A system for the registration of arthroscopic images to magnetic resonance images of the knee: for improved virtual knee arthroscopy

Science.gov (United States)

Hu, Chengliang; Amati, Giancarlo; Gullick, Nicola; Oakley, Stephen; Hurmusiadis, Vassilios; Schaeffter, Tobias; Penney, Graeme; Rhode, Kawal

2009-02-01

Knee arthroscopy is a minimally invasive procedure that is routinely carried out for the diagnosis and treatment of pathologies of the knee joint. A high level of expertise is required to carry out this procedure and therefore the clinical training is extensive. There are several reasons for this that include the small field of view seen by the arthroscope and the high degree of distortion in the video images. Several virtual arthroscopy simulators have been proposed to augment the learning process. One of the limitations of these simulators is the generic models that are used. We propose to develop a new virtual arthroscopy simulator that will allow the use of pathology-specific models with an increased level of photo-realism. In order to generate these models we propose to use registered magnetic resonance images (MRI) and arthroscopic video images collected from patients with a variety of knee pathologies. We present a method to perform this registration based on the use of a combined X-ray and MR imaging system (XMR). In order to validate our technique we carried out MR imaging and arthroscopy of a custom-made acrylic phantom in the XMR environment. The registration between the two modalities was computed using a combination of XMR and camera calibration, and optical tracking. Both two-dimensional (2D) and three-dimensional (3D) registration errors were computed and shown to be approximately 0.8 and 3 mm, respectively. Further to this, we qualitatively tested our approach using a more realistic plastic knee model that is used for the arthroscopy training.

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

Elastix : a toolbox for intensity-based medical image registration

NARCIS (Netherlands)

Klein, S.; Staring, M.; Murphy, K.; Viergever, M.A.; Pluim, J.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

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)

MRI and CBCT image registration of temporomandibular joint: a systematic review.

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Al-Saleh, Mohammed A Q; Alsufyani, Noura A; Saltaji, Humam; Jaremko, Jacob L; Major, Paul W

2016-05-10

The purpose of the present review is to systematically and critically analyze the available literature regarding the importance, applicability, and practicality of (MRI), computerized tomography (CT) or cone-beam CT (CBCT) image registration for TMJ anatomy and assessment. A systematic search of 4 databases; MEDLINE, EMBASE, EBM reviews and Scopus, was conducted by 2 reviewers. An additional manual search of the bibliography was performed. All articles discussing the magnetic resonance imaging MRI and CT or CBCT image registration for temporomandibular joint (TMJ) visualization or assessment were included. Only 3 articles satisfied the inclusion criteria. All included articles were published within the last 7 years. Two articles described MRI to CT multimodality image registration as a complementary tool to visualize TMJ. Both articles used images of one patient only to introduce the complementary concept of MRI-CT fused image. One article assessed the reliability of using MRI-CBCT registration to evaluate the TMJ disc position and osseous pathology for 10 temporomandibular disorder (TMD) patients. There are very limited studies of MRI-CT/CBCT registration to reach a conclusion regarding its accuracy or clinical use in the temporomandibular joints.

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.

Semiautomated Multimodal Breast Image Registration

Directory of Open Access Journals (Sweden)

Charlotte Curtis

2012-01-01

However, due to the highly deformable nature of breast tissues, comparison of 3D and 2D modalities is a challenge. To enable this comparison, a registration technique was developed to map features from 2D mammograms to locations in the 3D image space. This technique was developed and tested using magnetic resonance (MR images as a reference 3D modality, as MR breast imaging is an established technique in clinical practice. The algorithm was validated using a numerical phantom then successfully tested on twenty-four image pairs. Dice's coefficient was used to measure the external goodness of fit, resulting in an excellent overall average of 0.94. Internal agreement was evaluated by examining internal features in consultation with a radiologist, and subjective assessment concludes that reasonable alignment was achieved.

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

INVITED REVIEW--IMAGE REGISTRATION IN VETERINARY RADIATION ONCOLOGY: INDICATIONS, IMPLICATIONS, AND FUTURE ADVANCES.

Science.gov (United States)

Feng, Yang; Lawrence, Jessica; Cheng, Kun; Montgomery, Dean; Forrest, Lisa; Mclaren, Duncan B; McLaughlin, Stephen; Argyle, David J; Nailon, William H

2016-01-01

The field of veterinary radiation therapy (RT) has gained substantial momentum in recent decades with significant advances in conformal treatment planning, image-guided radiation therapy (IGRT), and intensity-modulated (IMRT) techniques. At the root of these advancements lie improvements in tumor imaging, image alignment (registration), target volume delineation, and identification of critical structures. Image registration has been widely used to combine information from multimodality images such as computerized tomography (CT), magnetic resonance imaging (MRI), and positron emission tomography (PET) to improve the accuracy of radiation delivery and reliably identify tumor-bearing areas. Many different techniques have been applied in image registration. This review provides an overview of medical image registration in RT and its applications in veterinary oncology. A summary of the most commonly used approaches in human and veterinary medicine is presented along with their current use in IGRT and adaptive radiation therapy (ART). It is important to realize that registration does not guarantee that target volumes, such as the gross tumor volume (GTV), are correctly identified on the image being registered, as limitations unique to registration algorithms exist. Research involving novel registration frameworks for automatic segmentation of tumor volumes is ongoing and comparative oncology programs offer a unique opportunity to test the efficacy of proposed algorithms. © 2016 American College of Veterinary Radiology.

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

Physiological basis and image processing in functional magnetic resonance imaging: Neuronal and motor activity in brain

Directory of Open Access Journals (Sweden)

Sharma Rakesh

2004-05-01

Full Text Available Abstract Functional magnetic resonance imaging (fMRI is recently developing as imaging modality used for mapping hemodynamics of neuronal and motor event related tissue blood oxygen level dependence (BOLD in terms of brain activation. Image processing is performed by segmentation and registration methods. Segmentation algorithms provide brain surface-based analysis, automated anatomical labeling of cortical fields in magnetic resonance data sets based on oxygen metabolic state. Registration algorithms provide geometric features using two or more imaging modalities to assure clinically useful neuronal and motor information of brain activation. This review article summarizes the physiological basis of fMRI signal, its origin, contrast enhancement, physical factors, anatomical labeling by segmentation, registration approaches with examples of visual and motor activity in brain. Latest developments are reviewed for clinical applications of fMRI along with other different neurophysiological and imaging modalities.

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.

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.

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

Topology preserving non-rigid image registration using time-varying elasticity model for MRI brain volumes.

Science.gov (United States)

Ahmad, Sahar; Khan, Muhammad Faisal

2015-12-01

In this paper, we present a new non-rigid image registration method that imposes a topology preservation constraint on the deformation. We propose to incorporate the time varying elasticity model into the deformable image matching procedure and constrain the Jacobian determinant of the transformation over the entire image domain. The motion of elastic bodies is governed by a hyperbolic partial differential equation, generally termed as elastodynamics wave equation, which we propose to use as a deformation model. We carried out clinical image registration experiments on 3D magnetic resonance brain scans from IBSR database. The results of the proposed registration approach in terms of Kappa index and relative overlap computed over the subcortical structures were compared against the existing topology preserving non-rigid image registration methods and non topology preserving variant of our proposed registration scheme. The Jacobian determinant maps obtained with our proposed registration method were qualitatively and quantitatively analyzed. The results demonstrated that the proposed scheme provides good registration accuracy with smooth transformations, thereby guaranteeing the preservation of topology. Copyright © 2015 Elsevier Ltd. All rights reserved.

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)

A prospective comparison between auto-registration and manual registration of real-time ultrasound with MR images for percutaneous ablation or biopsy of hepatic lesions.

Science.gov (United States)

Cha, Dong Ik; Lee, Min Woo; Song, Kyoung Doo; 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

2017-06-01

To compare the accuracy and required time for image fusion of real-time ultrasound (US) with pre-procedural magnetic resonance (MR) images between positioning auto-registration and manual registration for percutaneous radiofrequency ablation or biopsy of hepatic lesions. This prospective study was approved by the institutional review board, and all patients gave written informed consent. Twenty-two patients (male/female, n = 18/n = 4; age, 61.0 ± 7.7 years) who were referred for planning US to assess the feasibility of radiofrequency ablation (n = 21) or biopsy (n = 1) for focal hepatic lesions were included. One experienced radiologist performed the two types of image fusion methods in each patient. The performance of auto-registration and manual registration was evaluated. The accuracy of the two methods, based on measuring registration error, and the time required for image fusion for both methods were recorded using in-house software and respectively compared using the Wilcoxon signed rank test. Image fusion was successful in all patients. The registration error was not significantly different between the two methods (auto-registration: median, 3.75 mm; range, 1.0-15.8 mm vs. manual registration: median, 2.95 mm; range, 1.2-12.5 mm, p = 0.242). The time required for image fusion was significantly shorter with auto-registration than with manual registration (median, 28.5 s; range, 18-47 s, vs. median, 36.5 s; range, 14-105 s, p = 0.026). Positioning auto-registration showed promising results compared with manual registration, with similar accuracy and even shorter registration time.

Biomechanical modeling constrained surface-based image registration for prostate MR guided TRUS biopsy

NARCIS (Netherlands)

Ven, W.J.M. van de; Hu, Y.; Barentsz, J.O.; Karssemeijer, N.; Barratt, D.; Huisman, H.J.

2015-01-01

Adding magnetic resonance (MR)-derived information to standard transrectal ultrasound (TRUS) images for guiding prostate biopsy is of substantial clinical interest. A tumor visible on MR images can be projected on ultrasound (US) by using MR-US registration. A common approach is to use surface-based

The use of atlas registration and graph cuts for prostate segmentation in magnetic resonance images

Energy Technology Data Exchange (ETDEWEB)

Korsager, Anne Sofie, E-mail: asko@hst.aau.dk; Østergaard, Lasse Riis [Department of Health Science and Technology, Aalborg University, Aalborg 9220 (Denmark); Fortunati, Valerio; Lijn, Fedde van der; Niessen, Wiro; Walsum, Theo van [Biomedical Imaging Group of Rotterdam, Department of Medical Informatics and Radiology, Erasmus MC, Rotterdam 3015 GE Rotterdam (Netherlands); Carl, Jesper [Department of Medical Physics, Oncology, Aalborg University Hospital, Aalborg 9220 (Denmark)

2015-04-15

Purpose: An automatic method for 3D prostate segmentation in magnetic resonance (MR) images is presented for planning image-guided radiotherapy treatment of prostate cancer. Methods: A spatial prior based on intersubject atlas registration is combined with organ-specific intensity information in a graph cut segmentation framework. The segmentation is tested on 67 axial T{sub 2}-weighted MR images in a leave-one-out cross validation experiment and compared with both manual reference segmentations and with multiatlas-based segmentations using majority voting atlas fusion. The impact of atlas selection is investigated in both the traditional atlas-based segmentation and the new graph cut method that combines atlas and intensity information in order to improve the segmentation accuracy. Best results were achieved using the method that combines intensity information, shape information, and atlas selection in the graph cut framework. Results: A mean Dice similarity coefficient (DSC) of 0.88 and a mean surface distance (MSD) of 1.45 mm with respect to the manual delineation were achieved. Conclusions: This approaches the interobserver DSC of 0.90 and interobserver MSD 0f 1.15 mm and is comparable to other studies performing prostate segmentation in MR.

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

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.

Comparison of manual vs. automated multimodality (CT-MRI) image registration for brain tumors

International Nuclear Information System (INIS)

Sarkar, Abhirup; Santiago, Roberto J.; Smith, Ryan; Kassaee, Alireza

2005-01-01

Computed tomgoraphy-magnetic resonance imaging (CT-MRI) registrations are routinely used for target-volume delineation of brain tumors. We clinically use 2 software packages based on manual operation and 1 automated package with 2 different algorithms: chamfer matching using bony structures, and mutual information using intensity patterns. In all registration algorithms, a minimum of 3 pairs of identical anatomical and preferably noncoplanar landmarks is used on each of the 2 image sets. In manual registration, the program registers these points and links the image sets using a 3-dimensional (3D) transformation. In automated registration, the 3 landmarks are used as an initial starting point and further processing is done to complete the registration. Using our registration packages, registration of CT and MRI was performed on 10 patients. We scored the results of each registration set based on the amount of time spent, the accuracy reported by the software, and a final evaluation. We evaluated each software program by measuring the residual error between 'matched' points on the right and left globes and the posterior fossa for fused image slices. In general, manual registration showed higher misalignment between corresponding points compared to automated registration using intensity matching. This error had no directional dependence and was, most of the time, larger for a larger structure in both registration techniques. Automated algorithm based on intensity matching also gave the best results in terms of registration accuracy, irrespective of whether or not the initial landmarks were chosen carefully, when compared to that done using bone matching algorithm. Intensity-matching algorithm required the least amount of user-time and provided better accuracy

Prostate multimodality image registration based on B-splines and quadrature local energy.

Science.gov (United States)

Mitra, Jhimli; Martí, Robert; Oliver, Arnau; Lladó, Xavier; Ghose, Soumya; Vilanova, Joan C; Meriaudeau, Fabrice

2012-05-01

Needle biopsy of the prostate is guided by Transrectal Ultrasound (TRUS) imaging. The TRUS images do not provide proper spatial localization of malignant tissues due to the poor sensitivity of TRUS to visualize early malignancy. Magnetic Resonance Imaging (MRI) has been shown to be sensitive for the detection of early stage malignancy, and therefore, a novel 2D deformable registration method that overlays pre-biopsy MRI onto TRUS images has been proposed. The registration method involves B-spline deformations with Normalized Mutual Information (NMI) as the similarity measure computed from the texture images obtained from the amplitude responses of the directional quadrature filter pairs. Registration accuracy of the proposed method is evaluated by computing the Dice Similarity coefficient (DSC) and 95% Hausdorff Distance (HD) values for 20 patients prostate mid-gland slices and Target Registration Error (TRE) for 18 patients only where homologous structures are visible in both the TRUS and transformed MR images. The proposed method and B-splines using NMI computed from intensities provide average TRE values of 2.64 ± 1.37 and 4.43 ± 2.77 mm respectively. Our method shows statistically significant improvement in TRE when compared with B-spline using NMI computed from intensities with Student's t test p = 0.02. The proposed method shows 1.18 times improvement over thin-plate splines registration with average TRE of 3.11 ± 2.18 mm. The mean DSC and the mean 95% HD values obtained with the proposed method of B-spline with NMI computed from texture are 0.943 ± 0.039 and 4.75 ± 2.40 mm respectively. The texture energy computed from the quadrature filter pairs provides better registration accuracy for multimodal images than raw intensities. Low TRE values of the proposed registration method add to the feasibility of it being used during TRUS-guided biopsy.

Constrained non-rigid registration for whole body image registration: method and validation

Science.gov (United States)

Li, Xia; Yankeelov, Thomas E.; Peterson, Todd E.; Gore, John C.; Dawant, Benoit M.

2007-03-01

3D intra- and inter-subject registration of image volumes is important for tasks that include measurements and quantification of temporal/longitudinal changes, atlas-based segmentation, deriving population averages, or voxel and tensor-based morphometry. A number of methods have been proposed to tackle this problem but few of them have focused on the problem of registering whole body image volumes acquired either from humans or small animals. These image volumes typically contain a large number of articulated structures, which makes registration more difficult than the registration of head images, to which the vast majority of registration algorithms have been applied. To solve this problem, we have previously proposed an approach, which initializes an intensity-based non-rigid registration algorithm with a point based registration technique [1, 2]. In this paper, we introduce new constraints into our non-rigid registration algorithm to prevent the bones from being deformed inaccurately. Results we have obtained show that the new constrained algorithm leads to better registration results than the previous one.

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..

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

Graphics Processing Unit-Accelerated Nonrigid Registration of MR Images to CT Images During CT-Guided Percutaneous Liver Tumor Ablations.

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Tokuda, Junichi; Plishker, William; Torabi, Meysam; Olubiyi, Olutayo I; Zaki, George; Tatli, Servet; Silverman, Stuart G; Shekher, Raj; Hata, Nobuhiko

2015-06-01

Accuracy and speed are essential for the intraprocedural nonrigid magnetic resonance (MR) to computed tomography (CT) image registration in the assessment of tumor margins during CT-guided liver tumor ablations. Although both accuracy and speed can be improved by limiting the registration to a region of interest (ROI), manual contouring of the ROI prolongs the registration process substantially. To achieve accurate and fast registration without the use of an ROI, we combined a nonrigid registration technique on the basis of volume subdivision with hardware acceleration using a graphics processing unit (GPU). We compared the registration accuracy and processing time of GPU-accelerated volume subdivision-based nonrigid registration technique to the conventional nonrigid B-spline registration technique. Fourteen image data sets of preprocedural MR and intraprocedural CT images for percutaneous CT-guided liver tumor ablations were obtained. Each set of images was registered using the GPU-accelerated volume subdivision technique and the B-spline technique. Manual contouring of ROI was used only for the B-spline technique. Registration accuracies (Dice similarity coefficient [DSC] and 95% Hausdorff distance [HD]) and total processing time including contouring of ROIs and computation were compared using a paired Student t test. Accuracies of the GPU-accelerated registrations and B-spline registrations, respectively, were 88.3 ± 3.7% versus 89.3 ± 4.9% (P = .41) for DSC and 13.1 ± 5.2 versus 11.4 ± 6.3 mm (P = .15) for HD. Total processing time of the GPU-accelerated registration and B-spline registration techniques was 88 ± 14 versus 557 ± 116 seconds (P processing time. The GPU-accelerated volume subdivision technique may enable the implementation of nonrigid registration into routine clinical practice. Copyright © 2015 AUR. 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.

3D non-rigid surface-based MR-TRUS registration for image-guided prostate biopsy

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Sun, Yue; Qiu, Wu; Romagnoli, Cesare; Fenster, Aaron

2014-03-01

Two dimensional (2D) transrectal ultrasound (TRUS) guided prostate biopsy is the standard approach for definitive diagnosis of prostate cancer (PCa). However, due to the lack of image contrast of prostate tumors needed to clearly visualize early-stage PCa, prostate biopsy often results in false negatives, requiring repeat biopsies. Magnetic Resonance Imaging (MRI) has been considered to be a promising imaging modality for noninvasive identification of PCa, since it can provide a high sensitivity and specificity for the detection of early stage PCa. Our main objective is to develop and validate a registration method of 3D MR-TRUS images, allowing generation of volumetric 3D maps of targets identified in 3D MR images to be biopsied using 3D TRUS images. Our registration method first makes use of an initial rigid registration of 3D MR images to 3D TRUS images using 6 manually placed approximately corresponding landmarks in each image. Following the manual initialization, two prostate surfaces are segmented from 3D MR and TRUS images and then non-rigidly registered using a thin-plate spline (TPS) algorithm. The registration accuracy was evaluated using 4 patient images by measuring target registration error (TRE) of manually identified corresponding intrinsic fiducials (calcifications and/or cysts) in the prostates. Experimental results show that the proposed method yielded an overall mean TRE of 2.05 mm, which is favorably comparable to a clinical requirement for an error of less than 2.5 mm.

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.

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

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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.

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

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

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...

Improving supervised classification accuracy using non-rigid multimodal image registration: detecting prostate cancer

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Chappelow, Jonathan; Viswanath, Satish; Monaco, James; Rosen, Mark; Tomaszewski, John; Feldman, Michael; Madabhushi, Anant

2008-03-01

Computer-aided diagnosis (CAD) systems for the detection of cancer in medical images require precise labeling of training data. For magnetic resonance (MR) imaging (MRI) of the prostate, training labels define the spatial extent of prostate cancer (CaP); the most common source for these labels is expert segmentations. When ancillary data such as whole mount histology (WMH) sections, which provide the gold standard for cancer ground truth, are available, the manual labeling of CaP can be improved by referencing WMH. However, manual segmentation is error prone, time consuming and not reproducible. Therefore, we present the use of multimodal image registration to automatically and accurately transcribe CaP from histology onto MRI following alignment of the two modalities, in order to improve the quality of training data and hence classifier performance. We quantitatively demonstrate the superiority of this registration-based methodology by comparing its results to the manual CaP annotation of expert radiologists. Five supervised CAD classifiers were trained using the labels for CaP extent on MRI obtained by the expert and 4 different registration techniques. Two of the registration methods were affi;ne schemes; one based on maximization of mutual information (MI) and the other method that we previously developed, Combined Feature Ensemble Mutual Information (COFEMI), which incorporates high-order statistical features for robust multimodal registration. Two non-rigid schemes were obtained by succeeding the two affine registration methods with an elastic deformation step using thin-plate splines (TPS). In the absence of definitive ground truth for CaP extent on MRI, classifier accuracy was evaluated against 7 ground truth surrogates obtained by different combinations of the expert and registration segmentations. For 26 multimodal MRI-WMH image pairs, all four registration methods produced a higher area under the receiver operating characteristic curve compared to that

Slice-to-Volume Nonrigid Registration of Histological Sections to MR Images of the Human Brain

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Osechinskiy, Sergey; Kruggel, Frithjof

2011-01-01

Registration of histological images to three-dimensional imaging modalities is an important step in quantitative analysis of brain structure, in architectonic mapping of the brain, and in investigation of the pathology of a brain disease. Reconstruction of histology volume from serial sections is a well-established procedure, but it does not address registration of individual slices from sparse sections, which is the aim of the slice-to-volume approach. This study presents a flexible framework for intensity-based slice-to-volume nonrigid registration algorithms with a geometric transformation deformation field parametrized by various classes of spline functions: thin-plate splines (TPS), Gaussian elastic body splines (GEBS), or cubic B-splines. Algorithms are applied to cross-modality registration of histological and magnetic resonance images of the human brain. Registration performance is evaluated across a range of optimization algorithms and intensity-based cost functions. For a particular case of histological data, best results are obtained with a TPS three-dimensional (3D) warp, a new unconstrained optimization algorithm (NEWUOA), and a correlation-coefficient-based cost function. PMID:22567290

Slice-to-Volume Nonrigid Registration of Histological Sections to MR Images of the Human Brain

Directory of Open Access Journals (Sweden)

Sergey Osechinskiy

2011-01-01

Full Text Available Registration of histological images to three-dimensional imaging modalities is an important step in quantitative analysis of brain structure, in architectonic mapping of the brain, and in investigation of the pathology of a brain disease. Reconstruction of histology volume from serial sections is a well-established procedure, but it does not address registration of individual slices from sparse sections, which is the aim of the slice-to-volume approach. This study presents a flexible framework for intensity-based slice-to-volume nonrigid registration algorithms with a geometric transformation deformation field parametrized by various classes of spline functions: thin-plate splines (TPS, Gaussian elastic body splines (GEBS, or cubic B-splines. Algorithms are applied to cross-modality registration of histological and magnetic resonance images of the human brain. Registration performance is evaluated across a range of optimization algorithms and intensity-based cost functions. For a particular case of histological data, best results are obtained with a TPS three-dimensional (3D warp, a new unconstrained optimization algorithm (NEWUOA, and a correlation-coefficient-based cost function.

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

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).

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)

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

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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.

Three-dimensional registration methods for multi-modal magnetic resonance neuroimages

International Nuclear Information System (INIS)

Triantafyllou, C.

2001-08-01

In this thesis, image alignment techniques are developed and evaluated for applications in neuroimaging. In particular, the problem of combining cross-sequence MRI (Magnetic Resonance Imaging) intra-subject scans is considered. The challenge in this case is to find topographically uniform mappings in order to register (find a mapping between) low resolution echo-planar images and their high resolution structural counterparts. Such an approach enables us to effectually fuse, in a clinically useful way, information across scans. This dissertation devises a new framework by which this may be achieved, involving appropriate optimisation of the required mapping functions, which turn out to be non-linear and high-dimensional in nature. Novel ways to constrain and regularise these functions to enhance the computational speed of the process and the accuracy of the solution are also studied. The algorithms, whose characteristics are demonstrated for this specific application should be fully generalisable to other medical imaging modalities and potentially, other areas of image processing. To begin with, some existing registration methods are reviewed, followed by the introduction of an automated global 3-D registration method. Its performance is investigated on extracted cortical and ventricular surfaces by utilising the principles of the chamfer matching approach. Evaluations on synthetic and real data-sets, are performed to show that removal of global image differences is possible in principle, although the true accuracy of the method depends on the type of geometrical distortions present. These results also reveal that this class of algorithm is unable to solve more localised variations and higher order magnetic field distortions between the images. These facts motivate the development of a high-dimensional 3-D registration method capable of effecting a one-to-one correspondence by capturing the localised differences. This method was seen to account not only for

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.

Development of Multiorgan Finite Element-Based Prostate Deformation Model Enabling Registration of Endorectal Coil Magnetic Resonance Imaging for Radiotherapy Planning

International Nuclear Information System (INIS)

Hensel, Jennifer M.; Menard, Cynthia; Chung, Peter W.M.; Milosevic, Michael F.; Kirilova, Anna; Moseley, Joanne L.; Haider, Masoom A.; Brock, Kristy K.

2007-01-01

Purpose: Endorectal coil (ERC) magnetic resonance imaging (MRI) provides superior visualization of the prostate compared with computed tomography at the expense of deformation. This study aimed to develop a multiorgan finite element deformable method, Morfeus, to accurately co-register these images for radiotherapy planning. Methods: Patients with prostate cancer underwent fiducial marker implantation and computed tomography simulation for radiotherapy planning. A series of axial MRI scans were acquired with and without an ERC. The prostate, bladder, rectum, and pubic bones were manually segmented and assigned linear elastic material properties. Morfeus mapped the surface of the bladder and rectum between two imaged states, calculating the deformation of the prostate through biomechanical properties. The accuracy of deformation was measured as fiducial marker error and residual surface deformation between the inferred and actual prostate. The deformation map was inverted to deform from 100 cm 3 to no coil. Results: The data from 19 patients were analyzed. Significant prostate deformation occurred with the ERC (mean intrapatient range, 0.88 ± 0.25 cm). The mean vector error in fiducial marker position (n = 57) was 0.22 ± 0.09 cm, and the mean vector residual surface deformation (n = 19) was 0.15 ± 0.06 cm for deformation from no coil to 100-cm 3 ERC, with an image vector resolution of 0.22 cm. Accurately deformed MRI scans improved soft-tissue resolution of the anatomy for radiotherapy planning. Conclusions: This method of multiorgan deformable registration enabled accurate co-registration of ERC-MRI scans with computed tomography treatment planning images. Superior structural detail was visible on ERC-MRI, which has potential for improving target delineation

The role of image registration in brain mapping

Science.gov (United States)

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

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

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

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.

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

Science.gov (United States)

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.

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].

Science.gov (United States)

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.

Assessment of rigid multi-modality image registration consistency using the multiple sub-volume registration (MSR) method

International Nuclear Information System (INIS)

Ceylan, C; Heide, U A van der; Bol, G H; Lagendijk, J J W; Kotte, A N T J

2005-01-01

Registration of different imaging modalities such as CT, MRI, functional MRI (fMRI), positron (PET) and single photon (SPECT) emission tomography is used in many clinical applications. Determining the quality of any automatic registration procedure has been a challenging part because no gold standard is available to evaluate the registration. In this note we present a method, called the 'multiple sub-volume registration' (MSR) method, for assessing the consistency of a rigid registration. This is done by registering sub-images of one data set on the other data set, performing a crude non-rigid registration. By analysing the deviations (local deformations) of the sub-volume registrations from the full registration we get a measure of the consistency of the rigid registration. Registration of 15 data sets which include CT, MR and PET images for brain, head and neck, cervix, prostate and lung was performed utilizing a rigid body registration with normalized mutual information as the similarity measure. The resulting registrations were classified as good or bad by visual inspection. The resulting registrations were also classified using our MSR method. The results of our MSR method agree with the classification obtained from visual inspection for all cases (p < 0.02 based on ANOVA of the good and bad groups). The proposed method is independent of the registration algorithm and similarity measure. It can be used for multi-modality image data sets and different anatomic sites of the patient. (note)

Display of cross sectional anatomy by nuclear magnetic resonance imaging.

Science.gov (United States)

Hinshaw, W S; Andrew, E R; Bottomley, P A; Holland, G N; Moore, W S

1978-04-01

High definition cross-sectional images produced by a new nuclear magnetic resonance (NMR) technique are shown. The images are a series of thin section scans in the coronal plane of the head of a rabbit. The NMR images are derived from the distribution of the density of mobile hydrogen atoms. Various tissue types can be distinguished and a clear registration of gross anatomy is demonstrated. No known hazards are associated with the technique.

Image registration: An essential part of radiation therapy treatment planning

International Nuclear Information System (INIS)

Rosenman, Julian G.; Miller, Elizabeth P.; Tracton, Gregg; Cullip, Tim J.

1998-01-01

Purpose: We believe that a three-dimensional (3D) registration of nonplanning (diagnostic) imaging data with the planning computed tomography (CT) offers a substantial improvement in tumor target identification for many radiation therapy patients. The purpose of this article is to review and discuss our experience to date. Methods and Materials: We reviewed the charts and treatment planning records of all patients that underwent 3D radiation treatment planning in our department from June 1994 to December 1995, to learn which patients had image registration performed and why it was thought they would benefit from this approach. We also measured how much error would have been introduced into the target definition if the nonplanning imaging data had not been available and only the planning CT had been used. Results: Between June 1994 and December 1995, 106 of 246 (43%) of patients undergoing 3D treatment planning had image registration. Four reasons for performing registration were identified. First, some tumor volumes have better definition on magnetic resonance imaging (MRI) than on CT. Second, a properly contrasted diagnostic CT sometimes can show the tumor target better than can the planning CT. Third, the diagnostic CT or MR may have been preoperative, with the postoperative planning CT no longer showing the tumor. Fourth, the patient may have undergone cytoreductive chemotherapy so that the postchemotherapy planning CT no longer showed the original tumor volume. In patients in whom the planning CT did not show the tumor volume well an analysis was done to determine how the treatment plan was changed with the addition of a better tumor-defining nonplanning CT or MR. We have found that the use of this additional imaging modality changed the tumor location in the treatment plan at least 1.5 cm for half of the patients, and up to 3.0 cm for ((1)/(4)) of the patients. Conclusions: Multimodality and/or sequential imaging can substantially aid in better tumor

Remote Sensing Image Registration Using Multiple Image Features

Directory of Open Access Journals (Sweden)

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.

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.

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

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

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.

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 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

CT image registration in sinogram space.

Science.gov (United States)

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

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.

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.

Display of cross sectional anatomy by nuclear magnetic resonance imaging. 1978.

Science.gov (United States)

Hinshaw, W S; Andrew, E R; Bottomley, P A; Holland, G N; Moore, W S

1995-12-01

High definition cross-sectional images produced by a new nuclear magnetic resonance (NMR) technique are shown. The images are a series of thin section scans in the coronal plane of the head of a rabbit. The NMR images are derived from the distribution of the density of mobile hydrogen atoms. Various tissue types can be distinguished and a clear registration of gross anatomy is demonstrated. No known hazards are associated with the technique.

A Novel Technique for Prealignment in Multimodality Medical Image Registration

Directory of Open Access Journals (Sweden)

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.

MR-CT registration using a Ni-Ti prostate stent in image-guided radiotherapy of prostate cancer.

Science.gov (United States)

Korsager, Anne Sofie; Carl, Jesper; Østergaard, Lasse Riis

2013-06-01

In image-guided radiotherapy of prostate cancer defining the clinical target volume often relies on magnetic resonance (MR). The task of transferring the clinical target volume from MR to standard planning computed tomography (CT) is not trivial due to prostate mobility. In this paper, an automatic local registration approach is proposed based on a newly developed removable Ni-Ti prostate stent. The registration uses the voxel similarity measure mutual information in a two-step approach where the pelvic bones are used to establish an initial registration for the local registration. In a phantom study, the accuracy was measured to 0.97 mm and visual inspection showed accurate registration of all 30 data sets. The consistency of the registration was examined where translation and rotation displacements yield a rotation error of 0.41° ± 0.45° and a translation error of 1.67 ± 2.24 mm. This study demonstrated the feasibility for an automatic local MR-CT registration using the prostate stent.

Registration of dynamic dopamine D{sub 2}receptor images using principal component analysis

Energy Technology Data Exchange (ETDEWEB)

Acton, P.D.; Ell, P.J. [Institute of Nuclear Medicine, University College London Medical School, London (United Kingdom); Pilowsky, L.S.; Brammer, M.J. [Institute of Psychiatry, De Crespigny Park, London (United Kingdom); Suckling, J. [Clinical Age Research Unit, Kings College School of Medicine and Dentistry, London (United Kingdom)

1997-11-01

This paper describes a novel technique for registering a dynamic sequence of single-photon emission tomography (SPET) dopamine D{sub 2}receptor images, using principal component analysis (PCA). Conventional methods for registering images, such as count difference and correlation coefficient algorithms, fail to take into account the dynamic nature of the data, resulting in large systematic errors when registering time-varying images. However, by using principal component analysis to extract the temporal structure of the image sequence, misregistration can be quantified by examining the distribution of eigenvalues. The registration procedures were tested using a computer-generated dynamic phantom derived from a high-resolution magnetic resonance image of a realistic brain phantom. Each method was also applied to clinical SPET images of dopamine D {sub 2}receptors, using the ligands iodine-123 iodobenzamide and iodine-123 epidepride, to investigate the influence of misregistration on kinetic modelling parameters and the binding potential. The PCA technique gave highly significant (P <0.001) improvements in image registration, leading to alignment errors in x and y of about 25% of the alternative methods, with reductions in autocorrelations over time. It could also be applied to align image sequences which the other methods failed completely to register, particularly {sup 123}I-epidepride scans. The PCA method produced data of much greater quality for subsequent kinetic modelling, with an improvement of nearly 50% in the {chi}{sup 2}of the fit to the compartmental model, and provided superior quality registration of particularly difficult dynamic sequences. (orig.) With 4 figs., 2 tabs., 26 refs.

Evaluation of interpolation effects on upsampling and accuracy of cost functions-based optimized automatic image registration.

Science.gov (United States)

Mahmoudzadeh, Amir Pasha; Kashou, Nasser H

2013-01-01

Interpolation has become a default operation in image processing and medical imaging and is one of the important factors in the success of an intensity-based registration method. Interpolation is needed if the fractional unit of motion is not matched and located on the high resolution (HR) grid. The purpose of this work is to present a systematic evaluation of eight standard interpolation techniques (trilinear, nearest neighbor, cubic Lagrangian, quintic Lagrangian, hepatic Lagrangian, windowed Sinc, B-spline 3rd order, and B-spline 4th order) and to compare the effect of cost functions (least squares (LS), normalized mutual information (NMI), normalized cross correlation (NCC), and correlation ratio (CR)) for optimized automatic image registration (OAIR) on 3D spoiled gradient recalled (SPGR) magnetic resonance images (MRI) of the brain acquired using a 3T GE MR scanner. Subsampling was performed in the axial, sagittal, and coronal directions to emulate three low resolution datasets. Afterwards, the low resolution datasets were upsampled using different interpolation methods, and they were then compared to the high resolution data. The mean squared error, peak signal to noise, joint entropy, and cost functions were computed for quantitative assessment of the method. Magnetic resonance image scans and joint histogram were used for qualitative assessment of the method.

Evaluation of Interpolation Effects on Upsampling and Accuracy of Cost Functions-Based Optimized Automatic Image Registration

Directory of Open Access Journals (Sweden)

Amir Pasha Mahmoudzadeh

2013-01-01

Full Text Available Interpolation has become a default operation in image processing and medical imaging and is one of the important factors in the success of an intensity-based registration method. Interpolation is needed if the fractional unit of motion is not matched and located on the high resolution (HR grid. The purpose of this work is to present a systematic evaluation of eight standard interpolation techniques (trilinear, nearest neighbor, cubic Lagrangian, quintic Lagrangian, hepatic Lagrangian, windowed Sinc, B-spline 3rd order, and B-spline 4th order and to compare the effect of cost functions (least squares (LS, normalized mutual information (NMI, normalized cross correlation (NCC, and correlation ratio (CR for optimized automatic image registration (OAIR on 3D spoiled gradient recalled (SPGR magnetic resonance images (MRI of the brain acquired using a 3T GE MR scanner. Subsampling was performed in the axial, sagittal, and coronal directions to emulate three low resolution datasets. Afterwards, the low resolution datasets were upsampled using different interpolation methods, and they were then compared to the high resolution data. The mean squared error, peak signal to noise, joint entropy, and cost functions were computed for quantitative assessment of the method. Magnetic resonance image scans and joint histogram were used for qualitative assessment of the method.

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.

Registration of dynamic dopamine D2receptor images using principal component analysis

International Nuclear Information System (INIS)

Acton, P.D.; Ell, P.J.; Pilowsky, L.S.; Brammer, M.J.; Suckling, J.

1997-01-01

This paper describes a novel technique for registering a dynamic sequence of single-photon emission tomography (SPET) dopamine D 2 receptor images, using principal component analysis (PCA). Conventional methods for registering images, such as count difference and correlation coefficient algorithms, fail to take into account the dynamic nature of the data, resulting in large systematic errors when registering time-varying images. However, by using principal component analysis to extract the temporal structure of the image sequence, misregistration can be quantified by examining the distribution of eigenvalues. The registration procedures were tested using a computer-generated dynamic phantom derived from a high-resolution magnetic resonance image of a realistic brain phantom. Each method was also applied to clinical SPET images of dopamine D 2 receptors, using the ligands iodine-123 iodobenzamide and iodine-123 epidepride, to investigate the influence of misregistration on kinetic modelling parameters and the binding potential. The PCA technique gave highly significant (P 123 I-epidepride scans. The PCA method produced data of much greater quality for subsequent kinetic modelling, with an improvement of nearly 50% in the χ 2 of the fit to the compartmental model, and provided superior quality registration of particularly difficult dynamic sequences. (orig.)

Facilitating tumor functional assessment by spatially relating 3D tumor histology and In Vivo MRI: Image registration approach

NARCIS (Netherlands)

L. Alic (Lejla); J.C. Haeck (Joost); K. Bol (Karin); S. Klein (Stefan); S.T. van Tiel (Sandra); P.A. Wielepolski (Piotr); M. de Jong (Marion); W.J. Niessen (Wiro); M.R. Bernsen (Monique); J.F. Veenland (Jifke)

2011-01-01

textabstractBackground: Magnetic resonance imaging (MRI), together with histology, is widely used to diagnose and to monitor treatment in oncology. Spatial correspondence between these modalities provides information about the ability of MRI to characterize cancerous tissue. However, registration is

Facilitating tumor functional assessment by spatially relating 3D tumor histology and in vivo MRI : Image registration approach

NARCIS (Netherlands)

Alic, L.; Haeck, J.C.; Bol, K.; Klein, S.; Van Tiel, S.T.; Wielepolski, P.A.; De Jong, M.; Niessen, W.J.; Bernsen, M.; Veenland, J.F.

2011-01-01

Background Magnetic resonance imaging (MRI), together with histology, is widely used to diagnose and to monitor treatment in oncology. Spatial correspondence between these modalities provides information about the ability of MRI to characterize cancerous tissue. However, registration is complicated

Facilitating tumor functional assessment by spatially relating 3D tumor histology and in vivo MRI: Image registration approach

NARCIS (Netherlands)

Alić, L.; Haeck, J.C.; Bol, K.; Klein, S.; Tiel, S.T. van; Wielopolski, P.A.; Bijster, M.; Bernsen, M.; Jong, M. de; Niessen, W.J.; Veenland, J.F.

2011-01-01

Background: Magnetic resonance imaging (MRI), together with histology, is widely used to diagnose and to monitor treatment in oncology. Spatial correspondence between these modalities provides information about the ability of MRI to characterize cancerous tissue. However, registration is complicated

TU-CD-BRA-01: A Novel 3D Registration Method for Multiparametric Radiological Images

International Nuclear Information System (INIS)

Akhbardeh, A; Parekth, VS; Jacobs, MA

2015-01-01

Purpose: Multiparametric and multimodality radiological imaging methods, such as, magnetic resonance imaging(MRI), computed tomography(CT), and positron emission tomography(PET), provide multiple types of tissue contrast and anatomical information for clinical diagnosis. However, these radiological modalities are acquired using very different technical parameters, e.g.,field of view(FOV), matrix size, and scan planes, which, can lead to challenges in registering the different data sets. Therefore, we developed a hybrid registration method based on 3D wavelet transformation and 3D interpolations that performs 3D resampling and rotation of the target radiological images without loss of information Methods: T1-weighted, T2-weighted, diffusion-weighted-imaging(DWI), dynamic-contrast-enhanced(DCE) MRI and PET/CT were used in the registration algorithm from breast and prostate data at 3T MRI and multimodality(PET/CT) cases. The hybrid registration scheme consists of several steps to reslice and match each modality using a combination of 3D wavelets, interpolations, and affine registration steps. First, orthogonal reslicing is performed to equalize FOV, matrix sizes and the number of slices using wavelet transformation. Second, angular resampling of the target data is performed to match the reference data. Finally, using optimized angles from resampling, 3D registration is performed using similarity transformation(scaling and translation) between the reference and resliced target volume is performed. After registration, the mean-square-error(MSE) and Dice Similarity(DS) between the reference and registered target volumes were calculated. Results: The 3D registration method registered synthetic and clinical data with significant improvement(p<0.05) of overlap between anatomical structures. After transforming and deforming the synthetic data, the MSE and Dice similarity were 0.12 and 0.99. The average improvement of the MSE in breast was 62%(0.27 to 0.10) and prostate was

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...

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)

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.

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.

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.

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

MR-CT registration using a Ni-Ti prostate stent in image-guided radiotherapy of prostate cancer

International Nuclear Information System (INIS)

Korsager, Anne Sofie; Østergaard, Lasse Riis; Carl, Jesper

2013-01-01

Purpose: In image-guided radiotherapy of prostate cancer defining the clinical target volume often relies on magnetic resonance (MR). The task of transferring the clinical target volume from MR to standard planning computed tomography (CT) is not trivial due to prostate mobility. In this paper, an automatic local registration approach is proposed based on a newly developed removable Ni-Ti prostate stent.Methods: The registration uses the voxel similarity measure mutual information in a two-step approach where the pelvic bones are used to establish an initial registration for the local registration.Results: In a phantom study, the accuracy was measured to 0.97 mm and visual inspection showed accurate registration of all 30 data sets. The consistency of the registration was examined where translation and rotation displacements yield a rotation error of 0.41° ± 0.45° and a translation error of 1.67 ± 2.24 mm.Conclusions: This study demonstrated the feasibility for an automatic local MR-CT registration using the prostate stent.

MR-CT registration using a Ni-Ti prostate stent in image-guided radiotherapy of prostate cancer

Energy Technology Data Exchange (ETDEWEB)

Korsager, Anne Sofie; Ostergaard, Lasse Riis [Department of Health Science and Technology, Aalborg University, Aalborg 9220 (Denmark); Carl, Jesper [Department of Medical Physics, Oncology, Aalborg Hospital, Aalborg 9100 (Denmark)

2013-06-15

Purpose: In image-guided radiotherapy of prostate cancer defining the clinical target volume often relies on magnetic resonance (MR). The task of transferring the clinical target volume from MR to standard planning computed tomography (CT) is not trivial due to prostate mobility. In this paper, an automatic local registration approach is proposed based on a newly developed removable Ni-Ti prostate stent.Methods: The registration uses the voxel similarity measure mutual information in a two-step approach where the pelvic bones are used to establish an initial registration for the local registration.Results: In a phantom study, the accuracy was measured to 0.97 mm and visual inspection showed accurate registration of all 30 data sets. The consistency of the registration was examined where translation and rotation displacements yield a rotation error of 0.41 Degree-Sign {+-} 0.45 Degree-Sign and a translation error of 1.67 {+-} 2.24 mm.Conclusions: This study demonstrated the feasibility for an automatic local MR-CT registration using the prostate stent.

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

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

Toward magnetic resonance-guided electroanatomical voltage mapping for catheter ablation of scar-related ventricular tachycardia: a comparison of registration methods.

Science.gov (United States)

Tao, Qian; Milles, Julien; VAN Huls VAN Taxis, Carine; Lamb, Hildo J; Reiber, Johan H C; Zeppenfeld, Katja; VAN DER Geest, Rob J

2012-01-01

Integration of preprocedural delayed enhanced magnetic resonance imaging (DE-MRI) with electroanatomical voltage mapping (EAVM) may provide additional high-resolution substrate information for catheter ablation of scar-related ventricular tachycardias (VT). Accurate and fast image integration of DE-MRI with EAVM is desirable for MR-guided ablation. Twenty-six VT patients with large transmural scar underwent catheter ablation and preprocedural DE-MRI. With different registration models and EAVM input, 3 image integration methods were evaluated and compared to the commercial registration module CartoMerge. The performance was evaluated both in terms of distance measure that describes surface matching, and correlation measure that describes actual scar correspondence. Compared to CartoMerge, the method that uses the translation-and-rotation model and high-density EAVM input resulted in a registration error of 4.32±0.69 mm as compared to 4.84 ± 1.07 (P <0.05); the method that uses the translation model and high-density EAVM input resulted in a registration error of 4.60 ± 0.65 mm (P = NS); and the method that uses the translation model and a single anatomical landmark input resulted in a registration error of 6.58 ± 1.63 mm (P < 0.05). No significant difference in scar correlation was observed between all 3 methods and CartoMerge (P = NS). During VT ablation procedures, accurate integration of EAVM and DE-MRI can be achieved using a translation registration model and a single anatomical landmark. This model allows for image integration in minimal mapping time and is likely to reduce fluoroscopy time and increase procedure efficacy. © 2011 Wiley Periodicals, Inc.

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

Science.gov (United States)

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.

A Comparative Evaluation of 3 Different Free-Form Deformable Image Registration and Contour Propagation Methods for Head and Neck MRI: The Case of Parotid Changes During Radiotherapy.

Science.gov (United States)

Broggi, Sara; Scalco, Elisa; Belli, Maria Luisa; Logghe, Gerlinde; Verellen, Dirk; Moriconi, Stefano; Chiara, Anna; Palmisano, Anna; Mellone, Renata; Fiorino, Claudio; Rizzo, Giovanna

2017-06-01

To validate and compare the deformable image registration and parotid contour propagation process for head and neck magnetic resonance imaging in patients treated with radiotherapy using 3 different approaches-the commercial MIM, the open-source Elastix software, and an optimized version of it. Twelve patients with head and neck cancer previously treated with radiotherapy were considered. Deformable image registration and parotid contour propagation were evaluated by considering the magnetic resonance images acquired before and after the end of the treatment. Deformable image registration, based on free-form deformation method, and contour propagation available on MIM were compared to Elastix. Two different contour propagation approaches were implemented for Elastix software, a conventional one (DIR_Trx) and an optimized homemade version, based on mesh deformation (DIR_Mesh). The accuracy of these 3 approaches was estimated by comparing propagated to manual contours in terms of average symmetric distance, maximum symmetric distance, Dice similarity coefficient, sensitivity, and inclusiveness. A good agreement was generally found between the manual contours and the propagated ones, without differences among the 3 methods; in few critical cases with complex deformations, DIR_Mesh proved to be more accurate, having the lowest values of average symmetric distance and maximum symmetric distance and the highest value of Dice similarity coefficient, although nonsignificant. The average propagation errors with respect to the reference contours are lower than the voxel diagonal (2 mm), and Dice similarity coefficient is around 0.8 for all 3 methods. The 3 free-form deformation approaches were not significantly different in terms of deformable image registration accuracy and can be safely adopted for the registration and parotid contour propagation during radiotherapy on magnetic resonance imaging. More optimized approaches (as DIR_Mesh) could be preferable for critical

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

Science.gov (United States)

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.

Image registration/fusion software for PET and CT/MRI by using simultaneous emission and transmission scans

International Nuclear Information System (INIS)

Kitamura, Keishi; Amano, Masaharu; Sato, Tomohiko; Okumura, Takeshi; Konishi, Norihiro; Komatsu, Masahiko

2003-01-01

When PET (positron emission tomography) is used for oncology studies, it is important to register and over-lay PET images with the images of other anatomical modalities, such as those obtained by CT (computed tomography) or MRI (magnetic resonance imaging), in order for the lesions to be anatomically located with high accuracy. The Shimadzu SET-2000W Series PET scanners provide simultaneous acquisition of emission and transmission data, which is capable of complete spatial alignment of both functional and attenuation images. This report describes our newly developed image registration/fusion software, which reformats PET emission images to the CT/MRI grid by using the transform matrix obtained by matching PET transmission images with CT/MRI images. Transmission images are registered and fused either automatically or manually, through 3-dimensional rotation and translation, with the transaxial, sagittal, and coronal fused images being monitored on the screen. This new method permits sufficiently accurate registration and efficient data processing with promoting effective use of CT/MRI images of the DICOM format, without using markers in data acquisition or any special equipment, such as a combined PET/CT scanner. (author)

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

Directory of Open Access Journals (Sweden)

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)

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

Science.gov (United States)

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.

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.

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.

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

Non-rigid registration of tomographic images with Fourier transforms

International Nuclear Information System (INIS)

Osorio, Ar; Isoardi, Ra; Mato, G

2007-01-01

Spatial image registration of deformable body parts such as thorax and abdomen has important medical applications, but at the same time, it represents an important computational challenge. In this work we propose an automatic algorithm to perform non-rigid registration of tomographic images using a non-rigid model based on Fourier transforms. As a measure of similarity, we use the correlation coefficient, finding that the optimal order of the transformation is n = 3 (36 parameters). We apply this method to a digital phantom and to 7 pairs of patient images corresponding to clinical CT scans. The preliminary results indicate a fairly good agreement according to medical experts, with an average registration error of 2 mm for the case of clinical images. For 2D images (dimensions 512x512), the average running time for the algorithm is 15 seconds using a standard personal computer. Summarizing, we find that intra-modality registration of the abdomen can be achieved with acceptable accuracy for slight deformations and can be extended to 3D with a reasonable execution time

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.

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.

Optimized imaging using non-rigid registration

International Nuclear Information System (INIS)

Berkels, Benjamin; Binev, Peter; Blom, Douglas A.; Dahmen, Wolfgang; Sharpley, Robert C.; Vogt, Thomas

2014-01-01

The extraordinary improvements of modern imaging devices offer access to data with unprecedented information content. However, widely used image processing methodologies fall far short of exploiting the full breadth of information offered by numerous types of scanning probe, optical, and electron microscopies. In many applications, it is necessary to keep measurement intensities below a desired threshold. We propose a methodology for extracting an increased level of information by processing a series of data sets suffering, in particular, from high degree of spatial uncertainty caused by complex multiscale motion during the acquisition process. An important role is played by a non-rigid pixel-wise registration method that can cope with low signal-to-noise ratios. This is accompanied by formulating objective quality measures which replace human intervention and visual inspection in the processing chain. Scanning transmission electron microscopy of siliceous zeolite material exhibits the above-mentioned obstructions and therefore serves as orientation and a test of our procedures. - Highlights: • Developed a new process for extracting more information from a series of STEM images. • An objective non-rigid registration process copes with distortions. • Images of zeolite Y show retrieval of all information available from the data set. • Quantitative measures of registration quality were implemented. • Applicable to any serially acquired data, e.g. STM, AFM, STXM, etc

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

Energy Technology Data Exchange (ETDEWEB)

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.

Nonrigid registration of dynamic medical imaging data using nD + t B-splines and a groupwise optimization approach.

Science.gov (United States)

Metz, C T; Klein, S; Schaap, M; van Walsum, T; Niessen, W J

2011-04-01

A registration method for motion estimation in dynamic medical imaging data is proposed. Registration is performed directly on the dynamic image, thus avoiding a bias towards a specifically chosen reference time point. Both spatial and temporal smoothness of the transformations are taken into account. Optionally, cyclic motion can be imposed, which can be useful for visualization (viewing the segmentation sequentially) or model building purposes. The method is based on a 3D (2D+time) or 4D (3D+time) free-form B-spline deformation model, a similarity metric that minimizes the intensity variances over time and constrained optimization using a stochastic gradient descent method with adaptive step size estimation. The method was quantitatively compared with existing registration techniques on synthetic data and 3D+t computed tomography data of the lungs. This showed subvoxel accuracy while delivering smooth transformations, and high consistency of the registration results. Furthermore, the accuracy of semi-automatic derivation of left ventricular volume curves from 3D+t computed tomography angiography data of the heart was evaluated. On average, the deviation from the curves derived from the manual annotations was approximately 3%. The potential of the method for other imaging modalities was shown on 2D+t ultrasound and 2D+t magnetic resonance images. The software is publicly available as an extension to the registration package elastix. Copyright © 2010 Elsevier B.V. All rights reserved.

Non-rigid image registration using bone growth model

DEFF Research Database (Denmark)

Bro-Nielsen, Morten; Gramkow, Claus; Kreiborg, Sven

1997-01-01

Non-rigid registration has traditionally used physical models like elasticity and fluids. These models are very seldom valid models of the difference between the registered images. This paper presents a non-rigid registration algorithm, which uses a model of bone growth as a model of the change...... between time sequence images of the human mandible. By being able to register the images, this paper at the same time contributes to the validation of the growth model, which is based on the currently available medical theories and knowledge...

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

The Insight ToolKit Image Registration Framework

Directory of Open Access Journals (Sweden)

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.

On removing interpolation and resampling artifacts in rigid image registration.

Science.gov (United States)

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.

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

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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%.

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)

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

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.

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.

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.

MRI (Magnetic Resonance Imaging)

Science.gov (United States)

... Procedures Medical Imaging MRI (Magnetic Resonance Imaging) MRI (Magnetic Resonance Imaging) Share Tweet Linkedin Pin it More sharing options Linkedin Pin it Email Print Magnetic Resonance Imaging (MRI) is a medical imaging procedure for ...

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.

A Bayesian nonrigid registration method to enhance intraoperative target definition in image-guided prostate procedures through uncertainty characterization

International Nuclear Information System (INIS)

Pursley, Jennifer; Risholm, Petter; Fedorov, Andriy; Tuncali, Kemal; Fennessy, Fiona M.; Wells, William M. III; Tempany, Clare M.; Cormack, Robert A.

2012-01-01

Purpose: This study introduces a probabilistic nonrigid registration method for use in image-guided prostate brachytherapy. Intraoperative imaging for prostate procedures, usually transrectal ultrasound (TRUS), is typically inferior to diagnostic-quality imaging of the pelvis such as endorectal magnetic resonance imaging (MRI). MR images contain superior detail of the prostate boundaries and provide substructure features not otherwise visible. Previous efforts to register diagnostic prostate images with the intraoperative coordinate system have been deterministic and did not offer a measure of the registration uncertainty. The authors developed a Bayesian registration method to estimate the posterior distribution on deformations and provide a case-specific measure of the associated registration uncertainty. Methods: The authors adapted a biomechanical-based probabilistic nonrigid method to register diagnostic to intraoperative images by aligning a physician's segmentations of the prostate in the two images. The posterior distribution was characterized with a Markov Chain Monte Carlo method; the maximum a posteriori deformation and the associated uncertainty were estimated from the collection of deformation samples drawn from the posterior distribution. The authors validated the registration method using a dataset created from ten patients with MRI-guided prostate biopsies who had both diagnostic and intraprocedural 3 Tesla MRI scans. The accuracy and precision of the estimated posterior distribution on deformations were evaluated from two predictive distance distributions: between the deformed central zone-peripheral zone (CZ-PZ) interface and the physician-labeled interface, and based on physician-defined landmarks. Geometric margins on the registration of the prostate's peripheral zone were determined from the posterior predictive distance to the CZ-PZ interface separately for the base, mid-gland, and apical regions of the prostate. Results: The authors observed

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.

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

Impact of Computed Tomography Image Quality on Image-Guided Radiation Therapy Based on Soft Tissue Registration

International Nuclear Information System (INIS)

Morrow, Natalya V.; Lawton, Colleen A.; Qi, X. Sharon; Li, X. Allen

2012-01-01

Purpose: In image-guided radiation therapy (IGRT), different computed tomography (CT) modalities with varying image quality are being used to correct for interfractional variations in patient set-up and anatomy changes, thereby reducing clinical target volume to the planning target volume (CTV-to-PTV) margins. We explore how CT image quality affects patient repositioning and CTV-to-PTV margins in soft tissue registration-based IGRT for prostate cancer patients. Methods and Materials: Four CT-based IGRT modalities used for prostate RT were considered in this study: MV fan beam CT (MVFBCT) (Tomotherapy), MV cone beam CT (MVCBCT) (MVision; Siemens), kV fan beam CT (kVFBCT) (CTVision, Siemens), and kV cone beam CT (kVCBCT) (Synergy; Elekta). Daily shifts were determined by manual registration to achieve the best soft tissue agreement. Effect of image quality on patient repositioning was determined by statistical analysis of daily shifts for 136 patients (34 per modality). Inter- and intraobserver variability of soft tissue registration was evaluated based on the registration of a representative scan for each CT modality with its corresponding planning scan. Results: Superior image quality with the kVFBCT resulted in reduced uncertainty in soft tissue registration during IGRT compared with other image modalities for IGRT. The largest interobserver variations of soft tissue registration were 1.1 mm, 2.5 mm, 2.6 mm, and 3.2 mm for kVFBCT, kVCBCT, MVFBCT, and MVCBCT, respectively. Conclusions: Image quality adversely affects the reproducibility of soft tissue-based registration for IGRT and necessitates a careful consideration of residual uncertainties in determining different CTV-to-PTV margins for IGRT using different image modalities.

Impact of Computed Tomography Image Quality on Image-Guided Radiation Therapy Based on Soft Tissue Registration

Energy Technology Data Exchange (ETDEWEB)

Morrow, Natalya V.; Lawton, Colleen A. [Department of Radiation Oncology, Medical College of Wisconsin, Milwaukee, Wisconsin (United States); Qi, X. Sharon [Department of Radiation Oncology, University of Colorado Denver, Denver, Colorado (United States); Li, X. Allen, E-mail: ali@mcw.edu [Department of Radiation Oncology, Medical College of Wisconsin, Milwaukee, Wisconsin (United States)

2012-04-01

Purpose: In image-guided radiation therapy (IGRT), different computed tomography (CT) modalities with varying image quality are being used to correct for interfractional variations in patient set-up and anatomy changes, thereby reducing clinical target volume to the planning target volume (CTV-to-PTV) margins. We explore how CT image quality affects patient repositioning and CTV-to-PTV margins in soft tissue registration-based IGRT for prostate cancer patients. Methods and Materials: Four CT-based IGRT modalities used for prostate RT were considered in this study: MV fan beam CT (MVFBCT) (Tomotherapy), MV cone beam CT (MVCBCT) (MVision; Siemens), kV fan beam CT (kVFBCT) (CTVision, Siemens), and kV cone beam CT (kVCBCT) (Synergy; Elekta). Daily shifts were determined by manual registration to achieve the best soft tissue agreement. Effect of image quality on patient repositioning was determined by statistical analysis of daily shifts for 136 patients (34 per modality). Inter- and intraobserver variability of soft tissue registration was evaluated based on the registration of a representative scan for each CT modality with its corresponding planning scan. Results: Superior image quality with the kVFBCT resulted in reduced uncertainty in soft tissue registration during IGRT compared with other image modalities for IGRT. The largest interobserver variations of soft tissue registration were 1.1 mm, 2.5 mm, 2.6 mm, and 3.2 mm for kVFBCT, kVCBCT, MVFBCT, and MVCBCT, respectively. Conclusions: Image quality adversely affects the reproducibility of soft tissue-based registration for IGRT and necessitates a careful consideration of residual uncertainties in determining different CTV-to-PTV margins for IGRT using different image modalities.

A Review of Point-Wise Motion Tracking Algorithms for Fetal Magnetic Resonance Imaging.

Science.gov (United States)

Chikop, Shivaprasad; Koulagi, Girish; Kumbara, Ankita; Geethanath, Sairam

2016-01-01

We review recent feature-based tracking algorithms as applied to fetal magnetic resonance imaging (MRI). Motion in fetal MRI is an active and challenging area of research, but the challenge can be mitigated by strategies related to patient setup, acquisition, reconstruction, and image processing. We focus on fetal motion correction through methods based on tracking algorithms for registration of slices with similar anatomy in multiple volumes. We describe five motion detection algorithms based on corner detection and region-based methods through pseudocodes, illustrating the results of their application to fetal MRI. We compare the performance of these methods on the basis of error in registration and minimum number of feature points required for registration. Harris, a corner detection method, provides similar error when compared to the other methods and has the lowest number of feature points required at that error level. We do not discuss group-wise methods here. Finally, we attempt to communicate the application of available feature extraction methods to fetal MRI.

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

NARCIS (Netherlands)

Shamonin, D.P.; Bron, E.E.; Lelieveldt, B.P.F.; Smits, M.; Klein, S.; Staring, M.

2014-01-01

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.

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

NARCIS (Netherlands)

D.P. Shamonin (Denis); E.E. Bron (Esther); B.P.F. Lelieveldt (Boudewijn); M. Smits (Marion); S. Klein (Stefan); M. Staring (Marius)

2014-01-01

textabstractNonrigid 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

Automatic registration of terrestrial point cloud using panoramic reflectance images

NARCIS (Netherlands)

Kang, Z.

2008-01-01

Much attention is paid to registration of terrestrial point clouds nowadays. Research is carried out towards improved efficiency and automation of the registration process. This paper reports a new approach for point clouds registration utilizing reflectance panoramic images. The approach follows a

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.

Advanced methods for image registration applied to JET videos

Energy Technology Data Exchange (ETDEWEB)

Craciunescu, Teddy, E-mail: teddy.craciunescu@jet.uk [EURATOM-MEdC Association, NILPRP, Bucharest (Romania); Murari, Andrea [Consorzio RFX, Associazione EURATOM-ENEA per la Fusione, Padova (Italy); Gelfusa, Michela [Associazione EURATOM-ENEA – University of Rome “Tor Vergata”, Roma (Italy); Tiseanu, Ion; Zoita, Vasile [EURATOM-MEdC Association, NILPRP, Bucharest (Romania); Arnoux, Gilles [EURATOM/CCFE Fusion Association, Culham Science Centre, Abingdon, Oxon (United Kingdom)

2015-10-15

Graphical abstract: - Highlights: • Development of an image registration method for JET IR and fast visible cameras. • Method based on SIFT descriptors and coherent point drift points set registration technique. • Method able to deal with extremely noisy images and very low luminosity images. • Computation time compatible with the inter-shot analysis. - Abstract: The last years have witnessed a significant increase in the use of digital cameras on JET. They are routinely applied for imaging in the IR and visible spectral regions. One of the main technical difficulties in interpreting the data of camera based diagnostics is the presence of movements of the field of view. Small movements occur due to machine shaking during normal pulses while large ones may arise during disruptions. Some cameras show a correlation of image movement with change of magnetic field strength. For deriving unaltered information from the videos and for allowing correct interpretation an image registration method, based on highly distinctive scale invariant feature transform (SIFT) descriptors and on the coherent point drift (CPD) points set registration technique, has been developed. The algorithm incorporates a complex procedure for rejecting outliers. The method has been applied for vibrations correction to videos collected by the JET wide angle infrared camera and for the correction of spurious rotations in the case of the JET fast visible camera (which is equipped with an image intensifier). The method has proved to be able to deal with the images provided by this camera frequently characterized by low contrast and a high level of blurring and noise.

Inverse consistent non-rigid image registration based on robust point set matching

Science.gov (United States)

2014-01-01

Background Robust point matching (RPM) has been extensively used in non-rigid registration of images to robustly register two sets of image points. However, except for the location at control points, RPM cannot estimate the consistent correspondence between two images because RPM is a unidirectional image matching approach. Therefore, it is an important issue to make an improvement in image registration based on RPM. Methods In our work, a consistent image registration approach based on the point sets matching is proposed to incorporate the property of inverse consistency and improve registration accuracy. Instead of only estimating the forward transformation between the source point sets and the target point sets in state-of-the-art RPM algorithms, the forward and backward transformations between two point sets are estimated concurrently in our algorithm. The inverse consistency constraints are introduced to the cost function of RPM and the fuzzy correspondences between two point sets are estimated based on both the forward and backward transformations simultaneously. A modified consistent landmark thin-plate spline registration is discussed in detail to find the forward and backward transformations during the optimization of RPM. The similarity of image content is also incorporated into point matching in order to improve image matching. Results Synthetic data sets, medical images are employed to demonstrate and validate the performance of our approach. The inverse consistent errors of our algorithm are smaller than RPM. Especially, the topology of transformations is preserved well for our algorithm for the large deformation between point sets. Moreover, the distance errors of our algorithm are similar to that of RPM, and they maintain a downward trend as whole, which demonstrates the convergence of our algorithm. The registration errors for image registrations are evaluated also. Again, our algorithm achieves the lower registration errors in same iteration number

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

Feasibility analysis of high resolution tissue image registration using 3-D synthetic data

Directory of Open Access Journals (Sweden)

Yachna Sharma

2011-01-01

Full Text Available Background: Registration of high-resolution tissue images is a critical step in the 3D analysis of protein expression. Because the distance between images (~4-5μm thickness of a tissue section is nearly the size of the objects of interest (~10-20μm cancer cell nucleus, a given object is often not present in both of two adjacent images. Without consistent correspondence of objects between images, registration becomes a difficult task. This work assesses the feasibility of current registration techniques for such images. Methods: We generated high resolution synthetic 3-D image data sets emulating the constraints in real data. We applied multiple registration methods to the synthetic image data sets and assessed the registration performance of three techniques (i.e., mutual information (MI, kernel density estimate (KDE method [1], and principal component analysis (PCA at various slice thicknesses (with increments of 1μm in order to quantify the limitations of each method. Results: Our analysis shows that PCA, when combined with the KDE method based on nuclei centers, aligns images corresponding to 5μm thick sections with acceptable accuracy. We also note that registration error increases rapidly with increasing distance between images, and that the choice of feature points which are conserved between slices improves performance. Conclusions: We used simulation to help select appropriate features and methods for image registration by estimating best-case-scenario errors for given data constraints in histological images. The results of this study suggest that much of the difficulty of stained tissue registration can be reduced to the problem of accurately identifying feature points, such as the center of nuclei.

Deformable registration of the planning image (kVCT) and the daily images (MVCT) for adaptive radiation therapy

International Nuclear Information System (INIS)

Lu Weiguo; Olivera, Gustavo H; Chen, Quan; Ruchala, Kenneth J; Haimerl, Jason; Meeks, Sanford L; Langen, Katja M; Kupelian, Patrick A

2006-01-01

The incorporation of daily images into the radiotherapy process leads to adaptive radiation therapy (ART), in which the treatment is evaluated periodically and the plan is adaptively modified for the remaining course of radiotherapy. Deformable registration between the planning image and the daily images is a key component of ART. In this paper, we report our researches on deformable registration between the planning kVCT and the daily MVCT image sets. The method is based on a fast intensity-based free-form deformable registration technique. Considering the noise and contrast resolution differences between the kVCT and the MVCT, an 'edge-preserving smoothing' is applied to the MVCT image prior to the deformable registration process. We retrospectively studied daily MVCT images from commercial TomoTherapy machines from different clinical centers. The data set includes five head-neck cases, one pelvis case, two lung cases and one prostate case. Each case has one kVCT image and 20-40 MVCT images. We registered the MVCT images with their corresponding kVCT image. The similarity measures and visual inspections of contour matches by physicians validated this technique. The applications of deformable registration in ART, including 'deformable dose accumulation', 'automatic re-contouring' and 'tumour growth/regression evaluation' throughout the course of radiotherapy are also studied

Error estimation of deformable image registration of pulmonary CT scans using convolutional neural networks.

Science.gov (United States)

Eppenhof, Koen A J; Pluim, Josien P W

2018-04-01

Error estimation in nonlinear medical image registration is a nontrivial problem that is important for validation of registration methods. We propose a supervised method for estimation of registration errors in nonlinear registration of three-dimensional (3-D) images. The method is based on a 3-D convolutional neural network that learns to estimate registration errors from a pair of image patches. By applying the network to patches centered around every voxel, we construct registration error maps. The network is trained using a set of representative images that have been synthetically transformed to construct a set of image pairs with known deformations. The method is evaluated on deformable registrations of inhale-exhale pairs of thoracic CT scans. Using ground truth target registration errors on manually annotated landmarks, we evaluate the method's ability to estimate local registration errors. Estimation of full domain error maps is evaluated using a gold standard approach. The two evaluation approaches show that we can train the network to robustly estimate registration errors in a predetermined range, with subvoxel accuracy. We achieved a root-mean-square deviation of 0.51 mm from gold standard registration errors and of 0.66 mm from ground truth landmark registration errors.

Image Registration Methode in Radar Interferometry

Directory of Open Access Journals (Sweden)

S. Chelbi

2015-08-01

Full Text Available This article presents a methodology for the determination of the registration of an Interferometric Synthetic radar (InSAR pair images with half pixel precision. Using the two superposed radar images Single Look complexes (SLC [1-4], we developed an iterative process to superpose these two images according to their correlation coefficient with a high coherence area. This work concerns the exploitation of ERS Tandem pair of radar images SLC of the Algiers area acquired on 03 January and 04 January 1994. The former is taken as a master image and the latter as a slave image.

FEM-based evaluation of deformable image registration for radiation therapy

International Nuclear Information System (INIS)

Zhong Hualiang; Peters, Terry; Siebers, Jeffrey V

2007-01-01

This paper presents a new concept to automatically detect the neighborhood in an image where deformable registration is mis-performing. Specifically, the displacement vector field (DVF) from a deformable image registration is substituted into a finite-element-based elastic framework to calculate unbalanced energy in each element. The value of the derived energy indicates the quality of the DVF in its neighborhood. The new voxel-based evaluation approach is compared with three other validation criteria: landmark measurement, a finite element approach and visual comparison, for deformable registrations performed with the optical-flow-based 'demons' algorithm as well as thin-plate spline interpolation. This analysis was performed on three pairs of prostate CT images. The results of the analysis show that the four criteria give mutually comparable quantitative assessments on the six registration instances. As an objective concept, the unbalanced energy presents no requirement on boundary constraints in its calculation, different from traditional mechanical modeling. This method is automatic, and at voxel level suitable to evaluate deformable registration in a clinical setting

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.

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

Deep Adaptive Log-Demons: Diffeomorphic Image Registration with Very Large Deformations

Directory of Open Access Journals (Sweden)

Liya Zhao

2015-01-01

Full Text Available This paper proposes a new framework for capturing large and complex deformation in image registration. Traditionally, this challenging problem relies firstly on a preregistration, usually an affine matrix containing rotation, scale, and translation and afterwards on a nonrigid transformation. According to preregistration, the directly calculated affine matrix, which is obtained by limited pixel information, may misregistrate when large biases exist, thus misleading following registration subversively. To address this problem, for two-dimensional (2D images, the two-layer deep adaptive registration framework proposed in this paper firstly accurately classifies the rotation parameter through multilayer convolutional neural networks (CNNs and then identifies scale and translation parameters separately. For three-dimensional (3D images, affine matrix is located through feature correspondences by a triplanar 2D CNNs. Then deformation removal is done iteratively through preregistration and demons registration. By comparison with the state-of-the-art registration framework, our method gains more accurate registration results on both synthetic and real datasets. Besides, principal component analysis (PCA is combined with correlation like Pearson and Spearman to form new similarity standards in 2D and 3D registration. Experiment results also show faster convergence speed.

Image registration in the brain: a test of clinical accuracy

International Nuclear Information System (INIS)

Rosenman, Julian; Miller, Elizabeth P.; Rinker, Lillian; Mukherji, Suresh; Tracton, Gregg; Cullip, Tim J.; Muller, Keith E.; DeLuca, Marla C.; Major, Stacey A.; Sailer, Scott; Varia, Mahesh

1997-01-01

Purpose/Objective: Accurate localization of tumor and normal structures is a critical step in the radiation treatment planning processes and has direct implications for tumor control success as well as normal tissue morbidity. We conducted a study to determine the accuracy of transferring tumor information from diagnostic images to the simulation films and planning CT with conventional methods using the best clinical judgment and compared that to tumor localization using 3D registration software. Materials and Methods: We measured the accuracy with which experienced clinicians could localize tumor volume from diagnostic images to either simulation films or a planning CT, with and without 3D registration software. To obtain absolute registration truth we used the method of identical pairs wherein a CT data set was duplicated and one copy resliced along a different plane than the original while maintaining the exact mathematical transformation between them. A tumor was then added to the resliced CT which became the surrogate diagnostic image. Because we were concerned that a CT/CT pair might be too easy to register, a simulated MR made by re-colorizing the resliced CT (to become a facsimile MR or fMR) was also used as a surrogate diagnostic image. Finally we studied the registration accuracy when a CT/(real)MR pair was used. The registration in this case could not be guaranteed to be exact, but the studies were obtained under carefully controlled conditions and were registered from bony landmarks using commercial radiosurgery software. A team of experts then placed the tumor from the resliced CT, fMR, or real MR to an AP and lateral 'isocenter simulation film' (a digitally reconstructed radiograph made from the unmarked CT) and to the 'planning CT' - also the unmarked CT. A registration of the data sets (CT/CT, CT/fMR and CT/MR) was also done using our 3D registration software. A total of thirty-six tasks on four subjects were performed. Four analyses (each with

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

Consistency of parametric registration in serial MRI studies of brain tumor progression

International Nuclear Information System (INIS)

Mang, Andreas; Buzug, Thorsten M.; Schnabel, Julia A.; Crum, William R.; Modat, Marc; Ourselin, Sebastien; Hawkes, David J.; Camara-Rey, Oscar; Palm, Christoph; Caseiras, Gisele Brasil; Jaeger, H.R.

2008-01-01

The consistency of parametric registration in multi-temporal magnetic resonance (MR) imaging studies was evaluated. Serial MRI scans of adult patients with a brain tumor (glioma) were aligned by parametric registration. The performance of low-order spatial alignment (6/9/12 degrees of freedom) of different 3D serial MR-weighted images is evaluated. A registration protocol for the alignment of all images to one reference coordinate system at baseline is presented. Registration results were evaluated for both, multimodal intra-timepoint and mono-modal multi-temporal registration. The latter case might present a challenge to automatic intensity-based registration algorithms due to ill-defined correspondences. The performance of our algorithm was assessed by testing the inverse registration consistency. Four different similarity measures were evaluated to assess consistency. Careful visual inspection suggests that images are well aligned, but their consistency may be imperfect. Sub-voxel inconsistency within the brain was found for allsimilarity measures used for parametric multi-temporal registration. T1-weighted images were most reliable for establishing spatial correspondence between different timepoints. The parametric registration algorithm is feasible for use in this application. The sub-voxel resolution mean displacement error of registration transformations demonstrates that the algorithm converges to an almost identical solution for forward and reverse registration. (orig.)

Interactive deformation registration of endorectal prostate MRI using ITK thin plate splines.

Science.gov (United States)

Cheung, M Rex; Krishnan, Karthik

2009-03-01

Magnetic resonance imaging with an endorectal coil allows high-resolution imaging of prostate cancer and the surrounding normal organs. These anatomic details can be used to direct radiotherapy. However, organ deformation introduced by the endorectal coil makes it difficult to register magnetic resonance images for treatment planning. In this study, plug-ins for the volume visualization software VolView were implemented on the basis of algorithms from the National Library of Medicine's Insight Segmentation and Registration Toolkit (ITK). Magnetic resonance images of a phantom simulating human pelvic structures were obtained with and without the endorectal coil balloon inflated. The prostate not deformed by the endorectal balloon was registered to the deformed prostate using an ITK thin plate spline (TPS). This plug-in allows the use of crop planes to limit the deformable registration in the region of interest around the prostate. These crop planes restricted the support of the TPS to the area around the prostate, where most of the deformation occurred. The region outside the crop planes was anchored by grid points. The TPS was more accurate in registering the local deformation of the prostate compared with a TPS variant, the elastic body spline. The TPS was also applied to register an in vivo T(2)-weighted endorectal magnetic resonance image. The intraprostatic tumor was accurately registered. This could potentially guide the boosting of intraprostatic targets. The source and target landmarks were placed graphically. This TPS plug-in allows the registration to be undone. The landmarks could be added, removed, and adjusted in real time and in three dimensions between repeated registrations. This interactive TPS plug-in allows a user to obtain a high level of accuracy satisfactory to a specific application efficiently. Because it is open-source software, the imaging community will be able to validate and improve the algorithm.

Estimation of regional lung expansion via 3D image registration

Science.gov (United States)

Pan, Yan; Kumar, Dinesh; Hoffman, Eric A.; Christensen, Gary E.; McLennan, Geoffrey; Song, Joo Hyun; Ross, Alan; Simon, Brett A.; Reinhardt, Joseph M.

2005-04-01

A method is described to estimate regional lung expansion and related biomechanical parameters using multiple CT images of the lungs, acquired at different inflation levels. In this study, the lungs of two sheep were imaged utilizing a multi-detector row CT at different lung inflations in the prone and supine positions. Using the lung surfaces and the airway branch points for guidance, a 3D inverse consistent image registration procedure was used to match different lung volumes at each orientation. The registration was validated using a set of implanted metal markers. After registration, the Jacobian of the deformation field was computed to express regional expansion or contraction. The regional lung expansion at different pressures and different orientations are compared.

Multiscale deformable registration for dual-energy x-ray imaging

International Nuclear Information System (INIS)

Gang, G. J.; Varon, C. A.; Kashani, H.; Richard, S.; Paul, N. S.; Van Metter, R.; Yorkston, J.; Siewerdsen, J. H.

2009-01-01

Dual-energy (DE) imaging of the chest improves the conspicuity of subtle lung nodules through the removal of overlying anatomical noise. Recent work has shown double-shot DE imaging (i.e., successive acquisition of low- and high-energy projections) to provide detective quantum efficiency, spectral separation (and therefore contrast), and radiation dose superior to single-shot DE imaging configurations (e.g., with a CR cassette). However, the temporal separation between high-energy (HE) and low-energy (LE) image acquisition can result in motion artifacts in the DE images, reducing image quality and diminishing diagnostic performance. This has motivated the development of a deformable registration technique that aligns the HE image onto the LE image before DE decomposition. The algorithm reported here operates in multiple passes at progressively smaller scales and increasing resolution. The first pass addresses large-scale motion by means of mutual information optimization, while successive passes (2-4) correct misregistration at finer scales by means of normalized cross correlation. Evaluation of registration performance in 129 patients imaged using an experimental DE imaging prototype demonstrated a statistically significant improvement in image alignment. Specific to the cardiac region, the registration algorithm was found to outperform a simple cardiac-gating system designed to trigger both HE and LE exposures during diastole. Modulation transfer function (MTF) analysis reveals additional advantages in DE image quality in terms of noise reduction and edge enhancement. This algorithm could offer an important tool in enhancing DE image quality and potentially improving diagnostic performance.

TU-A-19A-01: Image Registration I: Deformable Image Registration, Contour Propagation and Dose Mapping: 101 and 201

Energy Technology Data Exchange (ETDEWEB)

Kessler, M [The University of Michigan, Ann Arbor, MI (United States)

2014-06-15

Deformable image registration, contour propagation and dose mapping have become common, possibly essential tools for modern image-guided radiation therapy. Historically, these tools have been largely developed at academic medical centers and used in a rather limited and well controlled fashion. Today these tools are now available to the radiotherapy community at large, both as stand-alone applications and as integrated components of both treatment planning and treatment delivery systems. Unfortunately, the details of how these tools work and their limitations are not generally documented or described by the vendors that provide them. Although “it looks right”, determining that unphysical deformations may have occurred is crucial. Because of this, understanding how and when to use, and not use these tools to support everyday clinical decisions is far from straight forward. The goal of this session will be to present both the theory (basic and advanced) and practical clinical use of deformable image registration, contour propagation and dose mapping. To the extent possible, the “secret sauce” that different vendor use to produce reasonable/acceptable results will be described. A detailed explanation of the possible sources of errors and actual examples of these will be presented. Knowing the underlying principles of the process and understanding the confounding factors will help the practicing medical physicist be better able to make decisions (about making decisions) using these tools available. Learning Objectives: Understand the basic (101) and advanced (201) principles of deformable image registration, contour propagation and dose mapping data mapping. Understand the sources and impact of errors in registration and data mapping and the methods for evaluating the performance of these tools. Understand the clinical use and value of these tools, especially when used as a “black box”.

Supervised Quality Assessment Of Medical Image Registration: Application to intra-patient CT lung registration

NARCIS (Netherlands)

Muenzing, S.E.; Ginneken, B. van; Murphy, K.; Pluim, J.P.

2012-01-01

A novel method for automatic quality assessment of medical image registration is presented. The method is based on supervised learning of local alignment patterns, which are captured by statistical image features at distinctive landmark points. A two-stage classifier cascade, employing an optimal

Supervised quality assessment of medical image registration : application to intra-patient CT lung registration

NARCIS (Netherlands)

Muenzing, S.E.A.; Ginneken, van B.; Murphy, K.; Pluim, J.P.W.

2012-01-01

A novel method for automatic quality assessment of medical image registration is presented. The method is based on supervised learning of local alignment patterns, which are captured by statistical image features at distinctive landmark points. A two-stage classifier cascade, employing an optimal

[Medical image elastic registration smoothed by unconstrained optimized thin-plate spline].

Science.gov (United States)

Zhang, Yu; Li, Shuxiang; Chen, Wufan; Liu, Zhexing

2003-12-01

Elastic registration of medical image is an important subject in medical image processing. Previous work has concentrated on selecting the corresponding landmarks manually and then using thin-plate spline interpolating to gain the elastic transformation. However, the landmarks extraction is always prone to error, which will influence the registration results. Localizing the landmarks manually is also difficult and time-consuming. We the optimization theory to improve the thin-plate spline interpolation, and based on it, used an automatic method to extract the landmarks. Combining these two steps, we have proposed an automatic, exact and robust registration method and have gained satisfactory registration results.

Pediatric magnetic resonance imaging

International Nuclear Information System (INIS)

Cohen, M.D.

1986-01-01

This book defines the current clinical potential of magnetic resonance imaging and focuses on direct clinical work with pediatric patients. A section dealing with the physics of magnetic resonance imaging provides an introduction to enable clinicians to utilize the machine and interpret the images. Magnetic resonance imaging is presented as an appropriate imaging modality for pediatric patients utilizing no radiation

Remote Sensing Image Registration with Line Segments and Their Intersections

Directory of Open Access Journals (Sweden)

Chengjin Lyu

2017-05-01

Full Text Available Image registration is a basic but essential step for remote sensing image processing, and finding stable features in multitemporal images is one of the most considerable challenges in the field. The main shape contours of artificial objects (e.g., roads, buildings, farmlands, and airports can be generally described as a group of line segments, which are stable features, even in images with evident background changes (e.g., images taken before and after a disaster. In this study, a registration method that uses line segments and their intersections is proposed for multitemporal remote sensing images. First, line segments are extracted in image pyramids to unify the scales of the reference image and the test image. Then, a line descriptor based on the gradient distribution of local areas is constructed, and the segments are matched in image pyramids. Lastly, triplets of intersections of matching lines are selected to estimate affine transformation between two images. Additional corresponding intersections are provided based on the estimated transformation, and an iterative process is adopted to remove outliers. The performance of the proposed method is tested on a variety of optical remote sensing image pairs, including synthetic and real data. Compared with existing methods, our method can provide more accurate registration results, even in images with significant background changes.

An automatic MRI/SPECT registration algorithm using image intensity and anatomical feature as matching characters: application on the evaluation of Parkinson's disease

International Nuclear Information System (INIS)

Lee, J.-D.; Huang, C.-H.; Weng, Y.-H.; Lin, K.-J.; Chen, C.-T.

2007-01-01

Single-photon emission computed tomography (SPECT) of dopamine transporters with 99m Tc-TRODAT-1 has recently been proposed to offer valuable information in assessing the functionality of dopaminergic systems. Magnetic resonance imaging (MRI) and SPECT imaging are important in the noninvasive examination of dopamine concentration in vivo. Therefore, this investigation presents an automated MRI/SPECT image registration algorithm based on a new similarity metric. This similarity metric combines anatomical features that are characterized by specific binding, the mean count per voxel in putamens and caudate nuclei, and the distribution of image intensity that is characterized by normalized mutual information (NMI). A preprocess, a novel two-cluster SPECT normalization algorithm, is also presented for MRI/SPECT registration. Clinical MRI/SPECT data from 18 healthy subjects and 13 Parkinson's disease (PD) patients are involved to validate the performance of the proposed algorithms. An appropriate color map, such as 'rainbow,' for image display enables the two-cluster SPECT normalization algorithm to provide clinically meaningful visual contrast. The proposed registration scheme reduces target registration error from >7 mm for conventional registration algorithm based on NMI to approximately 4 mm. The error in the specific/nonspecific 99m Tc-TRODAT-1 binding ratio, which is employed as a quantitative measure of TRODAT receptor binding, is also reduced from 0.45±0.22 to 0.08±0.06 among healthy subjects and from 0.28±0.18 to 0.12±0.09 among PD patients

SU-E-J-248: Comparative Study of Two Image Registration for Image-Guided Radiation Therapy in Esophageal Cancer

Energy Technology Data Exchange (ETDEWEB)

Shang, K; Wang, J; Liu, D; Li, R; Cao, Y; Chi, Z [The Fourth Hospital of Hebei Medical University, Shijiazhuang, Hebei, CN, Shijiazhuang, Hebei (China)

2014-06-01

Purpose: Image-guided radiation therapy (IGRT) is one of the major treatment of esophageal cancer. Gray value registration and bone registration are two kinds of image registration, the purpose of this work is to compare which one is more suitable for esophageal cancer patients. Methods: Twenty three esophageal patients were treated by Elekta Synergy, CBCT images were acquired and automatically registered to planning kilovoltage CT scans according to gray value or bone registration. The setup errors were measured in the X, Y and Z axis, respectively. Two kinds of setup errors were analysed by matching T test statistical method. Results: Four hundred and five groups of CBCT images were available and the systematic and random setup errors (cm) in X, Y, Z directions were 0.35, 0.63, 0.29 and 0.31, 0.53, 0.21 with gray value registration, while 0.37, 0.64, 0.26 and 0.32, 0.55, 0.20 with bone registration, respectively. Compared with bone registration and gray value registration, the setup errors in X and Z axis have significant differences. In Y axis, both measurement comparison results of T value is 0.256 (P value > 0.05); In X axis, the T value is 5.287(P value < 0.05); In Z axis, the T value is −5.138 (P value < 0.05). Conclusion: Gray value registration is recommended in image-guided radiotherapy for esophageal cancer and the other thoracic tumors. Manual registration could be applied when it is necessary. Bone registration is more suitable for the head tumor and pelvic tumor department where composed of redundant interconnected and immobile bone tissue.

Technical Note: Deformable image registration on partially matched images for radiotherapy applications

International Nuclear Information System (INIS)

Yang Deshan; Goddu, S. Murty; Lu Wei; Pechenaya, Olga L.; Wu Yu; Deasy, Joseph O.; El Naqa, Issam; Low, Daniel A.

2010-01-01

In radiation therapy applications, deformable image registrations (DIRs) are often carried out between two images that only partially match. Image mismatching could present as superior-inferior coverage differences, field-of-view (FOV) cutoffs, or motion crossing the image boundaries. In this study, the authors propose a method to improve the existing DIR algorithms so that DIR can be carried out in such situations. The basic idea is to extend the image volumes and define the extension voxels (outside the FOV or outside the original image volume) as NaN (not-a-number) values that are transparent to all floating-point computations in the DIR algorithms. Registrations are then carried out with one additional rule that NaN voxels can match any voxels. In this way, the matched sections of the images are registered properly, and the mismatched sections of the images are registered to NaN voxels. This method makes it possible to perform DIR on partially matched images that otherwise are difficult to register. It may also improve DIR accuracy, especially near or in the mismatched image regions.

Fusion of magnetic resonance angiography and magnetic resonance imaging for surgical planning for meningioma. Technical note

International Nuclear Information System (INIS)

Kashimura, Hiroshi; Ogasawara, Kuniaki; Arai, Hiroshi

2008-01-01

A fusion technique for magnetic resonance (MR) angiography and MR imaging was developed to help assess the peritumoral angioarchitecture during surgical planning for meningioma. Three-dimensional time-of-flight (3D-TOF) and 3D-spoiled gradient recalled (SPGR) datasets were obtained from 10 patients with intracranial meningioma, and fused using newly developed volume registration and visualization software. Maximum intensity projection (MIP) images from 3D-TOF MR angiography and axial SPGR MR imaging were displayed at the same time on the monitor. Selecting a vessel on the real-time MIP image indicated the corresponding points on the axial image automatically. Fusion images showed displacement of the anterior cerebral or middle cerebral artery in 7 patients and encasement of the anterior cerebral arteries in I patient, with no relationship between the main arterial trunk and tumor in 2 patients. Fusion of MR angiography and MR imaging can clarify relationships between the intracranial vasculature and meningioma, and may be helpful for surgical planning for meningioma. (author)

Image registration based on virtual frame sequence analysis

Energy Technology Data Exchange (ETDEWEB)

Chen, H.; Ng, W.S. [Nanyang Technological University, Computer Integrated Medical Intervention Laboratory, School of Mechanical and Aerospace Engineering, Singapore (Singapore); Shi, D. (Nanyang Technological University, School of Computer Engineering, Singapore, Singpore); Wee, S.B. [Tan Tock Seng Hospital, Department of General Surgery, Singapore (Singapore)

2007-08-15

This paper is to propose a new framework for medical image registration with large nonrigid deformations, which still remains one of the biggest challenges for image fusion and further analysis in many medical applications. Registration problem is formulated as to recover a deformation process with the known initial state and final state. To deal with large nonlinear deformations, virtual frames are proposed to be inserted to model the deformation process. A time parameter is introduced and the deformation between consecutive frames is described with a linear affine transformation. Experiments are conducted with simple geometric deformation as well as complex deformations presented in MRI and ultrasound images. All the deformations are characterized with nonlinearity. The positive results demonstrated the effectiveness of this algorithm. The framework proposed in this paper is feasible to register medical images with large nonlinear deformations and is especially useful for sequential images. (orig.)

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

Reducing uncertainties in volumetric image based deformable organ registration

International Nuclear Information System (INIS)

Liang, J.; Yan, D.

2003-01-01

Applying volumetric image feedback in radiotherapy requires image based deformable organ registration. The foundation of this registration is the ability of tracking subvolume displacement in organs of interest. Subvolume displacement can be calculated by applying biomechanics model and the finite element method to human organs manifested on the multiple volumetric images. The calculation accuracy, however, is highly dependent on the determination of the corresponding organ boundary points. Lacking sufficient information for such determination, uncertainties are inevitable--thus diminishing the registration accuracy. In this paper, a method of consuming energy minimization was developed to reduce these uncertainties. Starting from an initial selection of organ boundary point correspondence on volumetric image sets, the subvolume displacement and stress distribution of the whole organ are calculated and the consumed energy due to the subvolume displacements is computed accordingly. The corresponding positions of the initially selected boundary points are then iteratively optimized to minimize the consuming energy under geometry and stress constraints. In this study, a rectal wall delineated from patient CT image was artificially deformed using a computer simulation and utilized to test the optimization. Subvolume displacements calculated based on the optimized boundary point correspondence were compared to the true displacements, and the calculation accuracy was thereby evaluated. Results demonstrate that a significant improvement on the accuracy of the deformable organ registration can be achieved by applying the consuming energy minimization in the organ deformation calculation

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

Science.gov (United States)

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.

RESEARCH OF REGISTRATION APPROACHES OF THERMAL INFRARED IMAGES AND INTENSITY IMAGES OF POINT CLOUD

Directory of Open Access Journals (Sweden)

L. Liu

2017-09-01

Full Text Available In order to realize the analysis of thermal energy of the objects in 3D vision, the registration approach of thermal infrared images and TLS (Terrestrial Laser Scanner point cloud was studied. The original data was pre-processed. For the sake of making the scale and brightness contrast of the two kinds of data meet the needs of basic matching, the intensity image of point cloud was produced and projected to spherical coordinate system, histogram equalization processing was done for thermal infrared image.This paper focused on the research of registration approaches of thermal infrared images and intensity images of point cloud based on SIFT，EOH-SIFT and PIIFD operators. The latter of which is usually used for medical image matching with different spectral character. The comparison results of the experiments showed that PIIFD operator got much more accurate feature point correspondences compared to SIFT and EOH-SIFT operators. The thermal infrared image and intensity image also have ideal overlap results by quadratic polynomial transformation. Therefore, PIIFD can be used as the basic operator for the registration of thermal infrared images and intensity images, and the operator can also be further improved by incorporating the iteration method.

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.

Error estimation of deformable image registration of pulmonary CT scans using convolutional neural networks

NARCIS (Netherlands)

Eppenhof, K.A.J.; Pluim, J.P.W.

2018-01-01

Error estimation in nonlinear medical image registration is a nontrivial problem that is important for validation of registration methods. We propose a supervised method for estimation of registration errors in nonlinear registration of three-dimensional (3-D) images. The method is based on a 3-D

Diffusion tensor image registration using hybrid connectivity and tensor features.

Science.gov (United States)

Wang, Qian; Yap, Pew-Thian; Wu, Guorong; Shen, Dinggang

2014-07-01

Most existing diffusion tensor imaging (DTI) registration methods estimate structural correspondences based on voxelwise matching of tensors. The rich connectivity information that is given by DTI, however, is often neglected. In this article, we propose to integrate complementary information given by connectivity features and tensor features for improved registration accuracy. To utilize connectivity information, we place multiple anchors representing different brain anatomies in the image space, and define the connectivity features for each voxel as the geodesic distances from all anchors to the voxel under consideration. The geodesic distance, which is computed in relation to the tensor field, encapsulates information of brain connectivity. We also extract tensor features for every voxel to reflect the local statistics of tensors in its neighborhood. We then combine both connectivity features and tensor features for registration of tensor images. From the images, landmarks are selected automatically and their correspondences are determined based on their connectivity and tensor feature vectors. The deformation field that deforms one tensor image to the other is iteratively estimated and optimized according to the landmarks and their associated correspondences. Experimental results show that, by using connectivity features and tensor features simultaneously, registration accuracy is increased substantially compared with the cases using either type of features alone. Copyright © 2013 Wiley Periodicals, Inc.

Non-rigid ultrasound image registration using generalized relaxation labeling process

Science.gov (United States)

Lee, Jong-Ha; Seong, Yeong Kyeong; Park, MoonHo; Woo, Kyoung-Gu; Ku, Jeonghun; Park, Hee-Jun

2013-03-01

This research proposes a novel non-rigid registration method for ultrasound images. The most predominant anatomical features in medical images are tissue boundaries, which appear as edges. In ultrasound images, however, other features can be identified as well due to the specular reflections that appear as bright lines superimposed on the ideal edge location. In this work, an image's local phase information (via the frequency domain) is used to find the ideal edge location. The generalized relaxation labeling process is then formulated to align the feature points extracted from the ideal edge location. In this work, the original relaxation labeling method was generalized by taking n compatibility coefficient values to improve non-rigid registration performance. This contextual information combined with a relaxation labeling process is used to search for a correspondence. Then the transformation is calculated by the thin plate spline (TPS) model. These two processes are iterated until the optimal correspondence and transformation are found. We have tested our proposed method and the state-of-the-art algorithms with synthetic data and bladder ultrasound images of in vivo human subjects. Experiments show that the proposed method improves registration performance significantly, as compared to other state-of-the-art non-rigid registration algorithms.

Registration methods for pulmonary image analysis integration of morphological and physiological knowledge

CERN Document Server

Schmidt-Richberg, Alexander

2014-01-01

Various applications in the field of pulmonary image analysis require a registration of CT images of the lung. For example, a registration-based estimation of the breathing motion is employed to increase the accuracy of dose distribution in radiotherapy. Alexander Schmidt-Richberg develops methods to explicitly model morphological and physiological knowledge about respiration in algorithms for the registration of thoracic CT images. The author focusses on two lung-specific issues: on the one hand, the alignment of the interlobular fissures and on the other hand, the estimation of sliding motion at the lung boundaries. He shows that by explicitly considering these aspects based on a segmentation of the respective structure, registration accuracy can be significantly improved.

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

[Non-rigid medical image registration based on mutual information and thin-plate spline].

Science.gov (United States)

Cao, Guo-gang; Luo, Li-min

2009-01-01

To get precise and complete details, the contrast in different images is needed in medical diagnosis and computer assisted treatment. The image registration is the basis of contrast, but the regular rigid registration does not satisfy the clinic requirements. A non-rigid medical image registration method based on mutual information and thin-plate spline was present. Firstly, registering two images globally based on mutual information; secondly, dividing reference image and global-registered image into blocks and registering them; then getting the thin-plate spline transformation according to the shift of blocks' center; finally, applying the transformation to the global-registered image. The results show that the method is more precise than the global rigid registration based on mutual information and it reduces the complexity of getting control points and satisfy the clinic requirements better by getting control points of the thin-plate transformation automatically.

Pre-processing, registration and selection of adaptive optics corrected retinal images.

Science.gov (United States)

Ramaswamy, Gomathy; Devaney, Nicholas

2013-07-01

In this paper, the aim is to demonstrate enhanced processing of sequences of fundus images obtained using a commercial AO flood illumination system. The purpose of the work is to (1) correct for uneven illumination at the retina (2) automatically select the best quality images and (3) precisely register the best images. Adaptive optics corrected retinal images are pre-processed to correct uneven illumination using different methods; subtracting or dividing by the average filtered image, homomorphic filtering and a wavelet based approach. These images are evaluated to measure the image quality using various parameters, including sharpness, variance, power spectrum kurtosis and contrast. We have carried out the registration in two stages; a coarse stage using cross-correlation followed by fine registration using two approaches; parabolic interpolation on the peak of the cross-correlation and maximum-likelihood estimation. The angle of rotation of the images is measured using a combination of peak tracking and Procrustes transformation. We have found that a wavelet approach (Daubechies 4 wavelet at 6th level decomposition) provides good illumination correction with clear improvement in image sharpness and contrast. The assessment of image quality using a 'Designer metric' works well when compared to visual evaluation, although it is highly correlated with other metrics. In image registration, sub-pixel translation measured using parabolic interpolation on the peak of the cross-correlation function and maximum-likelihood estimation are found to give very similar results (RMS difference 0.047 pixels). We have confirmed that correcting rotation of the images provides a significant improvement, especially at the edges of the image. We observed that selecting the better quality frames (e.g. best 75% images) for image registration gives improved resolution, at the expense of poorer signal-to-noise. The sharpness map of the registered and de-rotated images shows increased

Magnetic resonance imaging apparatus

International Nuclear Information System (INIS)

Ehnholm, G.J.

1991-01-01

This patent describes an electron spin resonance enhanced magnetic resonance (MR) imaging (ESREMRI) apparatus able to generate a primary magnetic field during periods of nuclear spin transition excitation and magnetic resonance signal detection. This allows the generation of ESREMRI images of a subject. A primary magnetic field of a second and higher value generated during periods of nuclear spin transition excitation and magnetic resonance signal detection can be used to generate conventional MR images of a subject. The ESREMRI and native MR images so generated may be combined, (or superimposed). (author)

Hierarchical and successive approximate registration of the non-rigid medical image based on thin-plate splines

Science.gov (United States)

Hu, Jinyan; Li, Li; Yang, Yunfeng

2017-06-01

The hierarchical and successive approximate registration method of non-rigid medical image based on the thin-plate splines is proposed in the paper. There are two major novelties in the proposed method. First, the hierarchical registration based on Wavelet transform is used. The approximate image of Wavelet transform is selected as the registered object. Second, the successive approximation registration method is used to accomplish the non-rigid medical images registration, i.e. the local regions of the couple images are registered roughly based on the thin-plate splines, then, the current rough registration result is selected as the object to be registered in the following registration procedure. Experiments show that the proposed method is effective in the registration process of the non-rigid medical images.

Neural network-based feature point descriptors for registration of optical and SAR images

Science.gov (United States)

Abulkhanov, Dmitry; Konovalenko, Ivan; Nikolaev, Dmitry; Savchik, Alexey; Shvets, Evgeny; Sidorchuk, Dmitry

2018-04-01

Registration of images of different nature is an important technique used in image fusion, change detection, efficient information representation and other problems of computer vision. Solving this task using feature-based approaches is usually more complex than registration of several optical images because traditional feature descriptors (SIFT, SURF, etc.) perform poorly when images have different nature. In this paper we consider the problem of registration of SAR and optical images. We train neural network to build feature point descriptors and use RANSAC algorithm to align found matches. Experimental results are presented that confirm the method's effectiveness.

Localization and registration accuracy in image guided neurosurgery: a clinical study

Energy Technology Data Exchange (ETDEWEB)

Shamir, Reuben R.; Joskowicz, Leo [Hebrew University of Jerusalem, School of Engineering and Computer Science, Jerusalem (Israel); Spektor, Sergey; Shoshan, Yigal [Hadassah University Hospital, Department of Neurosurgery, School of Medicine, Jerusalem (Israel)

2009-01-15

To measure and compare the clinical localization and registration errors in image-guided neurosurgery, with the purpose of revising current assumptions. Twelve patients who underwent brain surgeries with a navigation system were randomly selected. A neurosurgeon localized and correlated the landmarks on preoperative MRI images and on the intraoperative physical anatomy with a tracked pointer. In the laboratory, we generated 612 scenarios in which one landmark pair was defined as the target and the remaining ones were used to compute the registration transformation. Four errors were measured: (1) fiducial localization error (FLE); (2) target registration error (TRE); (3) fiducial registration error (FRE); (4) Fitzpatrick's target registration error estimation (F-TRE). We compared the different errors and computed their correlation. The image and physical FLE ranges were 0.5-2.0 and 1.6-3.0 mm, respectively. The measured TRE, FRE and F-TRE were 4.1{+-}1.6, 3.9{+-}1.2, and 3.7{+-}2.2 mm, respectively. Low correlations of 0.19 and 0.37 were observed between the FRE and TRE and between the F-TRE and the TRE, respectively. The differences of the FRE and F-TRE from the TRE were 1.3{+-}1.0 mm (max=5.5 mm) and 1.3{+-}1.2 mm (max=7.3 mm), respectively. Contrary to common belief, the FLE presents significant variations. Moreover, both the FRE and the F-TRE are poor indicators of the TRE in image-to-patient registration. (orig.)

Registration of vehicle based panoramic image and LiDAR point cloud

Science.gov (United States)

Chen, Changjun; Cao, Liang; Xie, Hong; Zhuo, Xiangyu

2013-10-01

Higher quality surface information would be got when data from optical images and LiDAR were integrated, owing to the fact that optical images and LiDAR point cloud have unique characteristics that make them preferable in many applications. While most previous works focus on registration of pinhole perspective cameras to 2D or 3D LiDAR data. In this paper, a method for the registration of vehicle based panoramic image and LiDAR point cloud is proposed. Using the translation among panoramic image, single CCD image, laser scanner and Position and Orientation System (POS) along with the GPS/IMU data, precise co-registration between the panoramic image and the LiDAR point cloud in the world system is achieved. Results are presented under a real world data set collected by a new developed Mobile Mapping System (MMS) integrated with a high resolution panoramic camera, two laser scanners and a POS.

Multi-institutional Validation Study of Commercially Available Deformable Image Registration Software for Thoracic Images

International Nuclear Information System (INIS)

Kadoya, Noriyuki; Nakajima, Yujiro; Saito, Masahide; Miyabe, Yuki; Kurooka, Masahiko; Kito, Satoshi; Fujita, Yukio; Sasaki, Motoharu; Arai, Kazuhiro; Tani, Kensuke; Yagi, Masashi; Wakita, Akihisa; Tohyama, Naoki; Jingu, Keiichi

2016-01-01

Purpose: To assess the accuracy of the commercially available deformable image registration (DIR) software for thoracic images at multiple institutions. Methods and Materials: Thoracic 4-dimensional (4D) CT images of 10 patients with esophageal or lung cancer were used. Datasets for these patients were provided by DIR-lab ( (dir-lab.com)) and included a coordinate list of anatomic landmarks (300 bronchial bifurcations) that had been manually identified. Deformable image registration was performed between the peak-inhale and -exhale images. Deformable image registration error was determined by calculating the difference at each landmark point between the displacement calculated by DIR software and that calculated by the landmark. Results: Eleven institutions participated in this study: 4 used RayStation (RaySearch Laboratories, Stockholm, Sweden), 5 used MIM Software (Cleveland, OH), and 3 used Velocity (Varian Medical Systems, Palo Alto, CA). The ranges of the average absolute registration errors over all cases were as follows: 0.48 to 1.51 mm (right-left), 0.53 to 2.86 mm (anterior-posterior), 0.85 to 4.46 mm (superior-inferior), and 1.26 to 6.20 mm (3-dimensional). For each DIR software package, the average 3-dimensional registration error (range) was as follows: RayStation, 3.28 mm (1.26-3.91 mm); MIM Software, 3.29 mm (2.17-3.61 mm); and Velocity, 5.01 mm (4.02-6.20 mm). These results demonstrate that there was moderate variation among institutions, although the DIR software was the same. Conclusions: We evaluated the commercially available DIR software using thoracic 4D-CT images from multiple centers. Our results demonstrated that DIR accuracy differed among institutions because it was dependent on both the DIR software and procedure. Our results could be helpful for establishing prospective clinical trials and for the widespread use of DIR software. In addition, for clinical care, we should try to find the optimal DIR procedure using thoracic 4D

Automatic UAV Image Geo-Registration by Matching UAV Images to Georeferenced Image Data

Directory of Open Access Journals (Sweden)

Xiangyu Zhuo

2017-04-01

Full Text Available Recent years have witnessed the fast development of UAVs (unmanned aerial vehicles. As an alternative to traditional image acquisition methods, UAVs bridge the gap between terrestrial and airborne photogrammetry and enable flexible acquisition of high resolution images. However, the georeferencing accuracy of UAVs is still limited by the low-performance on-board GNSS and INS. This paper investigates automatic geo-registration of an individual UAV image or UAV image blocks by matching the UAV image(s with a previously taken georeferenced image, such as an individual aerial or satellite image with a height map attached or an aerial orthophoto with a DSM (digital surface model attached. As the biggest challenge for matching UAV and aerial images is in the large differences in scale and rotation, we propose a novel feature matching method for nadir or slightly tilted images. The method is comprised of a dense feature detection scheme, a one-to-many matching strategy and a global geometric verification scheme. The proposed method is able to find thousands of valid matches in cases where SIFT and ASIFT fail. Those matches can be used to geo-register the whole UAV image block towards the reference image data. When the reference images offer high georeferencing accuracy, the UAV images can also be geolocalized in a global coordinate system. A series of experiments involving different scenarios was conducted to validate the proposed method. The results demonstrate that our approach achieves not only decimeter-level registration accuracy, but also comparable global accuracy as the reference images.

Cross Correlation versus Normalized Mutual Information on Image Registration

Science.gov (United States)

Tan, Bin; Tilton, James C.; Lin, Guoqing

2016-01-01

This is the first study to quantitatively assess and compare cross correlation and normalized mutual information methods used to register images in subpixel scale. The study shows that the normalized mutual information method is less sensitive to unaligned edges due to the spectral response differences than is cross correlation. This characteristic makes the normalized image resolution a better candidate for band to band registration. Improved band-to-band registration in the data from satellite-borne instruments will result in improved retrievals of key science measurements such as cloud properties, vegetation, snow and fire.

Use of volume-rendered images in registration of nuclear medicine studies

International Nuclear Information System (INIS)

Wallis, J.W.; Miller, T.R.; Hsu, S.S.

1995-01-01

A simple operator-guided alignment technique based on volume-rendered images was developed to register tomographic nuclear medicine studies. For each of 2 three-dimensional data sets to be registered, volume-rendered images were generated in 3 orthogonal projections (x,y,z) using the method of maximum-activity projection. Registration was achieved as follows: (a) One of the rendering orientations (e.g. x) was chosen for manipulation; (b) The two dimensional rendering was translated and rotated under operator control to achieve the best alignment as determined by visual assessment; (c) This rotation and translation was then applied to the underlying three-dimensional data set, with updating of the rendered images in each of the orthogonal projections; (d) Another orientation was chosen, and the process repeated. Since manipulation was performed on the small two-dimensional rendered image, feedback was instantaneous. To aid in the visual alignment, difference images and flicker images (toggling between the two data sets) were displayed. Accuracy was assessed by analysis of separate clinical data sets acquired without patient movement. After arbitrary rotation and translation of one of the two data sets, the 2 data sets were registered. Mean registration error was 0.36 pixels, corresponding to a 2.44 mm registration error. Thus, accurate registration can be achieved in under 10 minutes using this simple technique. The accuracy of registration was assessed with use of duplicate SPECT studies originating from separate reconstructions of the data from each of the detectors of a triple-head gamma camera

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.

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.

Multiscale registration of medical images based on edge preserving scale space with application in image-guided radiation therapy

Science.gov (United States)

Li, Dengwang; Li, Hongsheng; Wan, Honglin; Chen, Jinhu; Gong, Guanzhong; Wang, Hongjun; Wang, Liming; Yin, Yong

2012-08-01

Mutual information (MI) is a well-accepted similarity measure for image registration in medical systems. However, MI-based registration faces the challenges of high computational complexity and a high likelihood of being trapped into local optima due to an absence of spatial information. In order to solve these problems, multi-scale frameworks can be used to accelerate registration and improve robustness. Traditional Gaussian pyramid representation is one such technique but it suffers from contour diffusion at coarse levels which may lead to unsatisfactory registration results. In this work, a new multi-scale registration framework called edge preserving multiscale registration (EPMR) was proposed based upon an edge preserving total variation L1 norm (TV-L1) scale space representation. TV-L1 scale space is constructed by selecting edges and contours of images according to their size rather than the intensity values of the image features. This ensures more meaningful spatial information with an EPMR framework for MI-based registration. Furthermore, we design an optimal estimation of the TV-L1 parameter in the EPMR framework by training and minimizing the transformation offset between the registered pairs for automated registration in medical systems. We validated our EPMR method on both simulated mono- and multi-modal medical datasets with ground truth and clinical studies from a combined positron emission tomography/computed tomography (PET/CT) scanner. We compared our registration framework with other traditional registration approaches. Our experimental results demonstrated that our method outperformed other methods in terms of the accuracy and robustness for medical images. EPMR can always achieve a small offset value, which is closer to the ground truth both for mono-modality and multi-modality, and the speed can be increased 5-8% for mono-modality and 10-14% for multi-modality registration under the same condition. Furthermore, clinical application by adaptive

Multiscale registration of medical images based on edge preserving scale space with application in image-guided radiation therapy

International Nuclear Information System (INIS)

Li Dengwang; Wan Honglin; Li Hongsheng; Chen Jinhu; Gong Guanzhong; Yin Yong; Wang Hongjun; Wang Liming

2012-01-01

Mutual information (MI) is a well-accepted similarity measure for image registration in medical systems. However, MI-based registration faces the challenges of high computational complexity and a high likelihood of being trapped into local optima due to an absence of spatial information. In order to solve these problems, multi-scale frameworks can be used to accelerate registration and improve robustness. Traditional Gaussian pyramid representation is one such technique but it suffers from contour diffusion at coarse levels which may lead to unsatisfactory registration results. In this work, a new multi-scale registration framework called edge preserving multiscale registration (EPMR) was proposed based upon an edge preserving total variation L1 norm (TV-L1) scale space representation. TV-L1 scale space is constructed by selecting edges and contours of images according to their size rather than the intensity values of the image features. This ensures more meaningful spatial information with an EPMR framework for MI-based registration. Furthermore, we design an optimal estimation of the TV-L1 parameter in the EPMR framework by training and minimizing the transformation offset between the registered pairs for automated registration in medical systems. We validated our EPMR method on both simulated mono- and multi-modal medical datasets with ground truth and clinical studies from a combined positron emission tomography/computed tomography (PET/CT) scanner. We compared our registration framework with other traditional registration approaches. Our experimental results demonstrated that our method outperformed other methods in terms of the accuracy and robustness for medical images. EPMR can always achieve a small offset value, which is closer to the ground truth both for mono-modality and multi-modality, and the speed can be increased 5–8% for mono-modality and 10–14% for multi-modality registration under the same condition. Furthermore, clinical application by

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

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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 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.

Brain Atlas Fusion from High-Thickness Diagnostic Magnetic Resonance Images by Learning-Based Super-Resolution.

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Zhang, Jinpeng; Zhang, Lichi; Xiang, Lei; Shao, Yeqin; Wu, Guorong; Zhou, Xiaodong; Shen, Dinggang; Wang, Qian

2017-03-01

It is fundamentally important to fuse the brain atlas from magnetic resonance (MR) images for many imaging-based studies. Most existing works focus on fusing the atlases from high-quality MR images. However, for low-quality diagnostic images (i.e., with high inter-slice thickness), the problem of atlas fusion has not been addressed yet. In this paper, we intend to fuse the brain atlas from the high-thickness diagnostic MR images that are prevalent for clinical routines. The main idea of our works is to extend the conventional groupwise registration by incorporating a novel super-resolution strategy. The contribution of the proposed super-resolution framework is two-fold. First, each high-thickness subject image is reconstructed to be isotropic by the patch-based sparsity learning. Then, the reconstructed isotropic image is enhanced for better quality through the random-forest-based regression model. In this way, the images obtained by the super-resolution strategy can be fused together by applying the groupwise registration method to construct the required atlas. Our experiments have shown that the proposed framework can effectively solve the problem of atlas fusion from the low-quality brain MR images.

Localization and registration accuracy in image guided neurosurgery: a clinical study

International Nuclear Information System (INIS)

Shamir, Reuben R.; Joskowicz, Leo; Spektor, Sergey; Shoshan, Yigal

2009-01-01

To measure and compare the clinical localization and registration errors in image-guided neurosurgery, with the purpose of revising current assumptions. Twelve patients who underwent brain surgeries with a navigation system were randomly selected. A neurosurgeon localized and correlated the landmarks on preoperative MRI images and on the intraoperative physical anatomy with a tracked pointer. In the laboratory, we generated 612 scenarios in which one landmark pair was defined as the target and the remaining ones were used to compute the registration transformation. Four errors were measured: (1) fiducial localization error (FLE); (2) target registration error (TRE); (3) fiducial registration error (FRE); (4) Fitzpatrick's target registration error estimation (F-TRE). We compared the different errors and computed their correlation. The image and physical FLE ranges were 0.5-2.0 and 1.6-3.0 mm, respectively. The measured TRE, FRE and F-TRE were 4.1±1.6, 3.9±1.2, and 3.7±2.2 mm, respectively. Low correlations of 0.19 and 0.37 were observed between the FRE and TRE and between the F-TRE and the TRE, respectively. The differences of the FRE and F-TRE from the TRE were 1.3±1.0 mm (max=5.5 mm) and 1.3±1.2 mm (max=7.3 mm), respectively. Contrary to common belief, the FLE presents significant variations. Moreover, both the FRE and the F-TRE are poor indicators of the TRE in image-to-patient registration. (orig.)

System and method for image registration of multiple video streams

Science.gov (United States)

Dillavou, Marcus W.; Shum, Phillip Corey; Guthrie, Baron L.; Shenai, Mahesh B.; Deaton, Drew Steven; May, Matthew Benton

2018-02-06

Provided herein are methods and systems for image registration from multiple sources. A method for image registration includes rendering a common field of interest that reflects a presence of a plurality of elements, wherein at least one of the elements is a remote element located remotely from another of the elements and updating the common field of interest such that the presence of the at least one of the elements is registered relative to another of the elements.

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

Science.gov (United States)

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.

Automated registration of multispectral MR vessel wall images of the carotid artery

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Klooster, R. van ' t; Staring, M.; Reiber, J. H. C.; Lelieveldt, B. P. F.; Geest, R. J. van der, E-mail: rvdgeest@lumc.nl [Department of Radiology, Division of Image Processing, Leiden University Medical Center, 2300 RC Leiden (Netherlands); Klein, S. [Department of Radiology and Department of Medical Informatics, Biomedical Imaging Group Rotterdam, Erasmus MC, Rotterdam 3015 GE (Netherlands); Kwee, R. M.; Kooi, M. E. [Department of Radiology, Cardiovascular Research Institute Maastricht, Maastricht University Medical Center, Maastricht 6202 AZ (Netherlands)

2013-12-15

Purpose: Atherosclerosis is the primary cause of heart disease and stroke. The detailed assessment of atherosclerosis of the carotid artery requires high resolution imaging of the vessel wall using multiple MR sequences with different contrast weightings. These images allow manual or automated classification of plaque components inside the vessel wall. Automated classification requires all sequences to be in alignment, which is hampered by patient motion. In clinical practice, correction of this motion is performed manually. Previous studies applied automated image registration to correct for motion using only nondeformable transformation models and did not perform a detailed quantitative validation. The purpose of this study is to develop an automated accurate 3D registration method, and to extensively validate this method on a large set of patient data. In addition, the authors quantified patient motion during scanning to investigate the need for correction. Methods: MR imaging studies (1.5T, dedicated carotid surface coil, Philips) from 55 TIA/stroke patients with ipsilateral <70% carotid artery stenosis were randomly selected from a larger cohort. Five MR pulse sequences were acquired around the carotid bifurcation, each containing nine transverse slices: T1-weighted turbo field echo, time of flight, T2-weighted turbo spin-echo, and pre- and postcontrast T1-weighted turbo spin-echo images (T1W TSE). The images were manually segmented by delineating the lumen contour in each vessel wall sequence and were manually aligned by applying throughplane and inplane translations to the images. To find the optimal automatic image registration method, different masks, choice of the fixed image, different types of the mutual information image similarity metric, and transformation models including 3D deformable transformation models, were evaluated. Evaluation of the automatic registration results was performed by comparing the lumen segmentations of the fixed image and

Automated registration of multispectral MR vessel wall images of the carotid artery

International Nuclear Information System (INIS)

Klooster, R. van 't; Staring, M.; Reiber, J. H. C.; Lelieveldt, B. P. F.; Geest, R. J. van der; Klein, S.; Kwee, R. M.; Kooi, M. E.

2013-01-01

Purpose: Atherosclerosis is the primary cause of heart disease and stroke. The detailed assessment of atherosclerosis of the carotid artery requires high resolution imaging of the vessel wall using multiple MR sequences with different contrast weightings. These images allow manual or automated classification of plaque components inside the vessel wall. Automated classification requires all sequences to be in alignment, which is hampered by patient motion. In clinical practice, correction of this motion is performed manually. Previous studies applied automated image registration to correct for motion using only nondeformable transformation models and did not perform a detailed quantitative validation. The purpose of this study is to develop an automated accurate 3D registration method, and to extensively validate this method on a large set of patient data. In addition, the authors quantified patient motion during scanning to investigate the need for correction. Methods: MR imaging studies (1.5T, dedicated carotid surface coil, Philips) from 55 TIA/stroke patients with ipsilateral <70% carotid artery stenosis were randomly selected from a larger cohort. Five MR pulse sequences were acquired around the carotid bifurcation, each containing nine transverse slices: T1-weighted turbo field echo, time of flight, T2-weighted turbo spin-echo, and pre- and postcontrast T1-weighted turbo spin-echo images (T1W TSE). The images were manually segmented by delineating the lumen contour in each vessel wall sequence and were manually aligned by applying throughplane and inplane translations to the images. To find the optimal automatic image registration method, different masks, choice of the fixed image, different types of the mutual information image similarity metric, and transformation models including 3D deformable transformation models, were evaluated. Evaluation of the automatic registration results was performed by comparing the lumen segmentations of the fixed image and

Children's (Pediatric) Magnetic Resonance Imaging

Science.gov (United States)

... Physician Resources Professions Site Index A-Z Children’s (Pediatric) Magnetic Resonance Imaging Children’s magnetic resonance imaging (MRI) ... limitations of Children’s (Pediatric) MRI? What is Children’s (Pediatric) MRI? Magnetic resonance imaging (MRI) is a noninvasive ...

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.

Medical Image Registration by means of a Bio-Inspired Optimization Strategy

Directory of Open Access Journals (Sweden)

Hariton Costin

2012-07-01

Full Text Available Medical imaging mainly treats and processes missing, ambiguous, complementary, redundant and distorted data. Biomedical image registration is the process of geometric overlaying or alignment of two or more 2D/3D images of the same scene, taken at different time slots, from different angles, and/or by different acquisition systems. In medical practice, it is becoming increasingly important in diagnosis, treatment planning, functional studies, computer-guided therapies, and in biomedical research. Technically, image registration implies a complex optimization of different parameters, performed at local or/and global levels. Local optimization methods frequently fail because functions of the involved metrics with respect to transformation parameters are generally nonconvex and irregular. Therefore, global methods are often required, at least at the beginning of the procedure. In this paper, a new evolutionary and bio-inspired approach -- bacterial foraging optimization -- is adapted for single-slice to 3-D PET and CT multimodal image registration. Preliminary results of optimizing the normalized mutual information similarity metric validated the efficacy of the proposed method by using a freely available medical image database.

Magnetic Resonance Imaging (MRI) -- Head

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... News Physician Resources Professions Site Index A-Z Magnetic Resonance Imaging (MRI) - Head Magnetic resonance imaging (MRI) of the head uses a powerful ... the Head? What is MRI of the Head? Magnetic resonance imaging (MRI) is a noninvasive medical test that ...

Children's (Pediatric) Magnetic Resonance Imaging

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Full Text Available ... Physician Resources Professions Site Index A-Z Children’s (Pediatric) Magnetic Resonance Imaging Children’s magnetic resonance imaging (MRI) ... limitations of Children’s (Pediatric) MRI? What is Children’s (Pediatric) MRI? Magnetic resonance imaging (MRI) is a noninvasive ...

Children's (Pediatric) Magnetic Resonance Imaging

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Full Text Available ... News Physician Resources Professions Site Index A-Z Children’s (Pediatric) Magnetic Resonance Imaging Children’s magnetic resonance imaging ( ... the limitations of Children’s (Pediatric) MRI? What is Children’s (Pediatric) MRI? Magnetic resonance imaging (MRI) is a ...

Grid Computing Application for Brain Magnetic Resonance Image Processing

International Nuclear Information System (INIS)

Valdivia, F; Crépeault, B; Duchesne, S

2012-01-01

This work emphasizes the use of grid computing and web technology for automatic post-processing of brain magnetic resonance images (MRI) in the context of neuropsychiatric (Alzheimer's disease) research. Post-acquisition image processing is achieved through the interconnection of several individual processes into pipelines. Each process has input and output data ports, options and execution parameters, and performs single tasks such as: a) extracting individual image attributes (e.g. dimensions, orientation, center of mass), b) performing image transformations (e.g. scaling, rotation, skewing, intensity standardization, linear and non-linear registration), c) performing image statistical analyses, and d) producing the necessary quality control images and/or files for user review. The pipelines are built to perform specific sequences of tasks on the alphanumeric data and MRIs contained in our database. The web application is coded in PHP and allows the creation of scripts to create, store and execute pipelines and their instances either on our local cluster or on high-performance computing platforms. To run an instance on an external cluster, the web application opens a communication tunnel through which it copies the necessary files, submits the execution commands and collects the results. We present result on system tests for the processing of a set of 821 brain MRIs from the Alzheimer's Disease Neuroimaging Initiative study via a nonlinear registration pipeline composed of 10 processes. Our results show successful execution on both local and external clusters, and a 4-fold increase in performance if using the external cluster. However, the latter's performance does not scale linearly as queue waiting times and execution overhead increase with the number of tasks to be executed.

Automated robust registration of grossly misregistered whole-slide images with varying stains

Science.gov (United States)

Litjens, G.; Safferling, K.; Grabe, N.

2016-03-01

Cancer diagnosis and pharmaceutical research increasingly depend on the accurate quantification of cancer biomarkers. Identification of biomarkers is usually performed through immunohistochemical staining of cancer sections on glass slides. However, combination of multiple biomarkers from a wide variety of immunohistochemically stained slides is a tedious process in traditional histopathology due to the switching of glass slides and re-identification of regions of interest by pathologists. Digital pathology now allows us to apply image registration algorithms to digitized whole-slides to align the differing immunohistochemical stains automatically. However, registration algorithms need to be robust to changes in color due to differing stains and severe changes in tissue content between slides. In this work we developed a robust registration methodology to allow for fast coarse alignment of multiple immunohistochemical stains to the base hematyoxylin and eosin stained image. We applied HSD color model conversion to obtain a less stain color dependent representation of the whole-slide images. Subsequently, optical density thresholding and connected component analysis were used to identify the relevant regions for registration. Template matching using normalized mutual information was applied to provide initial translation and rotation parameters, after which a cost function-driven affine registration was performed. The algorithm was validated using 40 slides from 10 prostate cancer patients, with landmark registration error as a metric. Median landmark registration error was around 180 microns, which indicates performance is adequate for practical application. None of the registrations failed, indicating the robustness of the algorithm.

Progressive attenuation fields: Fast 2D-3D image registration without precomputation

International Nuclear Information System (INIS)

Rohlfing, Torsten; Russakoff, Daniel B.; Denzler, Joachim; Mori, Kensaku; Maurer, Calvin R. Jr.

2005-01-01

Computation of digitally reconstructed radiograph (DRR) images is the rate-limiting step in most current intensity-based algorithms for the registration of three-dimensional (3D) images to two-dimensional (2D) projection images. This paper introduces and evaluates the progressive attenuation field (PAF), which is a new method to speed up DRR computation. A PAF is closely related to an attenuation field (AF). A major difference is that a PAF is constructed on the fly as the registration proceeds; it does not require any precomputation time, nor does it make any prior assumptions of the patient pose or limit the permissible range of patient motion. A PAF effectively acts as a cache memory for projection values once they are computed, rather than as a lookup table for precomputed projections like standard AFs. We use a cylindrical attenuation field parametrization, which is better suited for many medical applications of 2D-3D registration than the usual two-plane parametrization. The computed attenuation values are stored in a hash table for time-efficient storage and access. Using clinical gold-standard spine image data sets from five patients, we demonstrate consistent speedups of intensity-based 2D-3D image registration using PAF DRRs by a factor of 10 over conventional ray casting DRRs with no decrease of registration accuracy or robustness

An Image Registration Method for Colposcopic Images

Directory of Open Access Journals (Sweden)

Efrén Mezura-Montes

2013-01-01

sequence and a division of such image into small windows. A search process is then carried out to find the window with the highest affinity in each image of the sequence and replace it with the window in the reference image. The affinity value is based on polynomial approximation of the time series computed and the search is bounded by a search radius which defines the neighborhood of each window. The proposed approach is tested in ten 310-frame real cases in two experiments: the first one to determine the best values for the window size and the search radius and the second one to compare the best obtained results with respect to four registration methods found in the specialized literature. The obtained results show a robust and competitive performance of the proposed approach with a significant lower time with respect to the compared methods.

Cardiac magnetic resonance imaging

African Journals Online (AJOL)

2011-03-06

Mar 6, 2011 ... Cardiac magnetic resonance imaging. Cardiovascular magnetic resonance imaging is becoming a routine diagnostic technique. BRUCE s sPOTTiswOOdE, PhD. MRC/UCT Medical Imaging Research Unit, University of Cape Town, and Division of Radiology, Stellenbosch University. Bruce Spottiswoode ...

Discontinuity Preserving Image Registration through Motion Segmentation: A Primal-Dual Approach

Directory of Open Access Journals (Sweden)

Silja Kiriyanthan

2016-01-01

Full Text Available Image registration is a powerful tool in medical image analysis and facilitates the clinical routine in several aspects. There are many well established elastic registration methods, but none of them can so far preserve discontinuities in the displacement field. These discontinuities appear in particular at organ boundaries during the breathing induced organ motion. In this paper, we exploit the fact that motion segmentation could play a guiding role during discontinuity preserving registration. The motion segmentation is embedded in a continuous cut framework guaranteeing convexity for motion segmentation. Furthermore we show that a primal-dual method can be used to estimate a solution to this challenging variational problem. Experimental results are presented for MR images with apparent breathing induced sliding motion of the liver along the abdominal wall.

An object-oriented framework for medical image registration, fusion, and visualization.

Science.gov (United States)

Zhu, Yang-Ming; Cochoff, Steven M

2006-06-01

An object-oriented framework for image registration, fusion, and visualization was developed based on the classic model-view-controller paradigm. The framework employs many design patterns to facilitate legacy code reuse, manage software complexity, and enhance the maintainability and portability of the framework. Three sample applications built a-top of this framework are illustrated to show the effectiveness of this framework: the first one is for volume image grouping and re-sampling, the second one is for 2D registration and fusion, and the last one is for visualization of single images as well as registered volume images.

Retinal image registration for eye movement estimation.

Science.gov (United States)

Kolar, Radim; Tornow, Ralf P; Odstrcilik, Jan

2015-01-01

This paper describes a novel methodology for eye fixation measurement using a unique videoophthalmoscope setup and advanced image registration approach. The representation of the eye movements via Poincare plot is also introduced. The properties, limitations and perspective of this methodology are finally discussed.

Magnetic Resonance Imaging (MRI) -- Head

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Full Text Available ... News Physician Resources Professions Site Index A-Z Magnetic Resonance Imaging (MRI) - Head Magnetic resonance imaging (MRI) of the head uses a powerful ... the Head? What is MRI of the Head? Magnetic resonance imaging (MRI) is a noninvasive medical test that ...

Evaluation of whole-body MR to CT deformable image registration.

Science.gov (United States)

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

2013-07-08

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 on B-spline transformation was performed using optimized parameters of the elastix package based on the Insight Toolkit (ITK) framework. Twenty-eight (17 male and 11 female) clinical studies were used in this work. The registration was evaluated using anatomical landmarks and segmented organs. In addition to 16 anatomical landmarks, three key organs (brain, lungs, and kidneys) and the entire body volume were segmented for evaluation. Several parameters--such as the Euclidean distance between anatomical landmarks, target overlap, Dice and Jaccard coefficients, false positives and false negatives, volume similarity, distance error, and Hausdorff distance--were calculated to quantify the quality of the registration algorithm. Dice coefficients for the majority of patients (> 75%) were in the 0.8-1 range for the whole body, brain, and lungs, which satisfies the criteria to achieve excellent alignment. On the other hand, for kidneys, Dice coefficients for volumes of 25% of the patients meet excellent volume agreement requirement, while the majority of patients satisfy good agreement criteria (> 0.6). For all patients, the distance error was in 0-10 mm range for all segmented organs. In summary, we optimized and evaluated the accuracy of an MR to CT deformable registration algorithm. The registered images constitute a useful 3D whole-body MR-CT atlas suitable for the development and evaluation of novel MR-guided attenuation correction procedures on hybrid PET-MR systems.

Fast Parallel Image Registration on CPU and GPU for Diagnostic Classification of Alzheimer's Disease

Directory of Open Access Journals (Sweden)

Denis P Shamonin

2014-01-01

Full Text Available Nonrigid image registration is an important, but time-consuming taskin medical image analysis. In typical neuroimaging studies, multipleimage registrations are performed, i.e. for atlas-based segmentationor template construction. Faster image registration routines wouldtherefore be beneficial.In this paper we explore acceleration of the image registrationpackage elastix by a combination of several techniques: iparallelization on the CPU, to speed up the cost function derivativecalculation; ii parallelization on the GPU building on andextending the OpenCL framework from ITKv4, to speed up the Gaussianpyramid computation and the image resampling step; iii exploitationof certain properties of the B-spline transformation model; ivfurther software optimizations.The accelerated registration tool is employed in a study ondiagnostic classification of Alzheimer's disease and cognitivelynormal controls based on T1-weighted MRI. We selected 299participants from the publicly available Alzheimer's DiseaseNeuroimaging Initiative database. Classification is performed with asupport vector machine based on gray matter volumes as a marker foratrophy. We evaluated two types of strategies (voxel-wise andregion-wise that heavily rely on nonrigid image registration.Parallelization and optimization resulted in an acceleration factorof 4-5x on an 8-core machine. Using OpenCL a speedup factor of ~2was realized for computation of the Gaussian pyramids, and 15-60 forthe resampling step, for larger images. The voxel-wise and theregion-wise classification methods had an area under thereceiver operator characteristic curve of 88% and 90%,respectively, both for standard and accelerated registration.We conclude that the image registration package elastix wassubstantially accelerated, with nearly identical results to thenon-optimized version. The new functionality will become availablein the next release of elastix as open source under the BSD license.

Combined preclinical magnetic particle imaging and magnetic resonance imaging. Initial results in mice

International Nuclear Information System (INIS)

Kaul, M.G.; Mummert, T.; Jung, C.; Raabe, N.; Ittrich, H.; Adam, G.; Heinen, U.; Reitmeier, A.

2015-01-01

Magnetic particle imaging (MPI) is a new radiologic imaging modality. For the first time, a commercial preclinical scanner is installed. The goal of this study was to establish a workflow between MPI and magnetic resonance imaging (MRI) scanners for a complete in vivo examination of a mouse and to generate the first co-registered in vivo MR-MP images. The in vivo examination of five mice were performed on a preclinical MPI scanner and a 7 Tesla preclinical MRI system. MRI measurements were used for anatomical referencing and validation of the injection of superparamagnetic iron oxide (SPIO) particles during a dynamic MPI scan. We extracted MPI data of the injection phase and co-registered it with MRI data. A workflow process for a combined in vivo MRI and MPI examination was established. A successful injection of ferucarbotran was proven in MPI and MRI. MR-MPI co-registration allocated the SPIOs in the inferior vena cava and the heart during and shortly after the injection. The acquisition of preclinical MPI and MRI data is feasible and allows the combined analysis of MR-MPI information.

Combined preclinical magnetic particle imaging and magnetic resonance imaging. Initial results in mice

Energy Technology Data Exchange (ETDEWEB)

Kaul, M.G.; Mummert, T.; Jung, C.; Raabe, N.; Ittrich, H.; Adam, G. [University Medical Center Hamburg-Eppendorf, Hamburg (Germany). Dept. of Diagnostic and Interventional Radiology; Weber, O. [Philips Medical Systems DMC GmbH, Hamburg (Germany); Heinen, U. [Bruker BioSpin MRI GmbH, Ettlingen (Germany); Reitmeier, A. [Medical Center Hamburg-Eppendorf, Hamburg (Germany). Animal Facility; Knopp, T. [University Medical Center Hamburg-Eppendorf, Hamburg (Germany). Dept. of Diagnostic and Interventional Radiology; Hamburg University of Technology, Hamburg (Germany)

2015-05-15

Magnetic particle imaging (MPI) is a new radiologic imaging modality. For the first time, a commercial preclinical scanner is installed. The goal of this study was to establish a workflow between MPI and magnetic resonance imaging (MRI) scanners for a complete in vivo examination of a mouse and to generate the first co-registered in vivo MR-MP images. The in vivo examination of five mice were performed on a preclinical MPI scanner and a 7 Tesla preclinical MRI system. MRI measurements were used for anatomical referencing and validation of the injection of superparamagnetic iron oxide (SPIO) particles during a dynamic MPI scan. We extracted MPI data of the injection phase and co-registered it with MRI data. A workflow process for a combined in vivo MRI and MPI examination was established. A successful injection of ferucarbotran was proven in MPI and MRI. MR-MPI co-registration allocated the SPIOs in the inferior vena cava and the heart during and shortly after the injection. The acquisition of preclinical MPI and MRI data is feasible and allows the combined analysis of MR-MPI information.

LINE-BASED REGISTRATION OF DSM AND HYPERSPECTRAL IMAGES

Directory of Open Access Journals (Sweden)

J. Avbelj

2013-04-01

Full Text Available Data fusion techniques require a good registration of all the used datasets. In remote sensing, images are usually geo-referenced using the GPS and IMU data. However, if more precise registration is required, image processing techniques can be employed. We propose a method for multi-modal image coregistration between hyperspectral images (HSI and digital surface models (DSM. The method is divided in three parts: object and line detection of the same object in HSI and DSM, line matching and determination of transformation parameters. Homogeneous coordinates are used to implement matching and adjustment of transformation parameters. The common object in HSI and DSM are building boundaries. They have apparent change in height and material, that can be detected in DSM and HSI, respectively. Thus, before the matching and transformation parameter computation, building outlines are detected and adjusted in HSI and DSM. We test the method on a HSI and two DSM, using extracted building outbounds and for comparison also extracted lines with a line detector. The results show that estimated building boundaries provide more line assignments, than using line detector.

3D/2D model-to-image registration by imitation learning for cardiac procedures.

Science.gov (United States)

Toth, Daniel; Miao, Shun; Kurzendorfer, Tanja; Rinaldi, Christopher A; Liao, Rui; Mansi, Tommaso; Rhode, Kawal; Mountney, Peter

2018-05-12

In cardiac interventions, such as cardiac resynchronization therapy (CRT), image guidance can be enhanced by involving preoperative models. Multimodality 3D/2D registration for image guidance, however, remains a significant research challenge for fundamentally different image data, i.e., MR to X-ray. Registration methods must account for differences in intensity, contrast levels, resolution, dimensionality, field of view. Furthermore, same anatomical structures may not be visible in both modalities. Current approaches have focused on developing modality-specific solutions for individual clinical use cases, by introducing constraints, or identifying cross-modality information manually. Machine learning approaches have the potential to create more general registration platforms. However, training image to image methods would require large multimodal datasets and ground truth for each target application. This paper proposes a model-to-image registration approach instead, because it is common in image-guided interventions to create anatomical models for diagnosis, planning or guidance prior to procedures. An imitation learning-based method, trained on 702 datasets, is used to register preoperative models to intraoperative X-ray images. Accuracy is demonstrated on cardiac models and artificial X-rays generated from CTs. The registration error was [Formula: see text] on 1000 test cases, superior to that of manual ([Formula: see text]) and gradient-based ([Formula: see text]) registration. High robustness is shown in 19 clinical CRT cases. Besides the proposed methods feasibility in a clinical environment, evaluation has shown good accuracy and high robustness indicating that it could be applied in image-guided interventions.

Facilitating tumor functional assessment by spatially relating 3D tumor histology and in vivo MRI: image registration approach.

Directory of Open Access Journals (Sweden)

Lejla Alic

Full Text Available BACKGROUND: Magnetic resonance imaging (MRI, together with histology, is widely used to diagnose and to monitor treatment in oncology. Spatial correspondence between these modalities provides information about the ability of MRI to characterize cancerous tissue. However, registration is complicated by deformations during pathological processing, and differences in scale and information content. METHODOLOGY/PRINCIPAL FINDINGS: This study proposes a methodology for establishing an accurate 3D relation between histological sections and high resolution in vivo MRI tumor data. The key features of the methodology are: 1 standardized acquisition and processing, 2 use of an intermediate ex vivo MRI, 3 use of a reference cutting plane, 4 dense histological sampling, 5 elastic registration, and 6 use of complete 3D data sets. Five rat pancreatic tumors imaged by T2*-w MRI were used to evaluate the proposed methodology. The registration accuracy was assessed by root mean squared (RMS distances between manually annotated landmark points in both modalities. After elastic registration the average RMS distance decreased from 1.4 to 0.7 mm. The intermediate ex vivo MRI and the reference cutting plane shared by all three 3D images (in vivo MRI, ex vivo MRI, and 3D histology data were found to be crucial for the accurate co-registration between the 3D histological data set and in vivo MRI. The MR intensity in necrotic regions, as manually annotated in 3D histology, was significantly different from other histologically confirmed regions (i.e., viable and hemorrhagic. However, the viable and the hemorrhagic regions showed a large overlap in T2(*-w MRI signal intensity. CONCLUSIONS: The established 3D correspondence between tumor histology and in vivo MRI enables extraction of MRI characteristics for histologically confirmed regions. The proposed methodology allows the creation of a tumor database of spatially registered multi-spectral MR images and multi-stained 3D

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

One registration multi-atlas-based pseudo-CT generation for attenuation correction in PET/MRI

DEFF Research Database (Denmark)

Arabi, H.; Zaidi, H.

2016-01-01

Purpose: The outcome of a detailed assessment of various strategies for atlas-based whole-body bone segmentation from magnetic resonance imaging (MRI) was exploited to select the optimal parameters and setting, with the aim of proposing a novel one-registration multi-atlas (ORMA) pseudo-CT genera......Purpose: The outcome of a detailed assessment of various strategies for atlas-based whole-body bone segmentation from magnetic resonance imaging (MRI) was exploited to select the optimal parameters and setting, with the aim of proposing a novel one-registration multi-atlas (ORMA) pseudo...... regarding the accuracy of extracted bone using the different techniques demonstrated the superiority of the VWW atlas fusion algorithm achieving a Dice similarity measure of 0.82 ± 0.04 compared to arithmetic averaging atlas fusion (0.60 ± 0.02), which uses conventional direct registration. Application...

Attenuation correction for flexible magnetic resonance coils in combined magnetic resonance/positron emission tomography imaging.

Science.gov (United States)

Eldib, Mootaz; Bini, Jason; Calcagno, Claudia; Robson, Philip M; Mani, Venkatesh; Fayad, Zahi A

2014-02-01

Attenuation correction for magnetic resonance (MR) coils is a new challenge that came about with the development of combined MR and positron emission tomography (PET) imaging. This task is difficult because such coils are not directly visible on either PET or MR acquisitions with current combined scanners and are therefore not easily localized in the field of view. This issue becomes more evident when trying to localize flexible MR coils (eg, cardiac or body matrix coil) that change position and shape from patient to patient and from one imaging session to another. In this study, we proposed a novel method to localize and correct for the attenuation and scatter of a flexible MR cardiac coil, using MR fiducial markers placed on the surface of the coil to allow for accurate registration of a template computed tomography (CT)-based attenuation map. To quantify the attenuation properties of the cardiac coil, a uniform cylindrical water phantom injected with 18F-fluorodeoxyglucose (18F-FDG) was imaged on a sequential MR/PET system with and without the flexible cardiac coil. After establishing the need to correct for the attenuation of the coil, we tested the feasibility of several methods to register a precomputed attenuation map to correct for the attenuation. To accomplish this, MR and CT visible markers were placed on the surface of the cardiac flexible coil. Using only the markers as a driver for registration, the CT image was registered to the reference image through a combination of rigid and deformable registration. The accuracy of several methods was compared for the deformable registration, including B-spline, thin-plate spline, elastic body spline, and volume spline. Finally, we validated our novel approach both in phantom and patient studies. The findings from the phantom experiments indicated that the presence of the coil resulted in a 10% reduction in measured 18F-FDG activity when compared with the phantom-only scan. Local underestimation reached 22% in

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.

3D nonrigid medical image registration using a new information theoretic measure

Science.gov (United States)

Li, Bicao; Yang, Guanyu; Coatrieux, Jean Louis; Li, Baosheng; Shu, Huazhong

2015-11-01

This work presents a novel method for the nonrigid registration of medical images based on the Arimoto entropy, a generalization of the Shannon entropy. The proposed method employed the Jensen-Arimoto divergence measure as a similarity metric to measure the statistical dependence between medical images. Free-form deformations were adopted as the transformation model and the Parzen window estimation was applied to compute the probability distributions. A penalty term is incorporated into the objective function to smooth the nonrigid transformation. The goal of registration is to optimize an objective function consisting of a dissimilarity term and a penalty term, which would be minimal when two deformed images are perfectly aligned using the limited memory BFGS optimization method, and thus to get the optimal geometric transformation. To validate the performance of the proposed method, experiments on both simulated 3D brain MR images and real 3D thoracic CT data sets were designed and performed on the open source elastix package. For the simulated experiments, the registration errors of 3D brain MR images with various magnitudes of known deformations and different levels of noise were measured. For the real data tests, four data sets of 4D thoracic CT from four patients were selected to assess the registration performance of the method, including ten 3D CT images for each 4D CT data covering an entire respiration cycle. These results were compared with the normalized cross correlation and the mutual information methods and show a slight but true improvement in registration accuracy.

3D nonrigid medical image registration using a new information theoretic measure

International Nuclear Information System (INIS)

Li, Bicao; Yang, Guanyu; Coatrieux, Jean Louis; Li, Baosheng; Shu, Huazhong

2015-01-01

This work presents a novel method for the nonrigid registration of medical images based on the Arimoto entropy, a generalization of the Shannon entropy. The proposed method employed the Jensen–Arimoto divergence measure as a similarity metric to measure the statistical dependence between medical images. Free-form deformations were adopted as the transformation model and the Parzen window estimation was applied to compute the probability distributions. A penalty term is incorporated into the objective function to smooth the nonrigid transformation. The goal of registration is to optimize an objective function consisting of a dissimilarity term and a penalty term, which would be minimal when two deformed images are perfectly aligned using the limited memory BFGS optimization method, and thus to get the optimal geometric transformation. To validate the performance of the proposed method, experiments on both simulated 3D brain MR images and real 3D thoracic CT data sets were designed and performed on the open source elastix package. For the simulated experiments, the registration errors of 3D brain MR images with various magnitudes of known deformations and different levels of noise were measured. For the real data tests, four data sets of 4D thoracic CT from four patients were selected to assess the registration performance of the method, including ten 3D CT images for each 4D CT data covering an entire respiration cycle. These results were compared with the normalized cross correlation and the mutual information methods and show a slight but true improvement in registration accuracy. (paper)

Image Navigation and Registration Performance Assessment Tool Set for the GOES-R Advanced Baseline Imager and Geostationary Lightning Mapper

Science.gov (United States)

De Luccia, Frank J.; Houchin, Scott; Porter, Brian C.; Graybill, Justin; Haas, Evan; Johnson, Patrick D.; Isaacson, Peter J.; Reth, Alan D.

2016-01-01

The GOES-R Flight Project has developed an Image Navigation and Registration (INR) Performance Assessment Tool Set (IPATS) for measuring Advanced Baseline Imager (ABI) and Geostationary Lightning Mapper (GLM) INR performance metrics in the post-launch period for performance evaluation and long term monitoring. For ABI, these metrics are the 3-sigma errors in navigation (NAV), channel-to-channel registration (CCR), frame-to-frame registration (FFR), swath-to-swath registration (SSR), and within frame registration (WIFR) for the Level 1B image products. For GLM, the single metric of interest is the 3-sigma error in the navigation of background images (GLM NAV) used by the system to navigate lightning strikes. 3-sigma errors are estimates of the 99.73rd percentile of the errors accumulated over a 24-hour data collection period. IPATS utilizes a modular algorithmic design to allow user selection of data processing sequences optimized for generation of each INR metric. This novel modular approach minimizes duplication of common processing elements, thereby maximizing code efficiency and speed. Fast processing is essential given the large number of sub-image registrations required to generate INR metrics for the many images produced over a 24-hour evaluation period. Another aspect of the IPATS design that vastly reduces execution time is the off-line propagation of Landsat based truth images to the fixed grid coordinates system for each of the three GOES-R satellite locations, operational East and West and initial checkout locations. This paper describes the algorithmic design and implementation of IPATS and provides preliminary test results.

Registration of planar bioluminescence to magnetic resonance and x-ray computed tomography images as a platform for the development of bioluminescence tomography reconstruction algorithms.

Science.gov (United States)

Beattie, Bradley J; Klose, Alexander D; Le, Carl H; Longo, Valerie A; Dobrenkov, Konstantine; Vider, Jelena; Koutcher, Jason A; Blasberg, Ronald G

2009-01-01

The procedures we propose make possible the mapping of two-dimensional (2-D) bioluminescence image (BLI) data onto a skin surface derived from a three-dimensional (3-D) anatomical modality [magnetic resonance (MR) or computed tomography (CT)] dataset. This mapping allows anatomical information to be incorporated into bioluminescence tomography (BLT) reconstruction procedures and, when applied using sources visible to both optical and anatomical modalities, can be used to evaluate the accuracy of those reconstructions. Our procedures, based on immobilization of the animal and a priori determined fixed projective transforms, should be more robust and accurate than previously described efforts, which rely on a poorly constrained retrospectively determined warping of the 3-D anatomical information. Experiments conducted to measure the accuracy of the proposed registration procedure found it to have a mean error of 0.36+/-0.23 mm. Additional experiments highlight some of the confounds that are often overlooked in the BLT reconstruction process, and for two of these confounds, simple corrections are proposed.

Magnetic resonance imaging

International Nuclear Information System (INIS)

Anon.

1988-01-01

Magnetic resonance imaging (MRI) is a new and innovative technique that affords anatomic images in multiple planes and that may provide information about tissue characterization. The magnetic resonance images are obtained by placing the patient or the area of interest within a powerful, highly uniform, static magnetic field. Magnetized protons (hydrogen nuclei) within the patient align like small magnets in this field. Radiofrequency pulses are then used to create an oscillating magnetic field perpendicular to the main field. Magnetic resonance images differ from those produced by x-rays: the latter are associated with absorption of x-ray energy while magnetic resonance images are based on proton density and proton relaxation dynamics. Proton characteristics vary according to the tissue under examination and reflect its physical and chemical properties. To resolve issues regarding safety and efficacy, the Warren Grant Magnuson Clinical Center and the Office of Medical Applications of Research of the National Institutes of Health (NIH) convened a consensus conference about MRI Oct 26 through 28, 1987. At the NIH, the Consensus Development Conference brings together investigators in the biomedical sciences, clinical investigators, practicing physicians, and consumer and special interest groups to make a scientific assessment of technologies, including drugs, devices, and procedures, and to seek agreement on their safety and effectiveness

Clinical assessment of SPECT/CT co-registration image fusion

International Nuclear Information System (INIS)

Zhou Wen; Luan Zhaosheng; Peng Yong

2004-01-01

Objective: Study the methodology of the SPECT/CT co-registration image fusion, and Assessment the Clinical application value. Method: 172 patients who underwent SPECT/CT image fusion during 2001-2003 were studied, 119 men, 53 women. 51 patients underwent 18FDG image +CT, 26 patients underwent 99m Tc-RBC Liver pool image +CT, 43 patients underwent 99mTc-MDP Bone image +CT, 18 patients underwent 99m Tc-MAA Lung perfusion image +CT. The machine is Millium VG SPECT of GE Company. All patients have been taken three steps image: X-ray survey, X-ray transmission and nuclear emission image (Including planer imaging, SPECT or 18 F-FDG of dual head camera) without changing the position of the patients. We reconstruct the emission image with X-ray map and do reconstruction, 18FDG with COSEM and 99mTc with OSEM. Then combine the transmission image and the reconstructed emission image. We use different process parameters in deferent image methods. The accurate rate of SPECT/CT image fusion were statistics, and compare their accurate with that of single nuclear emission image. Results: The nuclear image which have been reconstructed by X-ray attenuation and OSEM are apparent better than pre-reconstructed. The post-reconstructed emission images have no scatter lines around the organs. The outline between different issues is more clear than before. The validity of All post-reconstructed images is better than pre-reconstructed. SPECT/CT image fusion make localization have worthy bases. 138 patients, the accuracy of SPECT/CT image fusion is 91.3% (126/138), whereas 60(88.2%) were found through SPECT/CT image fusion, There are significant difference between them(P 99m Tc- RBC-SPECT +CT image fusion, but 21 of them were inspected by emission image. In BONE 99m Tc -MDP-SPECT +CT image fusion, 4 patients' removed bone(1-6 months after surgery) and their relay with normal bone had activity, their morphologic and density in CT were different from normal bones. 11 of 20 patients who could

Evaluation of whole‐body MR to CT deformable image registration

Science.gov (United States)

Akbarzadeh, A.; Gutierrez, D.; Baskin, A.; Ay, M.R.; Ahmadian, A.; Alam, N. Riahi; Lövblad, KO

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 on B‐spline transformation was performed using optimized parameters of the elastix package based on the Insight Toolkit (ITK) framework. Twenty‐eight (17 male and 11 female) clinical studies were used in this work. The registration was evaluated using anatomical landmarks and segmented organs. In addition to 16 anatomical landmarks, three key organs (brain, lungs, and kidneys) and the entire body volume were segmented for evaluation. Several parameters — such as the Euclidean distance between anatomical landmarks, target overlap, Dice and Jaccard coefficients, false positives and false negatives, volume similarity, distance error, and Hausdorff distance — were calculated to quantify the quality of the registration algorithm. Dice coefficients for the majority of patients (>75%) were in the 0.8–1 range for the whole body, brain, and lungs, which satisfies the criteria to achieve excellent alignment. On the other hand, for kidneys, Dice coefficients for volumes of 25% of the patients meet excellent volume agreement requirement, while the majority of patients satisfy good agreement criteria (>0.6). For all patients, the distance error was in 0–10 mm range for all segmented organs. In summary, we optimized and evaluated the accuracy of an MR to CT deformable registration algorithm. The registered images constitute a useful 3D whole‐body MR‐CT atlas suitable for the development and evaluation of novel MR‐guided attenuation correction procedures on hybrid PET‐MR systems. PACS number: 07.05.Pj PMID:23835382

Deformable image registration in radiation therapy

Energy Technology Data Exchange (ETDEWEB)

Oh, Seung Jong; Kim, Si Yong [Dept. of Radiation Oncology, Virginia Commonwealth University, Richmond (United States)

2017-06-15

The number of imaging data sets has significantly increased during radiation treatment after introducing a diverse range of advanced techniques into the field of radiation oncology. As a consequence, there have been many studies proposing meaningful applications of imaging data set use. These applications commonly require a method to align the data sets at a reference. Deformable image registration (DIR) is a process which satisfies this requirement by locally registering image data sets into a reference image set. DIR identifies the spatial correspondence in order to minimize the differences between two or among multiple sets of images. This article describes clinical applications, validation, and algorithms of DIR techniques. Applications of DIR in radiation treatment include dose accumulation, mathematical modeling, automatic segmentation, and functional imaging. Validation methods discussed are based on anatomical landmarks, physical phantoms, digital phantoms, and per application purpose. DIR algorithms are also briefly reviewed with respect to two algorithmic components: similarity index and deformation models.

Real-time registration of 3D to 2D ultrasound images for image-guided prostate biopsy.

Science.gov (United States)

Gillies, Derek J; Gardi, Lori; De Silva, Tharindu; Zhao, Shuang-Ren; Fenster, Aaron

2017-09-01

During image-guided prostate biopsy, needles are targeted at tissues that are suspicious of cancer to obtain specimen for histological examination. Unfortunately, patient motion causes targeting errors when using an MR-transrectal ultrasound (TRUS) fusion approach to augment the conventional biopsy procedure. This study aims to develop an automatic motion correction algorithm approaching the frame rate of an ultrasound system to be used in fusion-based prostate biopsy systems. Two modes of operation have been investigated for the clinical implementation of the algorithm: motion compensation using a single user initiated correction performed prior to biopsy, and real-time continuous motion compensation performed automatically as a background process. Retrospective 2D and 3D TRUS patient images acquired prior to biopsy gun firing were registered using an intensity-based algorithm utilizing normalized cross-correlation and Powell's method for optimization. 2D and 3D images were downsampled and cropped to estimate the optimal amount of image information that would perform registrations quickly and accurately. The optimal search order during optimization was also analyzed to avoid local optima in the search space. Error in the algorithm was computed using target registration errors (TREs) from manually identified homologous fiducials in a clinical patient dataset. The algorithm was evaluated for real-time performance using the two different modes of clinical implementations by way of user initiated and continuous motion compensation methods on a tissue mimicking prostate phantom. After implementation in a TRUS-guided system with an image downsampling factor of 4, the proposed approach resulted in a mean ± std TRE and computation time of 1.6 ± 0.6 mm and 57 ± 20 ms respectively. The user initiated mode performed registrations with in-plane, out-of-plane, and roll motions computation times of 108 ± 38 ms, 60 ± 23 ms, and 89 ± 27 ms, respectively, and corresponding

Robust surface registration using salient anatomical features for image-guided liver surgery: Algorithm and validation

OpenAIRE

Clements, Logan W.; Chapman, William C.; Dawant, Benoit M.; Galloway, Robert L.; Miga, Michael I.

2008-01-01

A successful surface-based image-to-physical space registration in image-guided liver surgery (IGLS) is critical to provide reliable guidance information to surgeons and pertinent surface displacement data for use in deformation correction algorithms. The current protocol used to perform the image-to-physical space registration involves an initial pose estimation provided by a point based registration of anatomical landmarks identifiable in both the preoperative tomograms and the intraoperati...

Evaluation of permanent I-125 prostate implants using radiography and magnetic resonance imaging

International Nuclear Information System (INIS)

Moerland, Marinus A.; Wijrdeman, Harm K.; Beersma, Robert; Bakker, Chris J.G.; Battermann, Jan J.

1997-01-01

Purpose: The aim of this study is the evaluation of permanent I-125 prostate implants using radiography and magnetic resonance imaging (MRI). Methods and Materials: Twenty-one patients underwent radiography on the simulator and MRI within 3 days after implantation of the I-125 seeds. Isocentric radiographs were used for reconstruction of the seed distribution, after which registration with the seed-induced signal voids on MRI provided the seed positions in relation to the prostate. The prostate was contoured on the transversal magnetic resonance images, and dose-volume histograms were computed to evaluate the implants. The validity of the ellipsoidal prostate volume approximation, as applied in preimplant dose calculation, was assessed by comparison of ellipsoidal volumes given by prostate width, height, and length and prostate volumes obtained by a slice-by-slice contouring method, both on postimplant MRI. Prostate volume changes due to postimplant prostate swelling were assessed from radiographs taken at 3 days and 1 month after the implantation. Results: The seeds were readily identified on T 1 -weighted spin-echo images and matched with the seed distribution reconstructed from the isocentric radiographs. The matching error, averaged over 21 patients, amounted to 1.8 ± 0.4 mm (mean ± standard deviation). The fractions of the prostate volumes receiving the prescribed matched peripheral dose (MPD) ranged from 32 to 71% (mean ± standard deviation: 60 ± 10%). Prostate volumes, obtained by the contouring method on postimplant MRI, were a factor 1.5 ± 0.3 larger than the ellipsoidal volumes given by the prostate dimensions on postimplant MRI. Prostate volumes 3 days after the implantation were a factor 1.3 ± 0.2 larger than the prostate volumes 1 month after the implantation. Registration of the reconstructed seed distribution and the MR images showed inaccuracies in seed placement, for example, two or more seeds clustering together or seeds outside the prostate

Quality assurance of CT-PET alignment and image registration for radiation treatment planning

International Nuclear Information System (INIS)

Gong, S.J.; O'Keefe, G.J.; Gunawardana, D.H.

2005-01-01

A multi-layer point source phantom was first used to calibrate and verify the CT-PET system alignment. A partial whole-body Aldcrson RANDO Man Phantom (head through mid-femur) was externally and internally marked with small metal cannulas filled with 18F-FDG and then scanned with both modalities. Six series of phantom studies with different acquisition settings and scan positions were performed to reveal possible system bias and evaluate the accuracy and reliabilities of Philips Syntegra program in image alignment, coregistration and fusion. The registration error was assessed quantitatively by measuring the root-mean-square distance between the iso-centers of corresponding fiducial marker geometries in reference CT volumes and transformed CT or PET volumes. Results: Experimental data confirms the accuracy of manual, parameter, point and image-based registration using Syntegra is better than 2 mm. Comparisons between blind and cross definition of iso-centers of fiducial marks indicate that the fused CT and PET is superior to visual correlation of CT and PET side-by-side. Conclusion: In this work we demonstrate the QA procedures of Gemini image alignment and registration. Syntegra produces intrinsic and robust multi-modality image registration and fusion with careful user interaction. The registration accuracy is generally better than the spatial resolution of the PET scanner used and this appears to be sufficient for most RTP CT-PET registration procedures

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

Semi-automatic construction of reference standards for evaluation of image registration

NARCIS (Netherlands)

Murphy, K.; Ginneken, van B.; Klein, S.; Staring, M.; Hoop, de B.J.; Viergever, M.A.; Pluim, J.P.W.

2011-01-01

Quantitative evaluation of image registration algorithms is a difficult and under-addressed issue due to the lack of a reference standard in most registration problems. In this work a method is presented whereby detailed reference standard data may be constructed in an efficient semi-automatic

Weight preserving image registration for monitoring disease progression in lung CT

DEFF Research Database (Denmark)

Gorbunova, Vladlena; Lo, Pechin Chien Pau; Haseem, Ashraf

2008-01-01

We present a new image registration based method for monitoring regional disease progression in longitudinal image studies of lung disease. A free-form image registration technique is used to match a baseline 3D CT lung scan onto a following scan. Areas with lower intensity in the following scan...... the density of lung tissue with respect to local expansion or compression such that the total weight of the lungs is preserved during deformation. Our method provides a good estimation of regional destruction of lung tissue for subjects with a significant difference in inspiration level between CT scans...

Band co-registration modeling of LAPAN-A3/IPB multispectral imager based on satellite attitude

Science.gov (United States)

Hakim, P. R.; Syafrudin, A. H.; Utama, S.; Jayani, A. P. S.

2018-05-01

One of significant geometric distortion on images of LAPAN-A3/IPB multispectral imager is co-registration error between each color channel detector. Band co-registration distortion usually can be corrected by using several approaches, which are manual method, image matching algorithm, or sensor modeling and calibration approach. This paper develops another approach to minimize band co-registration distortion on LAPAN-A3/IPB multispectral image by using supervised modeling of image matching with respect to satellite attitude. Modeling results show that band co-registration error in across-track axis is strongly influenced by yaw angle, while error in along-track axis is fairly influenced by both pitch and roll angle. Accuracy of the models obtained is pretty good, which lies between 1-3 pixels error for each axis of each pair of band co-registration. This mean that the model can be used to correct the distorted images without the need of slower image matching algorithm, nor the laborious effort needed in manual approach and sensor calibration. Since the calculation can be executed in order of seconds, this approach can be used in real time quick-look image processing in ground station or even in satellite on-board image processing.

Intra-operative fiducial-based CT/fluoroscope image registration framework for image-guided robot-assisted joint fracture surgery.

Science.gov (United States)

Dagnino, Giulio; Georgilas, Ioannis; Morad, Samir; Gibbons, Peter; Tarassoli, Payam; Atkins, Roger; Dogramadzi, Sanja

2017-08-01

Joint fractures must be accurately reduced minimising soft tissue damages to avoid negative surgical outcomes. To this regard, we have developed the RAFS surgical system, which allows the percutaneous reduction of intra-articular fractures and provides intra-operative real-time 3D image guidance to the surgeon. Earlier experiments showed the effectiveness of the RAFS system on phantoms, but also key issues which precluded its use in a clinical application. This work proposes a redesign of the RAFS's navigation system overcoming the earlier version's issues, aiming to move the RAFS system into a surgical environment. The navigation system is improved through an image registration framework allowing the intra-operative registration between pre-operative CT images and intra-operative fluoroscopic images of a fractured bone using a custom-made fiducial marker. The objective of the registration is to estimate the relative pose between a bone fragment and an orthopaedic manipulation pin inserted into it intra-operatively. The actual pose of the bone fragment can be updated in real time using an optical tracker, enabling the image guidance. Experiments on phantom and cadavers demonstrated the accuracy and reliability of the registration framework, showing a reduction accuracy (sTRE) of about [Formula: see text] (phantom) and [Formula: see text] (cadavers). Four distal femur fractures were successfully reduced in cadaveric specimens using the improved navigation system and the RAFS system following the new clinical workflow (reduction error [Formula: see text], [Formula: see text]. Experiments showed the feasibility of the image registration framework. It was successfully integrated into the navigation system, allowing the use of the RAFS system in a realistic surgical application.

Multiresolution image registration for multimodal brain images and fusion for better neurosurgical planning

Directory of Open Access Journals (Sweden)

Siddeshappa Nandish

2017-12-01

Conclusion: The end resultant fused images are validated by the radiologists and mutual information measure is used to validate registration results. It is found that CT and MRI sequence with more number of slices gave promising results. Few cases with deformation during misregistrations recorded with low mutual information of about 0.3 and which is not acceptable and few recorded with 0.6 and above mutual information during registration gives promising results.

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.

AN AUTOMATIC OPTICAL AND SAR IMAGE REGISTRATION METHOD USING ITERATIVE MULTI-LEVEL AND REFINEMENT MODEL

Directory of Open Access Journals (Sweden)

C. Xu

2016-06-01

Full Text Available Automatic image registration is a vital yet challenging task, particularly for multi-sensor remote sensing images. Given the diversity of the data, it is unlikely that a single registration algorithm or a single image feature will work satisfactorily for all applications. Focusing on this issue, the mainly contribution of this paper is to propose an automatic optical-to-SAR image registration method using –level and refinement model: Firstly, a multi-level strategy of coarse-to-fine registration is presented, the visual saliency features is used to acquire coarse registration, and then specific area and line features are used to refine the registration result, after that, sub-pixel matching is applied using KNN Graph. Secondly, an iterative strategy that involves adaptive parameter adjustment for re-extracting and re-matching features is presented. Considering the fact that almost all feature-based registration methods rely on feature extraction results, the iterative strategy improve the robustness of feature matching. And all parameters can be automatically and adaptively adjusted in the iterative procedure. Thirdly, a uniform level set segmentation model for optical and SAR images is presented to segment conjugate features, and Voronoi diagram is introduced into Spectral Point Matching (VSPM to further enhance the matching accuracy between two sets of matching points. Experimental results show that the proposed method can effectively and robustly generate sufficient, reliable point pairs and provide accurate registration.

Mass preserving image registration for lung CT

DEFF Research Database (Denmark)

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

2012-01-01

This paper presents a mass preserving image registration algorithm for lung CT images. To account for the local change in lung tissue intensity during the breathing cycle, a tissue appearance model based on the principle of preservation of total lung mass is proposed. This model is incorporated...... on four groups of data: 44 pairs of longitudinal inspiratory chest CT scans with small difference in lung volume; 44 pairs of longitudinal inspiratory chest CT scans with large difference in lung volume; 16 pairs of expiratory and inspiratory CT scans; and 5 pairs of images extracted at end exhale and end...

Unbiased group-wise image registration: applications in brain fiber tract atlas construction and functional connectivity analysis.

Science.gov (United States)

Geng, Xiujuan; Gu, Hong; Shin, Wanyong; Ross, Thomas J; Yang, Yihong

2011-10-01

We propose an unbiased implicit-reference group-wise (IRG) image registration method and demonstrate its applications in the construction of a brain white matter fiber tract atlas and the analysis of resting-state functional MRI (fMRI) connectivity. Most image registration techniques pair-wise align images to a selected reference image and group analyses are performed in the reference space, which may produce bias. The proposed method jointly estimates transformations, with an elastic deformation model, registering all images to an implicit reference corresponding to the group average. The unbiased registration is applied to build a fiber tract atlas by registering a group of diffusion tensor images. Compared to reference-based registration, the IRG registration improves the fiber track overlap within the group. After applying the method in the fMRI connectivity analysis, results suggest a general improvement in functional connectivity maps at a group level in terms of larger cluster size and higher average t-scores.

A first step toward uncovering the truth about weight tuning in deformable image registration

Science.gov (United States)

Pirpinia, Kleopatra; Bosman, Peter A. N.; Sonke, Jan-Jakob; van Herk, Marcel; Alderliesten, Tanja

2016-03-01

Deformable image registration is currently predominantly solved by optimizing a weighted linear combination of objectives. Successfully tuning the weights associated with these objectives is not trivial, leading to trial-and-error approaches. Such an approach assumes an intuitive interplay between weights, optimization objectives, and target registration errors. However, it is not known whether this always holds for existing registration methods. To investigate the interplay between weights, optimization objectives, and registration errors, we employ multi-objective optimization. Here, objectives of interest are optimized simultaneously, causing a set of multiple optimal solutions to exist, called the optimal Pareto front. Our medical application is in breast cancer and includes the challenging prone-supine registration problem. In total, we studied the interplay in three different ways. First, we ran many random linear combinations of objectives using the well-known registration software elastix. Second, since the optimization algorithms used in registration are typically of a local-search nature, final solutions may not always form a Pareto front. We therefore employed a multi-objective evolutionary algorithm that finds weights that correspond to registration outcomes that do form a Pareto front. Third, we examined how the interplay differs if a true multi-objective (i.e., weight-free) image registration method is used. Results indicate that a trial-and-error weight-adaptation approach can be successful for the easy prone to prone breast image registration case, due to the absence of many local optima. With increasing problem difficulty the use of more advanced approaches can be of value in finding and selecting the optimal registration outcomes.

Fully automatic registration and segmentation of first-pass myocardial perfusion MR image sequences.

Science.gov (United States)

Gupta, Vikas; Hendriks, Emile A; Milles, Julien; van der Geest, Rob J; Jerosch-Herold, Michael; Reiber, Johan H C; Lelieveldt, Boudewijn P F

2010-11-01

Derivation of diagnostically relevant parameters from first-pass myocardial perfusion magnetic resonance images involves the tedious and time-consuming manual segmentation of the myocardium in a large number of images. To reduce the manual interaction and expedite the perfusion analysis, we propose an automatic registration and segmentation method for the derivation of perfusion linked parameters. A complete automation was accomplished by first registering misaligned images using a method based on independent component analysis, and then using the registered data to automatically segment the myocardium with active appearance models. We used 18 perfusion studies (100 images per study) for validation in which the automatically obtained (AO) contours were compared with expert drawn contours on the basis of point-to-curve error, Dice index, and relative perfusion upslope in the myocardium. Visual inspection revealed successful segmentation in 15 out of 18 studies. Comparison of the AO contours with expert drawn contours yielded 2.23 ± 0.53 mm and 0.91 ± 0.02 as point-to-curve error and Dice index, respectively. The average difference between manually and automatically obtained relative upslope parameters was found to be statistically insignificant (P = .37). Moreover, the analysis time per slice was reduced from 20 minutes (manual) to 1.5 minutes (automatic). We proposed an automatic method that significantly reduced the time required for analysis of first-pass cardiac magnetic resonance perfusion images. The robustness and accuracy of the proposed method were demonstrated by the high spatial correspondence and statistically insignificant difference in perfusion parameters, when AO contours were compared with expert drawn contours. Copyright © 2010 AUR. Published by Elsevier Inc. All rights reserved.

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.

Automatic Registration Method for Fusion of ZY-1-02C Satellite Images

Directory of Open Access Journals (Sweden)

Qi Chen

2013-12-01

Full Text Available Automatic image registration (AIR has been widely studied in the fields of medical imaging, computer vision, and remote sensing. In various cases, such as image fusion, high registration accuracy should be achieved to meet application requirements. For satellite images, the large image size and unstable positioning accuracy resulting from the limited manufacturing technology of charge-coupled device, focal plane distortion, and unrecorded spacecraft jitter lead to difficulty in obtaining agreeable corresponding points for registration using only area-based matching or feature-based matching. In this situation, a coarse-to-fine matching strategy integrating two types of algorithms is proven feasible and effective. In this paper, an AIR method for application to the fusion of ZY-1-02C satellite imagery is proposed. First, the images are geometrically corrected. Coarse matching, based on scale invariant feature transform, is performed for the subsampled corrected images, and a rough global estimation is made with the matching results. Harris feature points are then extracted, and the coordinates of the corresponding points are calculated according to the global estimation results. Precise matching is conducted, based on normalized cross correlation and least squares matching. As complex image distortion cannot be precisely estimated, a local estimation using the structure of triangulated irregular network is applied to eliminate the false matches. Finally, image resampling is conducted, based on local affine transformation, to achieve high-precision registration. Experiments with ZY-1-02C datasets demonstrate that the accuracy of the proposed method meets the requirements of fusion application, and its efficiency is also suitable for the commercial operation of the automatic satellite data process system.

MREG V1.1 : a multi-scale image registration algorithm for SAR applications.

Energy Technology Data Exchange (ETDEWEB)

Eichel, Paul H.

2013-08-01

MREG V1.1 is the sixth generation SAR image registration algorithm developed by the Signal Processing&Technology Department for Synthetic Aperture Radar applications. Like its predecessor algorithm REGI, it employs a powerful iterative multi-scale paradigm to achieve the competing goals of sub-pixel registration accuracy and the ability to handle large initial offsets. Since it is not model based, it allows for high fidelity tracking of spatially varying terrain-induced misregistration. Since it does not rely on image domain phase, it is equally adept at coherent and noncoherent image registration. This document provides a brief history of the registration processors developed by Dept. 5962 leading up to MREG V1.1, a full description of the signal processing steps involved in the algorithm, and a user's manual with application specific recommendations for CCD, TwoColor MultiView, and SAR stereoscopy.

Children's (Pediatric) Magnetic Resonance Imaging

Medline Plus

Full Text Available ... Resources Professions Site Index A-Z Children’s (Pediatric) Magnetic Resonance Imaging Children’s magnetic resonance imaging (MRI) uses ... identify and accurately characterize diseases than other imaging methods. This detail makes MRI an invaluable tool in ...

Magnetic resonance imaging

International Nuclear Information System (INIS)

Robertson, Angus

1990-01-01

An assessment is made of the clinical benefits of expensive diagnostic technology, such as the magnetic resonance imaging. It is concluded that to most radiologists, magnetic resonance imaging has a definite place in the diagnostic scenario, especially for demonstrating central nervous system lesions in multiple sclerosis. While it is recognized that medical and financial resources are limited, it is emphasised that the cost to society must be balanced against the patient benefit. 17 refs

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

Mesh-to-raster region-of-interest-based nonrigid registration of multimodal images.

Science.gov (United States)

Tatano, Rosalia; Berkels, Benjamin; Deserno, Thomas M

2017-10-01

Region of interest (RoI) alignment in medical images plays a crucial role in diagnostics, procedure planning, treatment, and follow-up. Frequently, a model is represented as triangulated mesh while the patient data is provided from computed axial tomography scanners as pixel or voxel data. Previously, we presented a 2-D method for curve-to-pixel registration. This paper contributes (i) a general mesh-to-raster framework to register RoIs in multimodal images; (ii) a 3-D surface-to-voxel application, and (iii) a comprehensive quantitative evaluation in 2-D using ground truth (GT) provided by the simultaneous truth and performance level estimation (STAPLE) method. The registration is formulated as a minimization problem, where the objective consists of a data term, which involves the signed distance function of the RoI from the reference image and a higher order elastic regularizer for the deformation. The evaluation is based on quantitative light-induced fluoroscopy (QLF) and digital photography (DP) of decalcified teeth. STAPLE is computed on 150 image pairs from 32 subjects, each showing one corresponding tooth in both modalities. The RoI in each image is manually marked by three experts (900 curves in total). In the QLF-DP setting, our approach significantly outperforms the mutual information-based registration algorithm implemented with the Insight Segmentation and Registration Toolkit and Elastix.

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)

WE-H-202-00: Session in Memory of Jean Pouliot: Next-Generation Deformable Image Registration

Energy Technology Data Exchange (ETDEWEB)

NONE

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.

WE-H-202-00: Session in Memory of Jean Pouliot: Next-Generation Deformable Image Registration

International Nuclear Information System (INIS)

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.

Image Registration to Compensate for EPI Distortion in Patients with Brain Tumors: An Evaluation of Tract-Specific Effects.

Science.gov (United States)

Albi, Angela; Meola, Antonio; Zhang, Fan; Kahali, Pegah; Rigolo, Laura; Tax, Chantal M W; Ciris, Pelin Aksit; Essayed, Walid I; Unadkat, Prashin; Norton, Isaiah; Rathi, Yogesh; Olubiyi, Olutayo; Golby, Alexandra J; O'Donnell, Lauren J

2018-03-01

Diffusion magnetic resonance imaging (dMRI) provides preoperative maps of neurosurgical patients' white matter tracts, but these maps suffer from echo-planar imaging (EPI) distortions caused by magnetic field inhomogeneities. In clinical neurosurgical planning, these distortions are generally not corrected and thus contribute to the uncertainty of fiber tracking. Multiple image processing pipelines have been proposed for image-registration-based EPI distortion correction in healthy subjects. In this article, we perform the first comparison of such pipelines in neurosurgical patient data. Five pipelines were tested in a retrospective clinical dMRI dataset of 9 patients with brain tumors. Pipelines differed in the choice of fixed and moving images and the similarity metric for image registration. Distortions were measured in two important tracts for neurosurgery, the arcuate fasciculus and corticospinal tracts. Significant differences in distortion estimates were found across processing pipelines. The most successful pipeline used dMRI baseline and T2-weighted images as inputs for distortion correction. This pipeline gave the most consistent distortion estimates across image resolutions and brain hemispheres. Quantitative results of mean tract distortions on the order of 1-2 mm are in line with other recent studies, supporting the potential need for distortion correction in neurosurgical planning. Novel results include significantly higher distortion estimates in the tumor hemisphere and greater effect of image resolution choice on results in the tumor hemisphere. Overall, this study demonstrates possible pitfalls and indicates that care should be taken when implementing EPI distortion correction in clinical settings. Copyright © 2018 by the American Society of Neuroimaging.

Registration of Images with N-fold Dihedral Blur

Czech Academy of Sciences Publication Activity Database

Pedone, M.; Flusser, Jan; Heikkila, J.

2015-01-01

Roč. 24, č. 3 (2015), s. 1036-1045 ISSN 1057-7149 R&D Projects: GA ČR GA13-29225S; GA ČR GA15-16928S Institutional support: RVO:67985556 Keywords : Image registration * blurred images * N-fold rotational symmetry * dihedral symmetry * phase correlation Subject RIV: JD - Computer Applications, Robotics Impact factor: 3.735, year: 2015 http://library.utia.cas.cz/separaty/2015/ZOI/flusser-0441247.pdf

Registration of Urban Aerial Image and LiDAR Based on Line Vectors

Directory of Open Access Journals (Sweden)

Qinghong Sheng

2017-09-01

Full Text Available In a traditional registration of a single aerial image with airborne light detection and ranging (LiDAR data using linear features that regard line direction as a control or linear features as constraints in the solution, lacking the constraint of linear position leads to the error propagation of the adjustment model. To solve this problem, this paper presents a line vector-based registration mode (LVR in which image rays and LiDAR lines are expressed by a line vector that integrates the line direction and the line position. A registration equation of line vector is set up by coplanar imaging rays and corresponding control lines. Three types of datasets consisting of synthetic, theInternational Society for Photogrammetry and Remote Sensing (ISPRS test project, and real aerial data are used. A group of progressive experiments is undertaken to evaluate the robustness of the LVR. Experimental results demonstrate that the integrated line direction and the line position contributes a great deal to the theoretical and real accuracies of the unknowns, as well as the stability of the adjustment model. This paper provides a new suggestion that, for a single image and LiDAR data, registration in urban areas can be accomplished by accommodating rich line features.

Evaluation of the mutual information cost function for registration of SPET and MRI images of the brain

International Nuclear Information System (INIS)

Taleb, M.; McKay, E.

1999-01-01

Full text: Any strategy for image registration requires some method (a cost function) by which two images may be compared The mutual information (MI) between images is one such cost function. MI measures the structural similarity between pairs of gray-scale images and performs cross-modality image registration with minimal image pre-processing. This project compares the performance of MI vs the sum of absolute differences (SAD) 'gold standard' in monomodality image registration problems. It also examines the precision of cross-modality registration based on MI, using a human observer to decide whether registration is accurate. Thirteen paired brain SPET scans were registered using SAD as a cost function. Registration was repeated using MI and differences from the SAD results were recorded. Ten paired MRI and SPET brain scans registered using the MI cost function. Registration was repeated three times for each pair, varying the SPET position or orientation each time. Comparing MI to SAD, the median values of translation error were 2.85, 4.63 and 2.56 mm in the x, y and z axis and 0.5 j , 1.1 j and 1.0 j around the x, y and z axis respectively. For the cross-modality problems, the mean standard deviation (MSD) observed in x, y and z positioning was 0.18, 0.28 and 0.16 mm respectively. The MSD of orientation was 5.35 j , 1.95 j and 2.48 j around the x, y and z axis respectively. MI performed as well as SAD for monomodality registration. Unlike SAD, MI is also useful for cross-modality image registration tasks, producing visually acceptable results with minimal preprocessing

Automatic registration of fused lidar/digital imagery (texel images) for three-dimensional image creation

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Budge, Scott E.; Badamikar, Neeraj S.; Xie, Xuan

2015-03-01

Several photogrammetry-based methods have been proposed that the derive three-dimensional (3-D) information from digital images from different perspectives, and lidar-based methods have been proposed that merge lidar point clouds and texture the merged point clouds with digital imagery. Image registration alone has difficulty with smooth regions with low contrast, whereas point cloud merging alone has difficulty with outliers and a lack of proper convergence in the merging process. This paper presents a method to create 3-D images that uses the unique properties of texel images (pixel-fused lidar and digital imagery) to improve the quality and robustness of fused 3-D images. The proposed method uses both image processing and point-cloud merging to combine texel images in an iterative technique. Since the digital image pixels and the lidar 3-D points are fused at the sensor level, more accurate 3-D images are generated because registration of image data automatically improves the merging of the point clouds, and vice versa. Examples illustrate the value of this method over other methods. The proposed method also includes modifications for the situation where an estimate of position and attitude of the sensor is known, when obtained from low-cost global positioning systems and inertial measurement units sensors.

Reducing Interpolation Artifacts for Mutual Information Based Image Registration

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Soleimani, H.; Khosravifard, M.A.

2011-01-01

Medical image registration methods which use mutual information as similarity measure have been improved in recent decades. Mutual Information is a basic concept of Information theory which indicates the dependency of two random variables (or two images). In order to evaluate the mutual information of two images their joint probability distribution is required. Several interpolation methods, such as Partial Volume (PV) and bilinear, are used to estimate joint probability distribution. Both of these two methods yield some artifacts on mutual information function. Partial Volume-Hanning window (PVH) and Generalized Partial Volume (GPV) methods are introduced to remove such artifacts. In this paper we show that the acceptable performance of these methods is not due to their kernel function. It's because of the number of pixels which incorporate in interpolation. Since using more pixels requires more complex and time consuming interpolation process, we propose a new interpolation method which uses only four pixels (the same as PV and bilinear interpolations) and removes most of the artifacts. Experimental results of the registration of Computed Tomography (CT) images show superiority of the proposed scheme. PMID:22606673

An Advanced Rotation Invariant Descriptor for SAR Image Registration

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Yuming Xiang

2017-07-01

Full Text Available The Scale-Invariant Feature Transform (SIFT algorithm and its many variants have been widely used in Synthetic Aperture Radar (SAR image registration. The SIFT-like algorithms maintain rotation invariance by assigning a dominant orientation for each keypoint, while the calculation of dominant orientation is not robust due to the effect of speckle noise in SAR imagery. In this paper, we propose an advanced local descriptor for SAR image registration to achieve rotation invariance without assigning a dominant orientation. Based on the improved intensity orders, we first divide a circular neighborhood into several sub-regions. Second, rotation-invariant ratio orientation histograms of each sub-region are proposed by accumulating the ratio values of different directions in a rotation-invariant coordinate system. The proposed descriptor is composed of the concatenation of the histograms of each sub-region. In order to increase the distinctiveness of the proposed descriptor, multiple image neighborhoods are aggregated. Experimental results on several satellite SAR images have shown an improvement in the matching performance over other state-of-the-art algorithms.

A fast inverse consistent deformable image registration method based on symmetric optical flow computation

International Nuclear Information System (INIS)

Yang Deshan; Li Hua; Low, Daniel A; Deasy, Joseph O; Naqa, Issam El

2008-01-01

Deformable image registration is widely used in various radiation therapy applications including daily treatment planning adaptation to map planned tissue or dose to changing anatomy. In this work, a simple and efficient inverse consistency deformable registration method is proposed with aims of higher registration accuracy and faster convergence speed. Instead of registering image I to a second image J, the two images are symmetrically deformed toward one another in multiple passes, until both deformed images are matched and correct registration is therefore achieved. In each pass, a delta motion field is computed by minimizing a symmetric optical flow system cost function using modified optical flow algorithms. The images are then further deformed with the delta motion field in the positive and negative directions respectively, and then used for the next pass. The magnitude of the delta motion field is forced to be less than 0.4 voxel for every pass in order to guarantee smoothness and invertibility for the two overall motion fields that are accumulating the delta motion fields in both positive and negative directions, respectively. The final motion fields to register the original images I and J, in either direction, are calculated by inverting one overall motion field and combining the inversion result with the other overall motion field. The final motion fields are inversely consistent and this is ensured by the symmetric way that registration is carried out. The proposed method is demonstrated with phantom images, artificially deformed patient images and 4D-CT images. Our results suggest that the proposed method is able to improve the overall accuracy (reducing registration error by 30% or more, compared to the original and inversely inconsistent optical flow algorithms), reduce the inverse consistency error (by 95% or more) and increase the convergence rate (by 100% or more). The overall computation speed may slightly decrease, or increase in most cases

MRI to X-ray mammography registration using a volume-preserving affine transformation.

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Mertzanidou, Thomy; Hipwell, John; Cardoso, M Jorge; Zhang, Xiying; Tanner, Christine; Ourselin, Sebastien; Bick, Ulrich; Huisman, Henkjan; Karssemeijer, Nico; Hawkes, David

2012-07-01

X-ray mammography is routinely used in national screening programmes and as a clinical diagnostic tool. Magnetic Resonance Imaging (MRI) is commonly used as a complementary modality, providing functional information about the breast and a 3D image that can overcome ambiguities caused by the superimposition of fibro-glandular structures associated with X-ray imaging. Relating findings between these modalities is a challenging task however, due to the different imaging processes involved and the large deformation that the breast undergoes. In this work we present a registration method to determine spatial correspondence between pairs of MR and X-ray images of the breast, that is targeted for clinical use. We propose a generic registration framework which incorporates a volume-preserving affine transformation model and validate its performance using routinely acquired clinical data. Experiments on simulated mammograms from 8 volunteers produced a mean registration error of 3.8±1.6mm for a mean of 12 manually identified landmarks per volume. When validated using 57 lesions identified on routine clinical CC and MLO mammograms (n=113 registration tasks) from 49 subjects the median registration error was 13.1mm. When applied to the registration of an MR image to CC and MLO mammograms of a patient with a localisation clip, the mean error was 8.9mm. The results indicate that an intensity based registration algorithm, using a relatively simple transformation model, can provide radiologists with a clinically useful tool for breast cancer diagnosis. Copyright © 2012 Elsevier B.V. All rights reserved.

Study on Low Illumination Simultaneous Polarization Image Registration Based on Improved SURF Algorithm

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Zhang, Wanjun; Yang, Xu

2017-12-01

Registration of simultaneous polarization images is the premise of subsequent image fusion operations. However, in the process of shooting all-weather, the polarized camera exposure time need to be kept unchanged, sometimes polarization images under low illumination conditions due to too dark result in SURF algorithm can not extract feature points, thus unable to complete the registration, therefore this paper proposes an improved SURF algorithm. Firstly, the luminance operator is used to improve overall brightness of low illumination image, and then create integral image, using Hession matrix to extract the points of interest to get the main direction of characteristic points, calculate Haar wavelet response in X and Y directions to get the SURF descriptor information, then use the RANSAC function to make precise matching, the function can eliminate wrong matching points and improve accuracy rate. And finally resume the brightness of the polarized image after registration, the effect of the polarized image is not affected. Results show that the improved SURF algorithm can be applied well under low illumination conditions.

Landmark Optimization Using Local Curvature for Point-Based Nonlinear Rodent Brain Image Registration

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Yutong Liu

2012-01-01

Full Text Available Purpose. To develop a technique to automate landmark selection for point-based interpolating transformations for nonlinear medical image registration. Materials and Methods. Interpolating transformations were calculated from homologous point landmarks on the source (image to be transformed and target (reference image. Point landmarks are placed at regular intervals on contours of anatomical features, and their positions are optimized along the contour surface by a function composed of curvature similarity and displacements of the homologous landmarks. The method was evaluated in two cases (=5 each. In one, MRI was registered to histological sections; in the second, geometric distortions in EPI MRI were corrected. Normalized mutual information and target registration error were calculated to compare the registration accuracy of the automatically and manually generated landmarks. Results. Statistical analyses demonstrated significant improvement (<0.05 in registration accuracy by landmark optimization in most data sets and trends towards improvement (<0.1 in others as compared to manual landmark selection.

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.

Preliminary study of lateral cerebral angiography with reverse rotation in the digital image registration and subtraction

International Nuclear Information System (INIS)

Shen Zhenglin; Liu Dongyang; Shen Zhenghai; Li Shuping; Zhang Ziyan; Wu Yongjuan; Liu Peijun

2012-01-01

Objective: Investigate the value and feasibility of image registration with reverse rotation in lateral cerebral DSA. Methods: (1) Experimental study: the target images were subtracted directly, and subtracted again after reverse rotation. Software of registration and subtraction with reverse rotation edited by the author utilizing Visual Basic. The function of the automatic angle detection by the software were evaluated to see whether it detected the angle of line. The subtraction function of DSA by the software was evaluated. (2) Clinical retrospective study: the untreated mask and target images of 15 patients with motion along vertical axis during lateral cerebral DSA were uploaded to the software. The target images were processed with and without the software to get two sets of images. (3) Evaluation: four experienced radiologists read and compared the two sets of the images,and graded their findings. Results: (1) The automatic detection by the software suggested that the target images should be rotated counterclockwise 1.3°. The subtraction result of the software was satisfactory. (2) In the 15 sets of images, there were only three sets of images deemed optimal after traditional subtraction. After reverse rotation, artifacts were significantly reduced and the image sharper. There were ten cases with significant artifacts after traditional subtraction, and those images were sharper and showed more peripheral vessels after reverse rotation. The traditional subtraction images of two sets could not be interpreted,the reverse rotation registration images reached the diagnostic quality. (3) Subjective evaluation: there were more information and less noise and distortion in the registration images with reverse rotation than in the traditional subtraction. But the image resolution decreased slightly after reverse rotation registration. Conclusion: The registration of digital angiography with reverse rotation can improve the image quality in lateral cerebral DSA

A comparison of publicly available linear MRI stereotaxic registration techniques.

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Dadar, Mahsa; Fonov, Vladimir S; Collins, D Louis

2018-07-01

Linear registration to a standard space is one of the major steps in processing and analyzing magnetic resonance images (MRIs) of the brain. Here we present an overview of linear stereotaxic MRI registration and compare the performance of 5 publicly available and extensively used linear registration techniques in medical image analysis. A set of 9693 T1-weighted MR images were obtained for testing from 4 datasets: ADNI, PREVENT-AD, PPMI, and HCP, two of which have multi-center and multi-scanner data and three of which have longitudinal data. Each individual native image was linearly registered to the MNI ICBM152 average template using five versions of MRITOTAL from MINC tools, FLIRT from FSL, two versions of Elastix, spm_affreg from SPM, and ANTs linear registration techniques. Quality control (QC) images were generated from the registered volumes and viewed by an expert rater to assess the quality of the registrations. The QC image contained 60 sub-images (20 of each of axial, sagittal, and coronal views at different levels throughout the brain) overlaid with contours of the ICBM152 template, enabling the expert rater to label the registration as acceptable or unacceptable. The performance of the registration techniques was then compared across different datasets. In addition, the effect of image noise, intensity non-uniformity, age, head size, and atrophy on the performance of the techniques was investigated by comparing differences between age, scaling factor, ventricle volume, brain volume, and white matter hyperintensity (WMH) volumes between passed and failed cases for each method. The average registration failure rate among all datasets was 27.41%, 27.14%, 12.74%, 13.03%, 0.44% for the five versions of MRITOTAL techniques, 8.87% for ANTs, 11.11% for FSL, 12.35% for Elastix Affine, 24.40% for Elastix Similarity, and 30.66% for SPM. There were significant effects of signal to noise ratio, image intensity non-uniformity estimates, as well as age, head size, and

3D-2D registration in endovascular image-guided surgery: evaluation of state-of-the-art methods on cerebral angiograms.

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Mitrović, Uroš; Likar, Boštjan; Pernuš, Franjo; Špiclin, Žiga

2018-02-01

Image guidance for minimally invasive surgery is based on spatial co-registration and fusion of 3D pre-interventional images and treatment plans with the 2D live intra-interventional images. The spatial co-registration or 3D-2D registration is the key enabling technology; however, the performance of state-of-the-art automated methods is rather unclear as they have not been assessed under the same test conditions. Herein we perform a quantitative and comparative evaluation of ten state-of-the-art methods for 3D-2D registration on a public dataset of clinical angiograms. Image database consisted of 3D and 2D angiograms of 25 patients undergoing treatment for cerebral aneurysms or arteriovenous malformations. On each of the datasets, highly accurate "gold-standard" registrations of 3D and 2D images were established based on patient-attached fiducial markers. The database was used to rigorously evaluate ten state-of-the-art 3D-2D registration methods, namely two intensity-, two gradient-, three feature-based and three hybrid methods, both for registration of 3D pre-interventional image to monoplane or biplane 2D images. Intensity-based methods were most accurate in all tests (0.3 mm). One of the hybrid methods was most robust with 98.75% of successful registrations (SR) and capture range of 18 mm for registrations of 3D to biplane 2D angiograms. In general, registration accuracy was similar whether registration of 3D image was performed onto mono- or biplanar 2D images; however, the SR was substantially lower in case of 3D to monoplane 2D registration. Two feature-based and two hybrid methods had clinically feasible execution times in the order of a second. Performance of methods seems to fall below expectations in terms of robustness in case of registration of 3D to monoplane 2D images, while translation into clinical image guidance systems seems readily feasible for methods that perform registration of the 3D pre-interventional image onto biplanar intra

The One to Multiple Automatic High Accuracy Registration of Terrestrial LIDAR and Optical Images

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Wang, Y.; Hu, C.; Xia, G.; Xue, H.

2018-04-01

The registration of ground laser point cloud and close-range image is the key content of high-precision 3D reconstruction of cultural relic object. In view of the requirement of high texture resolution in the field of cultural relic at present, The registration of point cloud and image data in object reconstruction will result in the problem of point cloud to multiple images. In the current commercial software, the two pairs of registration of the two kinds of data are realized by manually dividing point cloud data, manual matching point cloud and image data, manually selecting a two - dimensional point of the same name of the image and the point cloud, and the process not only greatly reduces the working efficiency, but also affects the precision of the registration of the two, and causes the problem of the color point cloud texture joint. In order to solve the above problems, this paper takes the whole object image as the intermediate data, and uses the matching technology to realize the automatic one-to-one correspondence between the point cloud and multiple images. The matching of point cloud center projection reflection intensity image and optical image is applied to realize the automatic matching of the same name feature points, and the Rodrigo matrix spatial similarity transformation model and weight selection iteration are used to realize the automatic registration of the two kinds of data with high accuracy. This method is expected to serve for the high precision and high efficiency automatic 3D reconstruction of cultural relic objects, which has certain scientific research value and practical significance.

Usefulness of the classification technique of cerebral artery for 2D/3D registration

International Nuclear Information System (INIS)

Takemura, Akihiro; Suzuki, Masayuki; Kikuchi, Yuzo; Okumura, Yusuke; Harauchi, Hajime

2007-01-01

Several papers have proposed 2D/3D registration methods of the cerebral artery using magnetic resonance angiography (MRA) and digital subtraction angiography (DSA). Since differences between vessels in a DSA image and MRA volume data cause registration failure, we previously proposed a method to extract vessels from MRA volume data using a technique based on classification of the cerebral artery. In this paper, we evaluated the usefulness of this classification technique by evaluating the reliability of this 2D/3D registration method. This classification method divides the cerebral artery in MRA volume data into 12 segments. According to the results of the classification, structures corresponding to vessels on a DSA image can then be extracted. We applied the 2D/3D registration with/without classification to 16 pairs of MRA volume data and DSA images obtained from six patients. The registration results were scored into four levels (Excellent, Good, Fair and Poor). The rates of successful registration (>fair) were 37.5% for registration without classification and 81.3% for that with classification. These findings suggested that there was a low percentage of incorrectly extracted voxels and we could facilitate reliable registration. Thus, the classification technique was shown to be useful for feature-based 2D/3D registration. (author)

SU-E-J-47: Comparison of Online Image Registrations of Varian TrueBeam Cone-Beam CT and BrainLab ExacTrac Imaging Systems

International Nuclear Information System (INIS)

Li, J; Shi, W; Andrews, D; Werner-Wasik, M; Yu, Y; Liu, H

2015-01-01

Purpose To compare online image registrations of TrueBeam cone-beam CT (CBCT) and BrainLab ExacTrac imaging systems. Methods Tests were performed on a Varian TrueBeam STx linear accelerator (Version 2.0), which is integrated with a BrainLab ExacTrac imaging system (Version 6.0.5). The study was focused on comparing the online image registrations for translational shifts. A Rando head phantom was placed on treatment couch and immobilized with a BrainLab mask. The phantom was shifted by moving the couch translationally for 8 mm with a step size of 1 mm, in vertical, longitudinal, and lateral directions, respectively. At each location, the phantom was imaged with CBCT and ExacTrac x-ray. CBCT images were registered with TrueBeam and ExacTrac online registration algorithms, respectively. And ExacTrac x-ray image registrations were performed. Shifts calculated from different registrations were compared with nominal couch shifts. Results The averages and ranges of absolute differences between couch shifts and calculated phantom shifts obtained from ExacTrac x-ray registration, ExacTrac CBCT registration with default window, ExaxTrac CBCT registration with adjusted window (bone), Truebeam CBCT registration with bone window, and Truebeam CBCT registration with soft tissue window, were: 0.07 (0.02–0.14), 0.14 (0.01–0.35), 0.12 (0.02–0.28), 0.09 (0–0.20), and 0.06 (0–0.10) mm, in vertical direction; 0.06 (0.01–0.12), 0.27 (0.07–0.57), 0.23 (0.02–0.48), 0.04 (0–0.10), and 0.08 (0– 0.20) mm, in longitudinal direction; 0.05 (0.01–0.21), 0.35 (0.14–0.80), 0.25 (0.01–0.56), 0.19 (0–0.40), and 0.20 (0–0.40) mm, in lateral direction. Conclusion The shifts calculated from ExacTrac x-ray and TrueBeam CBCT registrations were close to each other (the differences between were less than 0.40 mm in any direction), and had better agreements with couch shifts than those from ExacTrac CBCT registrations. There were no significant differences between True

Magnetic resonance imaging for radiotherapy planning of brain cancer patients using immobilization and surface coils

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Hanvey, S.; Glegg, M.; Foster, J.

2009-09-01

This study investigated the compatibility of a head and neck immobilization device with magnetic resonance imaging (MRI). The immobilization device is used to position a patient in the same way as when receiving a computed tomography (CT) scan for radiotherapy planning and radiation treatment. The advantage of using immobilization in MR is improved accuracy in CT/MR image registration enabling greater confidence in the delineation of structures. The main practical difficulty in using an immobilization device in MRI is that physical constraints make their use incompatible with head imaging coils. Within this paper we describe a method for MR imaging of the brain which allows the use of head and neck immobilization devices. By a series of image quality tests we obtained the same or better image quality as a multi-channel head coil.

Image Navigation and Registration (INR) Performance Assessment Tool Set (IPATS) for the GOES-R Advanced Baseline Imager and Geostationary Lightning Mapper

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DeLuccia, Frank J.; Houchin, Scott; Porter, Brian C.; Graybill, Justin; Haas, Evan; Johnson, Patrick D.; Isaacson, Peter J.; Reth, Alan D.

2016-01-01

The GOES-R Flight Project has developed an Image Navigation and Registration (INR) Performance Assessment Tool Set (IPATS) for measuring Advanced Baseline Imager (ABI) and Geostationary Lightning Mapper (GLM) INR performance metrics in the post-launch period for performance evaluation and long term monitoring. For ABI, these metrics are the 3-sigma errors in navigation (NAV), channel-to-channel registration (CCR), frame-to-frame registration (FFR), swath-to-swath registration (SSR), and within frame registration (WIFR) for the Level 1B image products. For GLM, the single metric of interest is the 3-sigma error in the navigation of background images (GLM NAV) used by the system to navigate lightning strikes. 3-sigma errors are estimates of the 99.73rd percentile of the errors accumulated over a 24 hour data collection period. IPATS utilizes a modular algorithmic design to allow user selection of data processing sequences optimized for generation of each INR metric. This novel modular approach minimizes duplication of common processing elements, thereby maximizing code efficiency and speed. Fast processing is essential given the large number of sub-image registrations required to generate INR metrics for the many images produced over a 24 hour evaluation period. Another aspect of the IPATS design that vastly reduces execution time is the off-line propagation of Landsat based truth images to the fixed grid coordinates system for each of the three GOES-R satellite locations, operational East and West and initial checkout locations. This paper describes the algorithmic design and implementation of IPATS and provides preliminary test results.