Radiotherapy is a potential curative option for liver cancer; however, respiratory motion creates uncertainty in treatment delivery. Advances in imaging and registration techniques can provide information regarding changes in respiratory motion. Currently image registration is challenged by computation and manual intervention. A Navigator Channel (NC) technique was developed to overcome these limitations. A population motion model was generated to predict patient-specific motion, while a point motion detection technique was developed to calculate the patient-specific liver edge motion from images. An adaptation technique uses the relative difference between the population and patient calculated liver edge motion to determine the patient's liver volume motion. The NC technique was tested on patient 4D-CT images for initial validation to determine the accuracy. Accuracy was less than 0.10 mm in liver edge detection and approximately 0.25 cm in predicting patient-specific motion. This technique can be used to ensure accurate treatment delivery for liver radiotherapy.

Radiotherapy is a potential curative option for liver cancer; however, respiratory motion creates uncertainty in treatment delivery. Advances in imaging and registration techniques can provide information regarding changes in respiratory motion. Currently image registration is challenged by computation and manual intervention. A Navigator Channel (NC) technique was developed to overcome these limitations. A population motion model was generated to predict patient-specific motion, while a point motion detection technique was developed to calculate the patient-specific liver edge motion from images. An adaptation technique uses the relative difference between the population and patient calculated liver edge motion to determine the patient's liver volume motion. The NC technique was tested on patient 4D-CT images for initial validation to determine the accuracy. Accuracy was less than 0.10 mm in liver edge detection and approximately 0.25 cm in predicting patient-specific motion. This technique can be used to ensure accurate treatment delivery for liver radiotherapy.

Alors que la perspective de la fusion d’images médicales capturées par des systèmes d’imageries de type différent est largement contemplée, la mise en pratique est toujours victime d’un obstacle théorique : la définition d’une mesure de similarité entre les images. Des efforts dans le domaine ont rencontrés un certain succès pour certains types d’images, cependant la définition d’un critère de similarité entre les images quelle que soit leur origine et un des plus gros défis en recalage d’images déformables. Dans cette thèse, nous avons décidé de développer une approche générique pour la comparaison de deux types de modalités donnés. Les récentes avancées en apprentissage statistique (Machine Learning) nous ont permis de développer des solutions innovantes pour la résolution de ce problème complexe. Pour appréhender le problème de la comparaison de données incommensurables, nous avons choisi de le regarder comme un problème de plongement de données : chacun des jeux de données est plongé dans un espace commun dans lequel les comparaisons sont possibles. A ces fins, nous avons exploré la projection d’un espace de données image sur l’espace de données lié à la seconde image et aussi la projection des deux espaces de données dans un troisième espace commun dans lequel les calculs sont conduits. Ceci a été entrepris grâce à l’étude des correspondances entre les images dans une base de données images pré-alignées. Dans la poursuite de ces buts, de nouvelles méthodes ont été développées que ce soit pour la régression d’images ou pour l’apprentissage de métrique multimodale. Les similarités apprises résultantes sont alors incorporées dans une méthode plus globale de recalage basée sur l’optimisation discrète qui diminue le besoin d’un critère différentiable pour la recherche de solution. Enfin nous explorons une méthode qui permet d’éviter le besoin d’une base de données pré-alignées en demandant seulement des données annotées (segmentations) par un spécialiste. De nombreuses expériences sont conduites sur deux bases de données complexes (Images d’IRM pré-alignées et Images TEP/Scanner) dans le but de justifier les directions prises par nos approches. / Even though the prospect of fusing images issued by different medical imagery systems is highly contemplated, the practical instantiation of it is subject to a theoretical hurdle: the definition of a similarity between images. Efforts in this field have proved successful for select pairs of images; however defining a suitable similarity between images regardless of their origin is one of the biggest challenges in deformable registration. In this thesis, we chose to develop generic approaches that allow the comparison of any two given modality. The recent advances in Machine Learning permitted us to provide innovative solutions to this very challenging problem. To tackle the problem of comparing incommensurable data we chose to view it as a data embedding problem where one embeds all the data in a common space in which comparison is possible. To this end, we explored the projection of one image space onto the image space of the other as well as the projection of both image spaces onto a common image space in which the comparison calculations are conducted. This was done by the study of the correspondences between image features in a pre-aligned dataset. In the pursuit of these goals, new methods for image regression as well as multi-modal metric learning methods were developed. The resulting learned similarities are then incorporated into a discrete optimization framework that mitigates the need for a differentiable criterion. Lastly we investigate on a new method that discards the constraint of a database of images that are pre-aligned, only requiring data annotated (segmented) by a physician. Experiments are conducted on two challenging medical images data-sets (Pre-Aligned MRI images and PET/CT images) to justify the benefits of our approach.

A substantial fraction of the automotive assembly comprises formed sheet metal parts. To reduce vehicle weight and improve fuel economy, total sheet metal mass should be minimized without compromising the structural integrity of the vehicle. Excessive deformation contributes to tearing or buckling of the metal, and therefore a forming limit is investigated experimentally to determine the extent to which each particular material can be safely strained. To assess sheet metal formability, this thesis proposes a novel framework for sheet metal surface strain measurement using a scalable dot-grid pattern. Aluminum sheet metal samples are marked with a regular grid of dot-features and imaged with a close-range monocular vision system. After forming, the sheet metal samples are imaged once again to examine the deformation of the surface pattern, and thereby resolve the material strain. Grid-features are localized with sub-pixel accuracy, and then topologically mapped using a novel algorithm for deformation-invariant grid registration. Experimental results collected from a laboratory setup demonstrate consistent robustness under practical imaging conditions. Accuracy, repeatability, and timing statistics are reported for several state-of-the-art feature detectors. / Thesis / Master of Applied Science (MASc)

<p>This thesis presents a family of automatic elastic registration methods applicable to single and multimodal images of similar or dissimilar dimensions. These registration algorithms employ a generic dynamic linear elastic continuum mechanics model of the tissue deformation which is discretized using the finite element method. The dynamic deformation model provides spatial and temporal correlation between images acquired from different orientations at different times. First, a volumetric registration algorithm is presented which estimates the deformation field by balancing internal deformation forces of the elastic model against external forces derived from an intensity-based similarity measure between images. The registration is achieved by iteratively solving a reduced form of the dynamic deformation equations in response to image-derived nodal forces. A general approach for automatic deformable image registration is also presented in this thesis which deals with different registration problems within a unified framework irrespective of the image modality and dimension. Using the dynamic deformation model, the problem of deformable image registration is approached as a classical state estimation problem with various image similarity measures providing an observation model. With this formulation, single and multiple-modality, 3D-3D and 3D-2D image registration problems can all be treated within the same framework.The registration is achieved through a Kalman-like filtering process which incorporates information from the deformation model and an observation error computed from an intensity-based similarity measure. Correlation ratio, normalized correlation coefficient, mutual information, modality independent neighborhood descriptor and sum of squared differences between images are similarity/distance measures employed for single and multiple modality image registration in this thesis</p> / Doctor of Philosophy (PhD)

In this thesis we address the problem of digitizing the motion of three-dimensional shapes that move and deform in time. These shapes are observed from several points of view with cameras that record the scene's evolution as videos. Using available reconstruction methods, these videos can be converted into a sequence of three-dimensional snapshots that capture the appearance and shape of the objects in the scene. The focus of this thesis is to complement appearance and shape with information on the motion and deformation of objects. In other words, we want to measure the trajectory of every point on the observed surfaces. This is a challenging problem because the captured videos are only sequences of images, and the reconstructed shapes are built independently from each other. While the human brain excels at recreating the illusion of motion from these snapshots, using them to automatically measure motion is still largely an open problem. The majority of prior works on the subject has focused on tracking the performance of one human actor, and used the strong prior knowledge on the articulated nature of human motion to handle the ambiguity and noise inherent to visual data. In contrast, the presented developments consist of generic methods that allow to digitize scenes involving several humans and deformable objects of arbitrary nature. To perform surface tracking as generically as possible, we formulate the problem as the geometric registration of surfaces and deform a reference mesh to fit a sequence of independently reconstructed meshes. We introduce a set of algorithms and numerical tools that integrate into a pipeline whose output is an animated mesh. Our first contribution consists of a generic mesh deformation model and numerical optimization framework that divides the tracked surface into a collection of patches, organizes these patches in a deformation graph and emulates elastic behavior with respect to the reference pose. As a second contribution, we present a probabilistic formulation of deformable surface registration that embeds the inference in an Expectation-Maximization framework that explicitly accounts for the noise and in the acquisition. As a third contribution, we look at how prior knowledge can be used when tracking articulated objects, and compare different deformation model with skeletal-based tracking. The studies reported by this thesis are supported by extensive experiments on various 4D datasets. They show that in spite of weaker assumption on the nature of the tracked objects, the presented ideas allow to process complex scenes involving several arbitrary objects, while robustly handling missing data and relatively large reconstruction artifacts.

Even though the prospect of fusing images issued by different medical imagery systems is highly contemplated, the practical instantiation of it is subject to a theoretical hurdle: the definition of a similarity between images. Efforts in this field have proved successful for select pairs of images; however defining a suitable similarity between images regardless of their origin is one of the biggest challenges in deformable registration. In this thesis, we chose to develop generic approaches that allow the comparison of any two given modality. The recent advances in Machine Learning permitted us to provide innovative solutions to this very challenging problem. To tackle the problem of comparing incommensurable data we chose to view it as a data embedding problem where one embeds all the data in a common space in which comparison is possible. To this end, we explored the projection of one image space onto the image space of the other as well as the projection of both image spaces onto a common image space in which the comparison calculations are conducted. This was done by the study of the correspondences between image features in a pre-aligned dataset. In the pursuit of these goals, new methods for image regression as well as multi-modal metric learning methods were developed. The resulting learned similarities are then incorporated into a discrete optimization framework that mitigates the need for a differentiable criterion. Lastly we investigate on a new method that discards the constraint of a database of images that are pre-aligned, only requiring data annotated (segmented) by a physician. Experiments are conducted on two challenging medical images data-sets (Pre-Aligned MRI images and PET/CT images) to justify the benefits of our approach.

Cette thèse traite du suivi temporel de surfaces déformables. Ces surfaces sont observées depuis plusieurs points de vue par des caméras qui capturent l'évolution de la scène et l'enregistrent sous la forme de vidéos. Du fait des progrès récents en reconstruction multi-vue, cet ensemble de vidéos peut être converti en une série de clichés tridimensionnels qui capturent l'apparence et la forme des objets dans la scène. Le problème au coeur des travaux rapportés par cette thèse est de complémenter les informations d'apparence et de forme avec des informations sur les mouvements et les déformations des objets. En d'autres mots, il s'agit de mesurer la trajectoire de chacun des points sur les surfaces observées. Ceci est un problème difficile car les vidéos capturées ne sont que des séquences d'images, et car les formes reconstruites à chaque instant le sont indépendemment les unes des autres. Si le cerveau humain excelle à recréer l'illusion de mouvement à partir de ces clichés, leur utilisation pour la mesure automatisée du mouvement reste une question largement ouverte. La majorité des précédents travaux sur le sujet se sont focalisés sur la capture du mouvement humain et ont bénéficié de la nature articulée de ce mouvement qui pouvait être utilisé comme a-priori dans les calculs. La spécificité des développements présentés ici réside dans la généricité des méthodes qui permettent de capturer des scènes dynamiques plus complexes contenant plusieurs acteurs et différents objets déformables de nature inconnue a priori. Pour suivre les surfaces de la façon la plus générique possible, nous formulons le problème comme celui de l'alignement géométrique de surfaces, et déformons un maillage de référence pour l'aligner avec les maillages indépendemment reconstruits de la séquence. Nous présentons un ensemble d'algorithmes et d'outils numériques intégrés dans une chaîne de traitements dont le résultat est un maillage animé. Notre première contribution est une méthode de déformation de maillage qui divise la surface en une collection de morceaux élémentaires de surfaces que nous nommons patches. Ces patches sont organisés dans un graphe de déformation, et une force est appliquée sur cette structure pour émuler une déformation élastique par rapport à la pose de référence. Comme seconde contribution, nous présentons une formulation probabiliste de l'alignement de surfaces déformables qui modélise explicitement le bruit dans le processus d'acquisition. Pour finir, nous étudions dans quelle mesure les a-prioris sur la nature articulée du mouvement peuvent aider, et comparons différents modèles de déformation à une méthode de suivi de squelette. Les développements rapportés par cette thèse sont validés par de nombreuses expériences sur une variété de séquences. Ces résultats montrent qu'en dépit d'a-prioris moins forts sur les surfaces suivies, les idées présentées permettent de traiter des scènes complexes contenant de multiples objets tout en se comportant de façon robuste vis-a-vis de données fragmentaires et d'erreurs de reconstruction. / In this thesis we address the problem of digitizing the motion of three-dimensional shapes that move and deform in time. These shapes are observed from several points of view with cameras that record the scene's evolution as videos. Using available reconstruction methods, these videos can be converted into a sequence of three-dimensional snapshots that capture the appearance and shape of the objects in the scene. The focus of this thesis is to complement appearance and shape with information on the motion and deformation of objects. In other words, we want to measure the trajectory of every point on the observed surfaces. This is a challenging problem because the captured videos are only sequences of images, and the reconstructed shapes are built independently from each other. While the human brain excels at recreating the illusion of motion from these snapshots, using them to automatically measure motion is still largely an open problem. The majority of prior works on the subject has focused on tracking the performance of one human actor, and used the strong prior knowledge on the articulated nature of human motion to handle the ambiguity and noise inherent to visual data. In contrast, the presented developments consist of generic methods that allow to digitize scenes involving several humans and deformable objects of arbitrary nature. To perform surface tracking as generically as possible, we formulate the problem as the geometric registration of surfaces and deform a reference mesh to fit a sequence of independently reconstructed meshes. We introduce a set of algorithms and numerical tools that integrate into a pipeline whose output is an animated mesh. Our first contribution consists of a generic mesh deformation model and numerical optimization framework that divides the tracked surface into a collection of patches, organizes these patches in a deformation graph and emulates elastic behavior with respect to the reference pose. As a second contribution, we present a probabilistic formulation of deformable surface registration that embeds the inference in an Expectation-Maximization framework that explicitly accounts for the noise and in the acquisition. As a third contribution, we look at how prior knowledge can be used when tracking articulated objects, and compare different deformation model with skeletal-based tracking. The studies reported by this thesis are supported by extensive experiments on various 4D datasets. They show that in spite of weaker assumption on the nature of the tracked objects, the presented ideas allow to process complex scenes involving several arbitrary objects, while robustly handling missing data and relatively large reconstruction artifacts.

Évaluer quantitativement les mouvements d'un patient lors d'un traitement par radiothérapie est un enjeu majeur. En effet, ces mouvements et ces déformations anatomiques induisent une incertitude balistique conduisant les thérapeutes à augmenter les marges de sécurité, ce qui peut empêcher de délivrer une dose suffisante à la région tumorale. Dans le cadre de cette thèse, nous nous sommes intéressés à l'estimation de ces mouvements dans les images obtenues juste avant le traitement par le scanner à faisceau conique. Pour cela, nous avons utilisé des algorithmes de recalage déformable. Dans un premier temps, nous avons cherché à améliorer la modélisation du mouvement respiratoire. Pour cela, nous nous sommes basés sur un modèle utilisant une segmentation de l'intérieur de la cage thoracique afin d'autoriser le glissement des organes internes contre cette dernière, tout en préservant un champ de déformation cohérent. La segmentation de l'intérieur de la cage thoracique est effectuée automatiquement par un algorithme qui prend en paramètres une segmentation des poumons et de la cage thoracique. Les algorithmes permettant de segmenter ces deux régions se sont avérés peu robustes, ce qui nous a poussé à les améliorer. Une fois ces structures bien segmentées, le modèle de transformation souffre d'un inconvénient majeur empêchant son utilisation dans un algorithme de recalage entre des projections 2D et une image 3D. En effet, il nécessite une segmentation 3D de l'intérieur de la cage thoracique dans les 2 images à recaler, ce qui est impossible à obtenir pour la série de projections 2D. Le modèle proposé dans cette thèse permet de contraindre les déformations à représenter des mouvements physiologiquement plausibles, tout en ne nécessitant qu'une seule segmentation de l'image 3D. Dans un deuxième temps, nous avons implémenté un algorithme de recalage 2D/3D utilisant le modèle de déformation proposé afin d'extraire le mouvement respiratoire des projections 2D de l'imageur à faisceau conique. Cet algorithme a été testé sur des images simulées dont les déformations étaient connues. Les résultats étant concluants, nous avons utilisé un algorithme de reconstruction compensée en mouvement dans le but de produire des images 3D sans flou respiratoire sur des données réelles. L'approche proposée permet d'obtenir une connaissance approfondie de l'anatomie du patient et de son mouvement respiratoire le jour du traitement, ce qui ouvre de nouvelles perspectives comme l'adaptation journalière du traitement, le calcul de dose prenant en compte le mouvement respiratoire et la re-planification de traitement. Cette approche de recalage entre une image 3D et des projections 2D est généralisable à d'autres mouvements et d'autres régions anatomiques. / Motion estimation is a challenge in radiotherapy. It requires security margins to account for the incertitude on the tumor position. In this thesis, we address the problem of estimating the motion directly in the treatment room using the cone-beam projections. Firstly, we proposed a new breathing motion model that takes into account the sliding discontinuity between the rib-cage and the lungs. This method uses a segmentation of the inner part of the rib-cage which is obtained by an algorithm that requires the segmentation of the lungs and the rib-cage. The algorithms segmenting these parts were not robust enough and we proposed methods to improve their robustness. Compared to previous methods using this mask, our motion model is more robust to segmentation inconsistencies because it only requires a single mask instead of two consistent masks. Moreover, in case of 2D/3D registration, the computation of the second mask is usually not possible. The proposed model restricts the transformation to physically plausible motions and rely on a single segmentation. Secondly, we proposed a 2D/3D registration algorithm that uses our breathing model to extract motion from the cone-beam projections obtained just before the treatment. This algorithm was tested on simulated data. Then, we applied it to real data to reconstruct motion compensated images to remove motion blur from cone-beam CT. The proposed approach gives access to the patient motion just before the treatment, which can be used to daily adapt the treatment or to compute 4D dose maps. This approach can be used for other motions in other anatomic regions.

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