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Cancer is one of the leading causes of death in the world, but advances in research and development of treatment methods is constantly ongoing to reduce the number of deaths and the amount of suffering. One of many approaches is radiation therapy, which uses high doses of radiation to kill tumors. Radiation therapy requires advanced software in image analysis to create careful treatment plans, evaluate treatment responses and to perform dose accumulation, among other things. One important tool for this is deformable image registration (DIR) which is used to find a correspondence between the images. The aim with this master thesis is to improve the DIR method ANACONDA by automatically provide additional information to the algorithm before the registration is performed.This work focuses on the registration of internal tubular structures in lung and liver images (bronchial and vascular tree, respectively). Two challenges in registering lung images are the sliding motion of lung surfaces and large motion of small internal structures. Several DIR methods have been proposed for solving the challenging internal structures, however most of them do not take into account the alignment of surrounding tissues. DIR methods applied to the liver are published less frequently, but accurate registration of the liver is of high interest since, for example, knowledge of the anatomy of the vascular tree is essential when removing tumors through liver surgeries. In this work, corresponding (anatomical) points are automatically found in two images and added to the DIR algorithm. The points are found by extracting and comparing the tubular structures between the images, and with use of different distance requirements, nearby points are paired.The new method manages to achieve good registration of both internal structures and surrounding tissue. Mean target registration errors for the internal structures of lungs was 1.11 ± 0.75 and for liver 1.67 ± 1.15 mm.

<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)

Yes / Confocal microscopy is employed as a fast and non-invasive way to capture a sequence of images from different layers and membranes of the cornea. The captured images are used to extract useful and helpful clinical information for early diagnosis of corneal diseases such as, Diabetic Peripheral Neuropathy (DPN). In this paper, an automatic corneal subbasal nerve registration system is proposed. The main aim of the proposed system is to produce a new informative corneal image that contains structural and functional information. In addition a colour coded corneal image map is produced by overlaying a sequence of Cornea Confocal Microscopy (CCM) images that differ in their displacement, illumination, scaling, and rotation to each other. An automatic image registration method is proposed based on combining the advantages of Fast Fourier Transform (FFT) and phase correlation techniques. The proposed registration algorithm searches for the best common features between a number of sequenced CCM images in the frequency domain to produce the formative image map. In this generated image map, each colour represents the severity level of a specific clinical feature that can be used to give ophthalmologists a clear and precise representation of the extracted clinical features from each nerve in the image map. Moreover, successful implementation of the proposed system and the availability of the required datasets opens the door for other interesting ideas; for instance, it can be used to give ophthalmologists a summarized and objective description about a diabetic patient’s health status using a sequence of CCM images that have been captured from different imaging devices and/or at different times

Corneal diseases are one of the major causes of visual impairment and blindness worldwide. Used for diagnoses, a laser confocal microscope provides a sequence of images, at incremental depths, of the various corneal layers and structures. From these, ophthalmologists can extract clinical information on the state of health of a patient’s cornea. However, many factors impede ophthalmologists in forming diagnoses starting with the large number and variable quality of the individual images (blurring, non-uniform illumination within images, variable illumination between images and noise), and there are also difficulties posed for automatic processing caused by eye movements in both lateral and axial directions during the scanning process. Aiding ophthalmologists working with long sequences of corneal image requires the development of new algorithms which enhance, correctly order and register the corneal images within a sequence. The novel algorithms devised for this purpose and presented in this thesis are divided into four main categories. The first is enhancement to reduce the problems within individual images. The second is automatic image classification to identify which part of the cornea each image belongs to, when they may not be in the correct sequence. The third is automatic reordering of the images to place the images in the right sequence. The fourth is automatic registration of the images with each other. A flexible application called CORNEASYS has been developed and implemented using MATLAB and the C language to provide and run all the algorithms and methods presented in this thesis. CORNEASYS offers users a collection of all the proposed approaches and algorithms in this thesis in one platform package. CORNEASYS also provides a facility to help the research team and Ophthalmologists, who are in discussions to determine future system requirements which meet clinicians’ needs. / The data and image files accompanying this thesis are not available online.

Corneal diseases are one of the major causes of visual impairment and blindness worldwide. Used for diagnoses, a laser confocal microscope provides a sequence of images, at incremental depths, of the various corneal layers and structures. From these, ophthalmologists can extract clinical information on the state of health of a patient’s cornea. However, many factors impede ophthalmologists in forming diagnoses starting with the large number and variable quality of the individual images (blurring, non-uniform illumination within images, variable illumination between images and noise), and there are also difficulties posed for automatic processing caused by eye movements in both lateral and axial directions during the scanning process. Aiding ophthalmologists working with long sequences of corneal image requires the development of new algorithms which enhance, correctly order and register the corneal images within a sequence. The novel algorithms devised for this purpose and presented in this thesis are divided into four main categories. The first is enhancement to reduce the problems within individual images. The second is automatic image classification to identify which part of the cornea each image belongs to, when they may not be in the correct sequence. The third is automatic reordering of the images to place the images in the right sequence. The fourth is automatic registration of the images with each other. A flexible application called CORNEASYS has been developed and implemented using MATLAB and the C language to provide and run all the algorithms and methods presented in this thesis. CORNEASYS offers users a collection of all the proposed approaches and algorithms in this thesis in one platform package. CORNEASYS also provides a facility to help the research team and Ophthalmologists, who are in discussions to determine future system requirements which meet clinicians’ needs.

Le cancer pulmonaire est la principale cause de décès parmi tous les cancers au Canada. Le pronostic est généralement faible, de l'ordre de 15% de taux de survie après 5 ans. Les déplacements internes des structures anatomiques apportent une incertitude sur la précision des traitements en radio-oncologie, ce qui diminue leur efficacité. Dans cette optique, certaines techniques comme la radio-chirurgie et la radiothérapie par modulation de l'intensité (IMRT) visent à améliorer les résultats cliniques en ciblant davantage la tumeur. Ceci permet d'augmenter la dose reçue par les tissus cancéreux et de réduire celle administrée aux tissus sains avoisinants. Ce projet vise à mieux évaluer la dose réelle reçue pendant un traitement considérant une anatomie en mouvement. Pour ce faire, des plans de CyberKnife et d'IMRT sont recalculés en utilisant un algorithme Monte Carlo 4D de transport de particules qui permet d'effectuer de l'accumulation de dose dans une géométrie déformable. Un environnement de simulation a été développé afin de modéliser ces deux modalités pour comparer les distributions de doses standard et 4D. Les déformations dans le patient sont obtenues en utilisant un algorithme de recalage déformable d'image (DIR) entre les différentes phases respiratoire générées par le scan CT 4D. Ceci permet de conserver une correspondance de voxels à voxels entre la géométrie de référence et celles déformées. La DIR est calculée en utilisant la suite ANTs («Advanced Normalization Tools») et est basée sur des difféomorphismes. Une version modifiée de DOSXYZnrc de la suite EGSnrc, defDOSXYZnrc, est utilisée pour le transport de particule en 4D. Les résultats sont comparés à une planification standard afin de valider le modèle actuel qui constitue une approximation par rapport à une vraie accumulation de dose en 4D. / Pulmonary cancer is the main cause of death amongst all cancers in Canada with a prognosis of about 15% survival rate in 5 years. The efficiency of radiotherapy treatments is lower when high displacements of the tumors are observed, mostly caused by intrafraction respiratory motion. Advanced techniques such as radiosurgery and intensity-modulated radiotherapy treatments (IMRT) are expected to provide better clinical results by delivering higher radiation doses to the tumor while sparing the surrounding healthy lung tissues. The goal of this project is to perform 4D Monte Carlo dose recalculations to assess the dosimetric impact of moving tumors in CyberKnife and IMRT treatments using dose accumulation in deforming anatomies. Scripts developed in-house were used to model both situations and to compare the Monte Carlo dose distributions with those obtained with standard clinical plans. Displacement vectors fields are obtained from a 4D CT data set and a deformable image registration (DIR) algorithm which allows a voxel-to-voxel correspondence between each respiratory phase. The DIR is computed by the Advanced Normalization Tools (ANTs) software and is mostly based on diffeormophisms. A modified version of DOSXYZnrc from EGSnrc software, defDOSXYZnrc, is used to transport radiation through non-linear geometries. These results are then compared to a typical 3D plan to determine whether or not the current planification is a good approximation of the true 4D dose calculation.

Alzheimer’s disease (AD) is the leading cause of dementia and is alone responsible for 60-70% of all cases of dementia. Though sharing clinical symptoms with other types of dementia, the hallmarks of AD are the abundance of extracellular depositions of β-amyloid (Aβ) plaques, intracellular neurofibrillary tangles of hyper phosphorylated tau proteins and synaptic depletion. The onset of the physiological hallmarks may precede clinical symptoms with a decade or more, and once clinical symptoms occur it may be difficult to separate AD from other types of dementia based on clinical symptoms alone. Since the introduction of radiolabeled Aβ tracer substances for positron emission tomography (PET) imaging it is possible to image the Aβ depositions in-vivo, strengthening the confidence in the diagnosis. Because the accumulation of Aβ may occur years before the first clinical symptoms are shown and even reach a plateau, Aβ PET imaging may not be feasible for disease progress monitoring. However, a negative scan may be used to rule out AD as the underlying cause to the clinical symptoms. It may also be used as a predictor to evaluate the risk of developing AD in patients with mild cognitive impairment (MCI) as well as monitoring potential effects of anti-amyloid drugs.Though currently validated for dichotomous visual assessment only, there is evidence to suggest that quantification of Aβ PET images may reduce inter-reader variability and aid in the monitoring of treatment effects from anti-amyloid drugs.The aim of this thesis was to refine existing methods and develop new ones for processing, quantification and visualization of Aβ PET images to aid in the diagnosis and monitoring of potential treatment of AD in clinical routine. Specifically, the focus for this thesis has been to find a way to fully automatically quantify and visualize a patient’s Aβ PET image in such way that it is presented in a uniform way and show how it relates to what is considered normal. To achieve the aim of the thesis registration algorithms, providing the means to register a patient’s Aβ PET image to a common stereotactic space avoiding the bias of different uptake patterns for Aβ- and Aβ+ images, a suitable region atlas and a 3-dimensional stereotactic surface projections (3D SSP) method, capable of projecting cortical activity onto the surface of a 3D model of the brain without sampling white matter, were developed and evaluated.The material for development and testing comprised 724 individual amyloid PET brain images from six distinct cohorts, ranging from healthy volunteers to definite AD. The new methods could be implemented in a fully automated workflow and were found to be highly accurate, when tested by comparisons to Standards of Truth, such as defining regional uptake from PET images co-registered to magnetic resonance images, post-mortem histopathology and the visual consensus diagnosis of imaging experts.

Ce travail de thèse porte sur la quantification et la prise en compte des variations anatomiques en cours de radiothérapie guidée par l'image pour le cancer de la prostate. Nous proposons tout d'abord une approche basée population pour quantifier et analyser les incertitudes géométriques, notamment à travers des matrices de probabilité de présence de la cible en cours de traitement. Nous proposons ensuite une méthode d'optimisation des marges suivant des critères de couverture géométrique de la cible tumorale. Cette méthode permet d'obtenir des marges objectives associées aux différents types d'incertitudes géométriques et aux différentes modalités de repositionnement du patient. Dans un second temps, nous proposons une méthode d'estimation de la dose cumulée reçue localement par les tissus pendant un traitement de radiothérapie de la prostate. Cette méthode repose notamment sur une étape de recalage d'images de façon à estimer les déformations des organes entre les séances de traitement et la planification. Différentes méthodes de recalage sont proposées, suivant les informations disponibles (délinéations ou points homologues) pour contraindre la déformation estimée. De façon à évaluer les méthodes proposées au regard de l'objectif de cumul de dose, nous proposons ensuite la génération et l'utilisation d'un fantôme numérique reposant sur un modèle biomécanique des organes considérés. Les résultats de l'approche sont présentés sur ce fantôme numérique et sur données réelles. Nous montrons ainsi que l'apport de contraintes géométriques permet d'améliorer significativement la précision du cumul et que la méthode reposant sur la sélection de contraintes ponctuelles présente un bon compromis entre niveau d'interaction et précision du résultat. Enfin, nous abordons la question de l'analyse de données de populations de patients dans le but de mieux comprendre les relations entre dose délivrée localement et effets cliniques. Grâce au recalage déformable d'une population de patients sur une référence anatomique, les régions dont la dose est significativement liée aux événements de récidive sont identifiées. Il s'agit d'une étude exploratoire visant à terme à mieux exploiter l'information portée par l'intégralité de la distribution de dose, et ce en fonction du profil du cancer. / This work deals with the quantification and the compensation of anatomical deformations during image-guided radiotherapy of prostate cancer. Firstly, we propose a population-based approach to quantify the geometrical uncertainties by means of coverage probability matrices of the target tumor during the treatment. We then propose a margins optimization method based on geometrical coverage criteria of the tumor target. This method provides rationnal margins models associated to the different geometrical uncertainties and patient repositioning protocols. Secondly, we propose a method to estimate the locally accumulated dose during the treatment. This method relies on a deformable image registration process in order to estimate the organ deformations between each treatment fraction and the planning. Different registration methods are proposed, using different level of user interactions (landmarks specification or delineations) to constrain the deformation estimation. In order to evaluate the performance of the proposed methods, we then describe the generation of a numerical phantom based on a biomechanical model. The results are presented for the numerical phantom and real clinical cases. We show that the benefit brought by the manual placement of some landmarks to constrain the registration represents a good compromise between the required interaction level and the dose estimation accuracy. Finally, we address the issue of the analysis of population data in order to better understand the relationship between the locally delivered dose and clinical effects. With deformable image registration of a population of patients on an anatomical template, regions whose dose is significantly associated with recurrence events are identified. This last part is an exploratory study aiming to better use the information carried by the entire distribution dose, and according to the cancer profile.