Efficiently computing and registering digitally reconstructed radiographs

Generation of digitally reconstructed radiographs (DRRs) is computationally expensive and has been cited as the rate-limiting step in the execution time of intensity-based two-dimensional/three-dimensional registration algorithms. This paper considers the problem of generating DRRs by conventional ray tracing. Experiments confirm that good quality reconstructions can be obtained using this approach in a few seconds. We evaluate the approach for automatic patient setup prior to radiotherapy treatment by performing intensity based 2D-3D registration using normalized cross correlation. Preliminary results using a pelvic CT data set show the method is accurate to about ±2 pixels (i.e. ±0.3 mm)

REGISTRASI CITRA DIGITALLY RECONTRUCTED RADIOGRAPHS TERHADAP CITRA ELECTRONIC PORTAL IMAGING DEVICES UNTUK VERIFIKASI POSISI PASIEN RADIOTERAPI

A research on development of automatic 2D/2D image registration system for digitally reconstructed radiographs (DRR) and electronic portal imaging devices image (EPID) has been carried out to verify patient position in radiotherapy treatment. Registration is done using two methods: rigid transformation and mutual information. Implementations using rigid transformations yields X-axis average translation (2,18??0,89) mm, Y-axis (1,18??0,59) mm as well as Z-axis (2,08??0,9) mm while the average registration time which is (42.85??15.20) s. Implementations using mutual information yields X-axis average translation (2,27??0,8) mm,Y axis (1,89??0,65) mm as well as Z axis (0,94??0,59) mm while the average registration time which is (2.85??0.54) s. We can conclude that there is no significant difference in P value > 0.05 according to chi square test corresponds to verification manual, semiautomatic fusion, rigid transformations, and mutual information

Local Metric Learning in 2D/3D Deformable Registration With Application in the Abdomen

In image-guided radiotherapy (IGRT) of disease sites subject to respiratory motion, soft tissue deformations can affect localization accuracy. We describe the application of a method of 2D/3D deformable registration to soft tissue localization in abdomen. The method, called registration efficiency and accuracy through learning a metric on shape (REALMS), is designed to support real-time IGRT. In a previously developed version of REALMS, the method interpolated 3D deformation parameters for any credible deformation in a deformation space using a single globally-trained Riemannian metric for each parameter. We propose a refinement of the method in which the metric is trained over a particular region of the deformation space, such that interpolation accuracy within that region is improved. We report on the application of the proposed algorithm to IGRT in abdominal disease sites, which is more challenging than in lung because of low intensity contrast and nonrespiratory deformation. We introduce a rigid translation vector to compensate for nonrespiratory deformation, and design a special region-of-interest around fiducial markers implanted near the tumor to produce a more reliable registration. Both synthetic data and actual data tests on abdominal datasets show that the localized approach achieves more accurate 2D/3D deformable registration than the global approach

Medical image registration by neural networks: a regression-based registration approach

This thesis focuses on the development and evaluation of a registration-by-regression approach for the 3D/2D registration of coronary Computed Tomography Angiography (CTA) and X-ray angiography. This regression-based method relates image features of 2D projection images to the transformation parameters of the 3D image by a nonlinear regression. It treats registration as a regression problem, as an alternative for the traditional iterative approach that often comes with high computational costs and limited capture range. First we presented a survey of the methods with a regression-based registration approach for medical applications, as well as a summary of their main characteristics (Chapter 2). Second, we studied the registration methodology, addressing the input features and the choice of regression model (Chapter 3 and Chapter 4). For that purpose, we evaluated different options using simulated X-ray images generated from coronary artery tree models derived from 3D CTA scans. We also compared the registration-by-regression results with a method based on iterative optimization. Different image features of 2D projections and seven regression techniques were considered. The regression approach for simulated X-rays was shown to be slightly less accurate, but much more robust than the method based on an iterative optimization approach. Neural Networks obtained accurate results and showed to be robust to large initial misalignment. Third, we evaluated the registration-by-regression method using clinical data, integrating the 3D preoperative CTA of the coronary arteries with intraoperative 2D X-ray angiography images (Chapter 5). For the evaluation of the image registration, a gold standard registration was established using an exhaustive search followed by a multi-observer visual scoring procedure. The influence of preprocessing options for the simulated images and the real X-rays was studied. Several image features were also compared. The coronary registration–by-regression results were not satisfactory, resembling manual initialization accuracy. Therefore, the proposed method for this concrete problem and in its current configuration is not sufficiently accurate to be used in the clinical practice. The framework developed enables us to better understand the dependency of the proposed method on the differences between simulated and real images. The main difficulty lies in the substantial differences in appearance between the images used for training (simulated X-rays from 3D coronary models) and the actual images obtained during the intervention (real X-ray angiography). We suggest alternative solutions and recommend to evaluate the registration-by-regression approach in other applications where training data is available that has similar appearance to the eventual test data

2D-3D-Registrierung mit Parameterentkopplung für die Patientenlagerung in der Strahlentherapie

Die optimale Lagerung des Patienten ist von entscheidender Bedeutung für den Erfolg einer Bestrahlung. In dieser Arbeit wird ein 2D-3D-Registrierungsverfahren für Hochenergiekontrollaufnahmen entwickelt, das die relevante anatomische Information gezielt herausarbeitet. Virtuelle Projektionsbilder können unter Ausnutzung des 3D-Radonraumes schnell berechnet werden. Die mathematische Entkopplung der Freiheitsgrade ermöglicht eine vollständige und robuste Lagebestimmung innerhalb weniger Sekunden

2D-3D-Registrierung mit Parameterentkopplung für die Patientenlagerung in der Strahlentherapie

Die optimale Lagerung des Patienten ist von entscheidender Bedeutung für den Erfolg einer Bestrahlung. In dieser Arbeit wird ein 2D-3D-Registrierungsverfahren für Hochenergiekontrollaufnahmen entwickelt, das die relevante anatomische Information gezielt herausarbeitet. Virtuelle Projektionsbilder können unter Ausnutzung des 3D-Radonraumes schnell berechnet werden. Die mathematische Entkopplung der Freiheitsgrade ermöglicht eine vollständige und robuste Lagebestimmung innerhalb weniger Sekunden

Recalage rigide 3D-2D par intensité pour le traitement percutané des cardiopathies congénitales

Les cardiopathies congénitales cyanogènes sont des malformations cardiaques infantiles qui, dans leurs formes les plus complexes, sont aggravées par des artères morbides partant de l’aorte et appelées collatérales aorto-pulmonaires majeures (MAPCAs). Pour corriger ces malformations, les cardiologues insèrent un cathéter dans une artère du patient puis, le guident jusqu’à atteindre la structure vasculaire d’intérêt. Le cathéter est visualisé grâce à des angiographies acquises lors de l’opération. Néanmoins, ces interventions, dîtes percutanées, sont délicates à réaliser. L’emploi des angiographies 2D limite le champ de vision des cardiologues et les oblige à mentalement reconstruire la structure vasculaire en mouvement. Afin d’améliorer les conditions d’intervention, des techniques d’imagerie médicale exploitant des données tomographiques acquis avant l’intervention sont développées. Les données tomographiques forment un modèle 3D fiable de la structure vasculaire qui, une fois précisément aligné avec les angiographies, définit un outil de navigation virtuel 3D qui augmente le champ de vision des cardiologues. Dans ce mémoire, une nouvelle méthode automatique de recalage rigide 3D-2D par intensité de données tomographiques 3D avec des angiographies 2D est présentée. Aussi, une technique d’alignement semi-automatique permettant d’accélérer l’initialisation de la méthode automatique est développée. Les résultats de la méthode de recalage proposée, obtenus avec deux jeux de données de patient atteints de malformations cardiaques, sont prometteurs. Un alignement précis et robuste des données tomographique de l’artère aorte et des MAPCAs (0;265�0;647mm et 99 % de succès) à partir d’un déplacement rigide d’amplitude maximale (20mm et 20°) est obtenu en un temps de calcul raisonnable (13,7 secondes)

REAL-TIME DATA MINING FOR PROCESS OPERATIONS USING GRAPHICS PROCESSING UNIT (GPU)-BASED HIGH PERFORMANCE COMPUTING

Ph.DDOCTOR OF PHILOSOPH

Automatic correspondence between 2D and 3D images of the breast

Radiologists often need to localise corresponding findings in different images of the breast, such as Magnetic Resonance Images and X-ray mammograms. However, this is a difficult task, as one is a volume and the other a projection image. In addition, the appearance of breast tissue structure can vary significantly between them. Some breast regions are often obscured in an X-ray, due to its projective nature and the superimposition of normal glandular tissue. Automatically determining correspondences between the two modalities could assist radiologists in the detection, diagnosis and surgical planning of breast cancer. This thesis addresses the problems associated with the automatic alignment of 3D and 2D breast images and presents a generic framework for registration that uses the structures within the breast for alignment, rather than surrogates based on the breast outline or nipple position. The proposed algorithm can adapt to incorporate different types of transformation models, in order to capture the breast deformation between modalities. The framework was validated on clinical MRI and X-ray mammography cases using both simple geometrical models, such as the affine, and also more complex ones that are based on biomechanical simulations. The results showed that the proposed framework with the affine transformation model can provide clinically useful accuracy (13.1mm when tested on 113 registration tasks). The biomechanical transformation models provided further improvement when applied on a smaller dataset. Our technique was also tested on determining corresponding findings in multiple X-ray images (i.e. temporal or CC to MLO) for a given subject using the 3D information provided by the MRI. Quantitative results showed that this approach outperforms 2D transformation models that are typically used for this task. The results indicate that this pipeline has the potential to provide a clinically useful tool for radiologists