ultrasound planes for the initialisation of image registration during endoscopic ultrasound-guided procedures.

Science.gov (United States)

Bonmati, Ester; Hu, Yipeng; Gibson, Eli; Uribarri, Laura; Keane, Geri; Gurusami, Kurinchi; Davidson, Brian; Pereira, Stephen P; Clarkson, Matthew J; Barratt, Dean C

2018-06-01

Navigation of endoscopic ultrasound (EUS)-guided procedures of the upper gastrointestinal (GI) system can be technically challenging due to the small fields-of-view of ultrasound and optical devices, as well as the anatomical variability and limited number of orienting landmarks during navigation. Co-registration of an EUS device and a pre-procedure 3D image can enhance the ability to navigate. However, the fidelity of this contextual information depends on the accuracy of registration. The purpose of this study was to develop and test the feasibility of a simulation-based planning method for pre-selecting patient-specific EUS-visible anatomical landmark locations to maximise the accuracy and robustness of a feature-based multimodality registration method. A registration approach was adopted in which landmarks are registered to anatomical structures segmented from the pre-procedure volume. The predicted target registration errors (TREs) of EUS-CT registration were estimated using simulated visible anatomical landmarks and a Monte Carlo simulation of landmark localisation error. The optimal planes were selected based on the 90th percentile of TREs, which provide a robust and more accurate EUS-CT registration initialisation. The method was evaluated by comparing the accuracy and robustness of registrations initialised using optimised planes versus non-optimised planes using manually segmented CT images and simulated ([Formula: see text]) or retrospective clinical ([Formula: see text]) EUS landmarks. The results show a lower 90th percentile TRE when registration is initialised using the optimised planes compared with a non-optimised initialisation approach (p value [Formula: see text]). The proposed simulation-based method to find optimised EUS planes and landmarks for EUS-guided procedures may have the potential to improve registration accuracy. Further work will investigate applying the technique in a clinical setting.

Feature and Intensity Based Medical Image Registration Using Particle Swarm Optimization.

Science.gov (United States)

Abdel-Basset, Mohamed; Fakhry, Ahmed E; El-Henawy, Ibrahim; Qiu, Tie; Sangaiah, Arun Kumar

2017-11-03

Image registration is an important aspect in medical image analysis, and kinds use in a variety of medical applications. Examples include diagnosis, pre/post surgery guidance, comparing/merging/integrating images from multi-modal like Magnetic Resonance Imaging (MRI), and Computed Tomography (CT). Whether registering images across modalities for a single patient or registering across patients for a single modality, registration is an effective way to combine information from different images into a normalized frame for reference. Registered datasets can be used for providing information relating to the structure, function, and pathology of the organ or individual being imaged. In this paper a hybrid approach for medical images registration has been developed. It employs a modified Mutual Information (MI) as a similarity metric and Particle Swarm Optimization (PSO) method. Computation of mutual information is modified using a weighted linear combination of image intensity and image gradient vector flow (GVF) intensity. In this manner, statistical as well as spatial image information is included into the image registration process. Maximization of the modified mutual information is effected using the versatile Particle Swarm Optimization which is developed easily with adjusted less parameter. The developed approach has been tested and verified successfully on a number of medical image data sets that include images with missing parts, noise contamination, and/or of different modalities (CT, MRI). The registration results indicate the proposed model as accurate and effective, and show the posture contribution in inclusion of both statistical and spatial image data to the developed approach.

Automated dental implantation using image-guided robotics: registration results.

Science.gov (United States)

Sun, Xiaoyan; McKenzie, Frederic D; Bawab, Sebastian; Li, Jiang; Yoon, Yongki; Huang, Jen-K

2011-09-01

One of the most important factors affecting the outcome of dental implantation is the accurate insertion of the implant into the patient's jaw bone, which requires a high degree of anatomical accuracy. With the accuracy and stability of robots, image-guided robotics is expected to provide more reliable and successful outcomes for dental implantation. Here, we proposed the use of a robot for drilling the implant site in preparation for the insertion of the implant. An image-guided robotic system for automated dental implantation is described in this paper. Patient-specific 3D models are reconstructed from preoperative Cone-beam CT images, and implantation planning is performed with these virtual models. A two-step registration procedure is applied to transform the preoperative plan of the implant insertion into intra-operative operations of the robot with the help of a Coordinate Measurement Machine (CMM). Experiments are carried out with a phantom that is generated from the patient-specific 3D model. Fiducial Registration Error (FRE) and Target Registration Error (TRE) values are calculated to evaluate the accuracy of the registration procedure. FRE values are less than 0.30 mm. Final TRE values after the two-step registration are 1.42 ± 0.70 mm (N = 5). The registration results of an automated dental implantation system using image-guided robotics are reported in this paper. Phantom experiments show that the practice of robot in the dental implantation is feasible and the system accuracy is comparable to other similar systems for dental implantation.

SU-E-I-66: Radiomics and Image Registration Updates for the Computational Environment for Radiotherapy Research (CERR)

Energy Technology Data Exchange (ETDEWEB)

Apte, A; Wang, Y; Deasy, J [Memorial Sloan Kettering Cancer Center, NY, NY (United States)

2014-06-01

Purpose: To present new tools in CERR for Radiomics, image registration and other software updates and additions. Methods: Radiomics: CERR supports generating 3-D texture metrics based on gray scale co-occurance. Two new ways to calculate texture features were added: (1) Local Texture Averaging: Local texture is calculated around a voxel within the userdefined bounding box. The final texture metrics are the average of local textures for all the voxels. This is useful to detect any local texture patterns within an image. (2) Image Smoothing: A convolution ball of user-defined radius is rolled over an image to smooth out artifacts. The texture metrics are then computed on the smooth image. Image Registration: (1) Support was added to import deformation vector fields as well as non-deformable transformation matrices generated by vendor software and stored in standard DICOM format. (2) Support was added to use image within masks while computing image deformations. CT to MR registration is supported. This registration uses morphological edge information within the images to guide the deformation process. In addition to these features, other noteworthy additions to CERR include (1) Irregularly shaped ROI: This is done by taking intersection between infinitely extended irregular polygons drawn on any of the two views. Such an ROI is more conformal and useful in avoiding any unwanted parts of images that cannot be avoided with the conventional cubic box. The ROI is useful to generate Radiomics metrics. (2) Ability to insert RTDOSE in DICOM format to existing CERR plans. (3) Ability to import multi-frame PET-CT and SPECT-CT while maintaining spatial registration between the two modalities. (4) Ability to compile CERR on Unix-like systems. Results: The new features and updates are available via https://www.github.com/adityaapte/cerr . Conclusion: Features added to CERR increase its utility in Radiomics, Image-Registration and Outcomes modeling.

SU-E-I-66: Radiomics and Image Registration Updates for the Computational Environment for Radiotherapy Research (CERR)

International Nuclear Information System (INIS)

Apte, A; Wang, Y; Deasy, J

2014-01-01

Purpose: To present new tools in CERR for Radiomics, image registration and other software updates and additions. Methods: Radiomics: CERR supports generating 3-D texture metrics based on gray scale co-occurance. Two new ways to calculate texture features were added: (1) Local Texture Averaging: Local texture is calculated around a voxel within the userdefined bounding box. The final texture metrics are the average of local textures for all the voxels. This is useful to detect any local texture patterns within an image. (2) Image Smoothing: A convolution ball of user-defined radius is rolled over an image to smooth out artifacts. The texture metrics are then computed on the smooth image. Image Registration: (1) Support was added to import deformation vector fields as well as non-deformable transformation matrices generated by vendor software and stored in standard DICOM format. (2) Support was added to use image within masks while computing image deformations. CT to MR registration is supported. This registration uses morphological edge information within the images to guide the deformation process. In addition to these features, other noteworthy additions to CERR include (1) Irregularly shaped ROI: This is done by taking intersection between infinitely extended irregular polygons drawn on any of the two views. Such an ROI is more conformal and useful in avoiding any unwanted parts of images that cannot be avoided with the conventional cubic box. The ROI is useful to generate Radiomics metrics. (2) Ability to insert RTDOSE in DICOM format to existing CERR plans. (3) Ability to import multi-frame PET-CT and SPECT-CT while maintaining spatial registration between the two modalities. (4) Ability to compile CERR on Unix-like systems. Results: The new features and updates are available via https://www.github.com/adityaapte/cerr . Conclusion: Features added to CERR increase its utility in Radiomics, Image-Registration and Outcomes modeling

Registration of SPECT, PET and/or X-ray CT images in patients with lung cancer

International Nuclear Information System (INIS)

Uemura, K.; Toyama, H.; Miyamoto, T.; Yoshikawa, K.; Mori, Y.

2002-01-01

Aim: In order to evaluate the therapeutic gain of heavy ion therapy performed on patients with lung cancer, the regional pulmonary functions and the amount of radio tracer accumulation to the tumor, we are investigated by using the region of interest based on anatomical information obtained from X-ray CT. There are many registration techniques for brain images, but not so much for the other organ images that we have studied registration of chest SPECT, PET and/or X-ray CT images. Materials and Methods: Perfusion, ventilation and blood pool images with Tc 99m labeled radiopharmaceuticals and SPECT, tumor images with 11 C-methionine and PET and X-ray CT scans were performed on several patients with lung cancer before and after heavy ion therapy. The registrations of SPECT-CT, PET-CT and CT-CT were performed by using AMIR (Automatic Multimodality Image Registration), which was developed by Babak et al. for registration of brain images. In a case of SPECT-CT registration, each of the three functional images was registered to the X-ray CT image, and the accuracy of each registration was compared. In the studies of PET-CT registration, the transmission images and X-ray CT images were registered at first, because the 11 C-methionine PET images bear little resemblance to the underlying anatomical images. Next, the emission images were realigned by using the same registration parameters. The X-ray CT images obtained from a single subject at the different time were registered to the first X-ray CT images, respectively. Results: In the SPECT-CT registration, the blood pool-CT registration is the best among three SPECT images in visual inspection by radiologists. In the PET-CT registration, the Transmission-CT registrations got good results. Therefore, Emission-CT registrations also got good results. In the CT-CT registration, the X-ray CT images obtained from a single subject at the different time were superimposed well each other except for lower lobe. As the results, it was