A Framework on A Computer Assisted and Systematic Methodology for Detection of Chronic Lower Back Pain using Artificial Intelligence and Computer Graphics Technologies

Back pain is one of the major musculoskeletal pain problems that can affect many people and is considered as one of the main causes of disability all over the world. Lower back pain, which is the most common type of back pain, is estimated to affect at least 60% to 80% of the adult population in the United Kingdom at some time in their lives. Some of those patients develop a more serious condition namely Chronic Lower Back Pain in which physicians must carry out a more involved diagnostic procedure to determine its cause. In most cases, this procedure involves a long and laborious task by the physicians to visually identify abnormalities from the patient’s Magnetic Resonance Images. Limited technological advances have been made in the past decades to support this process. This paper presents a comprehensive literature review on these technological advances and presents a framework of a methodology for diagnosing and predicting Chronic Lower Back Pain. This framework will combine current state-of-the-art computing technologies including those in the area of artificial intelligence, physics modelling, and computer graphics, and is argued to be able to improve the diagnosis process

A Machine Learning and Computer Assisted Methodology for Diagnosing Chronic Lower Back Pain on Lumbar Spine Magnetic Resonance Images

Chronic Lower Back Pain (CLBP) is one of the major types of pain that affects many people around the world. It is estimated that 28.1% of US adults suffer from this illness and 2.5 million of the UK population experience this type of pain every day. Most CLBP cases do not happen overnight and it is usually developed from a less serious but acute variant of lower back pain. An acute type of lower back pain can develop into a chronic one if the underlying cause is serious and left untreated. The longer a person is disabled by back pain, the less chance he or she returns to work and the more health care cost he or she will require. It is therefore important to identify the cause of back pains as early as possible in order to improve the chance of patient rehabilitation. The speediness of early diagnosis can depend on many factors including referral time from a general practitioner to the hospital, waiting time for a specialist appointment, time for a Magnetic Resonance Imaging (MRI) scan and time for the analysis result to come out. Currently diagnosing the lower back pain is done by visual observation and analysis of the lumbar spine MRI images by radiologists and clinicians and this process could take up much of their time and effort. This, therefore, rationalizes the need for a new method to increase the efficiency and effectiveness of the imaging diagnostic process. This thesis details a novel methodology to automatically aid clinicians in performing diagnosis of CLBP on lumbar spine MRI images. The methodology is based on the current accepted medical practice of manual inspection of the MRI scans of the patient’s lumbar spine as advised by several practitioners in this field. The main methodology is divided into three sub-methods the first sub-method is disc herniation detection using disc segmentation and centroid distance function. While the second sub-method is lumbar spinal stenosis detection via segmentation of area between anterior and posterior (AAP) Elements. Whereas, the last sub-method is the use of deep learning to perform semantic segmentation to identify regions in the MRI images that are relevant to the diagnosis process. The method then performs boundary delineation between these regions, identifies key points along the boundaries and measures distances between these points that can be used as an indication to the health of the lumbar spine. Due to a limitation in the size and suitability of the currently existing open-access lumbar spine dataset necessary to train and test any good classification algorithms, a dataset consisting of 48,345 MRI slices from a complete clinical lumbar MRI study of 515 symptomatic back pain patients from several specialty hospitals around the world has been created. Each MRI study is annotated by expert radiologists with notes regarding the observed characteristics, condition of the lumbar spine, or presence of diseases. The ground-truth dataset containing manually labelled segmented images has also been developed. To complement this ground-truth dataset, a novel method of constructing and evaluating the suitability of ground truth data for lumbar spine MRI image segmentation has been developed. A subset of the dataset, which includes the data for 101 patients, is used in a set of experiments that have been conducted using a variety of algorithms to conclude with using SegNet as the image segmentation algorithm. The network consists of VGG16 layers pre-trained using a subset of non-medical images from the ImageNet database and fine-tuned using the training portion of the ground-truth dataset. The results of these experiments show the accurate delineation of important boundaries of regions in lumbar spine MRI. The experiments also show very close agreement between the expert radiologists’ notes on the condition of a lumbar spine and the conclusion of the system about the lumbar spine in the majority of cases

Probabilistic and geometric shape based segmentation methods.

Image segmentation is one of the most important problems in image processing, object recognition, computer vision, medical imaging, etc. In general, the objective of the segmentation is to partition the image into the meaningful areas using the existing (low level) information in the image and prior (high level) information which can be obtained using a number of features of an object. As stated in [1,2], the human vision system aims to extract and use as much information as possible in the image including but not limited to the intensity, possible motion of the object (in sequential images), spatial relations (interaction) as the existing information, and the shape of the object which is learnt from the experience as the prior information. The main objective of this dissertation is to couple the prior information with the existing information since the machine vision system cannot predict the prior information unless it is given. To label the image into meaningful areas, the chosen information is modelled to fit progressively in each of the regions by an optimization process. The intensity and spatial interaction (as the existing information) and shape (as the prior information) are modeled to obtain the optimum segmentation in this study. The intensity information is modelled using the Gaussian distribution. Spatial interaction that describes the relation between neighboring pixels/voxels is modelled by assuming that the pixel intensity depends on the intensities of the neighboring pixels. The shape model is obtained using occurrences of histogram of training shape pixels or voxels. The main objective is to capture the shape variation of the object of interest. Each pixel in the image will have three probabilities to be an object and a background class based on the intensity, spatial interaction, and shape models. These probabilistic values will guide the energy (cost) functionals in the optimization process. This dissertation proposes segmentation frameworks which has the following properties: i) original to solve some of the existing problems, ii) robust under various segmentation challenges, and iii) fast enough to be used in the real applications. In this dissertation, the models are integrated into different methods to obtain the optimum segmentation: 1) variational (can be considered as the spatially continuous), and 2) statistical (can be considered as the spatially discrete) methods. The proposed segmentation frameworks start with obtaining the initial segmentation using the intensity / spatial interaction models. The shape model, which is obtained using the training shapes, is registered to the image domain. Finally, the optimal segmentation is obtained using the optimization of the energy functionals. Experiments show that the use of the shape prior improves considerably the accuracy of the alternative methods which use only existing or both information in the image. The proposed methods are tested on the synthetic and clinical images/shapes and they are shown to be robust under various noise levels, occlusions, and missing object information. Vertebral bodies (VBs) in clinical computed tomography (CT) are segmented using the proposed methods to help the bone mineral density measurements and fracture analysis in bones. Experimental results show that the proposed solutions eliminate some of the existing problems in the VB segmentation. One of the most important contributions of this study is to offer a segmentation framework which can be suitable to the clinical works

Modeling and visualization of medical anesthesiology acts

Dissertação para obtenção do Grau de Mestre em Engenharia InformáticaIn recent years, medical visualization has evolved from simple 2D images on a light board to 3D computarized images. This move enabled doctors to find better ways of planning surgery and to diagnose patients. Although there is a great variety of 3D medical imaging software, it falls short when dealing with anesthesiology acts. Very little anaesthesia related work has been done. As a consequence, doctors and medical students have had little support to study the subject of anesthesia in the human body. We all are aware of how costly can be setting medical experiments, covering not just medical aspects but ethical and financial ones as well. With this work we hope to contribute for having better medical visualization tools in the area of anesthesiology. Doctors and in particular medical students should study anesthesiology acts more efficiently. They should be able to identify better locations to administrate the anesthesia, to study how long does it take for the anesthesia to affect patients, to relate the effect on patients with quantity of anaesthesia provided, etc. In this work, we present a medical visualization prototype with three main functionalities: image pre-processing, segmentation and rendering. The image pre-processing is mainly used to remove noise from images, which were obtained via imaging scanners. In the segmentation stage it is possible to identify relevant anatomical structures using proper segmentation algorithms. As a proof of concept, we focus our attention in the lumbosacral region of the human body, with data acquired via MRI scanners. The segmentation we provide relies mostly in two algorithms: region growing and level sets. The outcome of the segmentation implies the creation of a 3D model of the anatomical structure under analysis. As for the rendering, the 3D models are visualized using the marching cubes algorithm. The software we have developed also supports time-dependent data. Hence, we could represent the anesthesia flowing in the human body. Unfortunately, we were not able to obtain such type of data for testing. But we have used human lung data to validate this functionality

In vivo lumbar spine biomechanics : vertebral kinematics, intervertebral disc deformation, and disc loads

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Mechanical Engineering, 2012.Cataloged from PDF version of thesis.Includes bibliographical references.Knowledge of lumbar spine biomechanics in living human subjects is fundamental for understanding mechanisms of spinal injury and pathology, for improvement of corresponding clinical treatments, and for design of spinal prosthesis. However, due to the complicated spine anatomy and loading conditions as well as high risks in these direct measurements, it has been a challenge to determine the in vivo biomechanics of the lumbar spine. To address this problem, the overall objective of this thesis was to develop and implement a dual fluoroscopic imaging system to non-invasively study human lumbar spine biomechanics. In line with this objective, the first goal was to quantify the ability of the dual fluoroscopic imaging system to determine vertebral kinematics. The second goal was to implement this technique to investigate spinal motion in both healthy subjects and patients with pathology. The third goal was to explore the feasibility of using kinematic data obtained from this system as boundary conditions in finite element analysis to calculate the physiological loads on the intervertebral disc. The system was shown to be accurate and repeatable in determining the vertebral kinematics in all degrees of freedom. For the first time, six degree-of-freedom motion of different structures of the spine, such as the vertebral body, intervertebral disc, facet joint and spinous process were measured in vivo in both healthy subjects and subjects with pathology during functional activities. In general, the group of subjects with pathology showed a significantly abnormal kinematic response during various physiological functional activities. Preliminary studies have shown the applicability and high accuracy of finite element modeling to calculate disc loads using in vivo vertebral kinematics as displacement boundary conditions.by Shaobai Wang.Ph.D

Development of ultrasound to measure deformation of functional spinal units in cervical spine

Neck pain is a pervasive problem in the general population, especially in those working in vibrating environments, e.g. military troops and truck drivers. Previous studies showed neck pain was strongly associated with the degeneration of intervertebral disc, which is commonly caused by repetitive loading in the work place. Currently, there is no existing method to measure the in-vivo displacement and loading condition of cervical spine on the site. Therefore, there is little knowledge about the alternation of cervical spine functionality and biomechanics in dynamic environments. In this thesis, a portable ultrasound system was explored as a tool to measure the vertebral motion and functional spinal unit deformation. It is hypothesized that the time sequences of ultrasound imaging signals can be used to characterize the deformation of cervical spine functional spinal units in response to applied displacements and loading. Specifically, a multi-frame tracking algorithm is developed to measure the dynamic movement of vertebrae, which is validated in ex-vivo models. The planar kinematics of the functional spinal units is derived from a dual ultrasound system, which applies two ultrasound systems to image C-spine anteriorly and posteriorly. The kinematics is reconstructed from the results of the multi-frame movement tracking algorithm and a method to co-register ultrasound vertebrae images to MRI scan. Using the dual ultrasound, it is shown that the dynamic deformation of functional spinal unit is affected by the biomechanics properties of intervertebral disc ex-vivo and different applied loading in activities in-vivo. It is concluded that ultrasound is capable of measuring functional spinal units motion, which allows rapid in-vivo evaluation of C-spine in dynamic environments where X-Ray, CT or MRI cannot be used.2020-02-20T00:00:00

Anatomo-functional magnetic resonance imaging of the spinal cord and its application to the characterization of spinal lesions in cats

Les lésions de la moelle épinière ont un impact significatif sur la qualité de la vie car elles peuvent induire des déficits moteurs (paralysie) et sensoriels. Ces déficits évoluent dans le temps à mesure que le système nerveux central se réorganise, en impliquant des mécanismes physiologiques et neurochimiques encore mal connus. L'ampleur de ces déficits ainsi que le processus de réhabilitation dépendent fortement des voies anatomiques qui ont été altérées dans la moelle épinière. Il est donc crucial de pouvoir attester l'intégrité de la matière blanche après une lésion spinale et évaluer quantitativement l'état fonctionnel des neurones spinaux. Un grand intérêt de l'imagerie par résonance magnétique (IRM) est qu'elle permet d'imager de façon non invasive les propriétés fonctionnelles et anatomiques du système nerveux central. Le premier objectif de ce projet de thèse a été de développer l'IRM de diffusion afin d'évaluer l'intégrité des axones de la matière blanche après une lésion médullaire. Le deuxième objectif a été d'évaluer dans quelle mesure l'IRM fonctionnelle permet de mesurer l'activité des neurones de la moelle épinière. Bien que largement appliquées au cerveau, l'IRM de diffusion et l'IRM fonctionnelle de la moelle épinière sont plus problématiques. Les difficultés associées à l'IRM de la moelle épinière relèvent de sa fine géométrie (environ 1 cm de diamètre chez l'humain), de la présence de mouvements d'origine physiologique (cardiaques et respiratoires) et de la présence d'artefacts de susceptibilité magnétique induits par les inhomogénéités de champ, notamment au niveau des disques intervertébraux et des poumons. L'objectif principal de cette thèse a donc été de développer des méthodes permettant de contourner ces difficultés. Ce développement a notamment reposé sur l'optimisation des paramètres d'acquisition d'images anatomiques, d'images pondérées en diffusion et de données fonctionnelles chez le chat et chez l'humain sur un IRM à 3 Tesla. En outre, diverses stratégies ont été étudiées afin de corriger les distorsions d'images induites par les artefacts de susceptibilité magnétique, et une étude a été menée sur la sensibilité et la spécificité de l'IRM fonctionnelle de la moelle épinière. Les résultats de ces études démontrent la faisabilité d'acquérir des images pondérées en diffusion de haute qualité, et d'évaluer l'intégrité de voies spinales spécifiques après lésion complète et partielle. De plus, l'activité des neurones spinaux a pu être détectée par IRM fonctionnelle chez des chats anesthésiés. Bien qu'encourageants, ces résultats mettent en lumière la nécessité de développer davantage ces nouvelles techniques. L'existence d'un outil de neuroimagerie fiable et robuste, capable de confirmer les paramètres cliniques, permettrait d'améliorer le diagnostic et le pronostic chez les patients atteints de lésions médullaires. Un des enjeux majeurs serait de suivre et de valider l'effet de diverses stratégies thérapeutiques. De telles outils représentent un espoir immense pour nombre de personnes souffrant de traumatismes et de maladies neurodégénératives telles que les lésions de la moelle épinière, les tumeurs spinales, la sclérose en plaques et la sclérose latérale amyotrophique.Spinal cord injury has a significant impact on quality of life since it can lead to motor (paralysis) and sensory deficits. These deficits evolve in time as reorganisation of the central nervous system occurs, involving physiological and neurochemical mechanisms that are still not fully understood. Given that both the severity of the deficit and the successful rehabilitation process depend on the anatomical pathways that have been altered in the spinal cord, it may be of great interest to assess white matter integrity after a spinal lesion and to evaluate quantitatively the functional state of spinal neurons. The great potential of magnetic resonance imaging (MRI) lies in its ability to investigate both anatomical and functional properties of the central nervous system non invasively. To address the problem of spinal cord injury, this project aimed to evaluate the benefits of diffusion-weighted MRI to assess the integrity of white matter axons that remain after spinal cord injury. The second objective was to evaluate to what extent functional MRI can measure the activity of neurons in the spinal cord. Although widely applied to the brain, diffusion-weighted MRI and functional MRI of the spinal cord are not straightforward. Various issues arise from the small cross-section width of the cord, the presence of cardiac and respiratory motions, and from magnetic field inhomogeneities in the spinal region. The main purpose of the present thesis was therefore to develop methodologies to circumvent these issues. This development notably focused on the optimization of acquisition parameters to image anatomical, diffusion-weighted and functional data in cats and humans at 3T using standard coils and pulse sequences. Moreover, various strategies to correct for susceptibility-induced distortions were investigated and the sensitivity and specificity in spinal cord functional MRI was studied. As a result, acquisition of high spatial and angular diffusion-weighted images and evaluation of the integrity of specific spinal pathways following spinal cord injury was achieved. Moreover, functional activations in the spinal cord of anaesthetized cats was detected. Although encouraging, these results highlight the need for further technical and methodological development in the near-future. Being able to develop a reliable neuroimaging tool for confirming clinical parameters would improve diagnostic and prognosis. It would also enable to monitor the effect of various therapeutic strategies. This would certainly bring hope to a large number of people suffering from trauma and neurodegenerative diseases such as spinal cord injury, tumours, multiple sclerosis and amyotrophic lateral sclerosis

Segmentation automatique de la moelle épinière sur des images de résonance magnétique par propagation de modèles déformables

RÉSUMÉ Les lésions de la moelle épinière, induites par des traumas (e.g. accident de la route) ou par des maladies neurodégénératives, touchent plus 85 000 personnes au Canada avec environ 4250 nouveaux cas chaque année1. Elles ont de plus un impact majeur sur la vie quotidienne des personnes atteintes, en provoquant des pertes de sensibilité et de contrôle moteur dont la gravité dépend de la taille et de l’emplacement des lésions. Bien qu’il existe des approches thérapeutiques permettant d’améliorer la réhabilitation fonctionnelle des patients, toutes ces approches se heurtent à une inconnue majeure : l’étendue des dégâts causés par les lésions. Un diagnostic précoce et précis des maladies neurodégénératives touchant la moelle épinière permettrait d’améliorer grandement l’efficacité de leurs traitements. Depuis de nombreuses années, l’IRM a prouvé son potentiel dans le diagnostic et le pronostic des lésions de la moelle épinière (Cadotte, 2011; Cohen-Adad et al., 2011). Ce domaine manque cependant encore d’outils complètement automatisés permettant l’extraction et la comparaison de métriques cliniques reliées à la structure de la moelle (aire de section transverse, volume, etc.). La segmentation de la moelle épinière sur des images IRM anatomiques peut fournir des mesures d’aires et de volumes de la moelle (Losseff et al., 1996) et peut quantifier son atrophie en cas de maladies neurodégénératives telles que la sclérose en plaques (Chen et al., 2013) et la sclérose latérale amyotrophique (Cohen-Adad et al., 2011). Ce projet de maîtrise vise à développer une méthode de segmentation complètement automatique de la moelle épinière, fonctionnant sur plusieurs types d’images IRM (pondérées en T1 et en T2) et sur n’importe quel champ de vue (cervical ou thoracique), et permettant d’extraire et de comparer des mesures précises de la moelle épinière. La revue de la littérature a permis de mettre en évidence le manque de méthode de segmentation automatique de la moelle épinière fonctionnant sur n’importe quel type de contraste et de champ de vue. Elle a toutefois fait ressortir une série de propriétés intéressantes, dans les méthodes semi-automatiques existantes, pouvant être combinées pour former une méthode complètement automatisée.----------ABSTRACT Spinal cord lesions affects more than 85,000 people in Canada with about 4,250 new cases every year. Lesions can be caused by traumatic injuries or by neurodegenerative diseases such as multiple sclerosis. They have an important impact on a patient’s daily life, inducing loss of sensibility or motor control in the human body. The extent of damages caused by a lesion varies with the number of damaged spinal cord tracks, and depends on the size and the position of the lesion within the spinal cord. Although therapeutic approaches for patient functional rehabilitation exist, they all face an unknown variable: the extent of spinal cord lesions. A precise and early diagnosis of neurodegenerative diseases would improve their treatment efficiency. For a number of years, MRI has demonstrated its potential in the diagnosis and prognosis of spinal cord lesions (Cadotte, 2011; Cohen-Adad et al., 2010). However, this research field still lacks of fully automatized tools for the extraction and comparison of clinical metrics related to the spinal cord structure (e.g. cross-sectional area, volumes). Spinal cord segmentation on anatomical MR images can provide accurate area and volume measurements (Losseff et al., 1996) and could quantify spinal cord atrophy caused by neurodegenerative diseases such as multiple sclerosis (Chen et al., 2013) or amyotrophic lateral sclerosis (Cohen-Adad et al., 2011). The objective of this Master’s project is to develop a fully automatic spinal cord segmentation method, working on multiple MR contrasts and any field of view, able to extract and compare accurate spinal cord measurements. The literature review pointed out the lack of such a method but highlighted several interesting features in existing methods, that can be combined to develop a new automatic segmentation algorithm. The method developed in this project is based on the multi-resolution propagation of a deformable model. First, the spinal cord position and orientation is detected in the image using an elliptical Hough transform on multiple adjacent axial slices. A low-resolution tubular mesh is then build around the detection point and direction and deformed on spinal cord edges by minimizing an energy equation. An iterative process, composed by the duplication, translation, orientation and deformation of the mesh, propagates the surface along the spinal cord. Finally, a refinement and a global deformation of the surface provide accurate segmentation of the spinal cord. Measurements can be directly extracted from the segmentation surface. The spinal canal can also be segmented with our method by simply inversing the gradient in the image an

Development of an MRI Template and Analysis Pipeline for the Spinal Cord and Application in Patients with Spinal Cord Injury

La moelle épinière est un organe fondamental du corps humain. Étant le lien entre le cerveau et le système nerveux périphérique, endommager la moelle épinière, que ce soit suite à un trauma ou une maladie neurodégénérative, a des conséquences graves sur la qualité de vie des patients. En effet, les maladies et traumatismes touchant la moelle épinière peuvent affecter l’intégrité des neurones et provoquer des troubles neurologiques et/ou des handicaps fonctionnels. Bien que de nombreuses voies thérapeutiques pour traiter les lésions de la moelle épinière existent, la connaissance de l’étendue des dégâts causés par ces lésions est primordiale pour améliorer l’efficacité de leur traitement et les décisions cliniques associées. L’imagerie par résonance magnétique (IRM) a démontré un grand potentiel pour le diagnostic et pronostic des maladies neurodégénératives et traumas de la moelle épinière. Plus particulièrement, l’analyse par template de données IRM du cerveau, couplée à des outils de traitement d’images automatisés, a permis une meilleure compréhension des mécanismes sous-jacents de maladies comme l’Alzheimer et la Sclérose en Plaques. Extraire automatiquement des informations pertinentes d’images IRM au sein de régions spécifiques de la moelle épinière présente toutefois de plus grands défis que dans le cerveau. Il n’existe en effet qu’un nombre limité de template de la moelle épinière dans la littérature, et aucun ne couvre toute la moelle épinière ou n’est lié à un template existant du cerveau. Ce manque de template et d’outils automatisés rend difficile la tenue de larges études d’analyse de la moelle épinière sur des populations variées. L’objectif de ce projet est donc de proposer un nouveau template IRM couvrant toute la moelle épinière, recalé avec un template existant du cerveau, et intégrant des atlas de la structure interne de la moelle épinière (e.g., matière blanche et grise, tracts de la matière blanche). Ce template doit venir avec une série d’outils automatisés permettant l’extraction d’information IRM au sein de régions spécifiques de la moelle épinière. La question générale de recherche de ce projet est donc « Comment créer un template générique de la moelle épinière, qui permettrait l’analyse non biaisée et reproductible de données IRM de la moelle épinière ? ». Plusieurs contributions originales ont été proposées pour répondre à cette question et vont être décrites dans les prochains paragraphes. La première contribution de ce projet est le développement du logiciel Spinal Cord Toolbox (SCT). SCT est un logiciel open-source de traitement d’images IRM multi-parametrique de la moelle épinière (De Leener, Lévy, et al., 2016). Ce logiciel intègre notamment des outils pour la détection et la segmentation automatique de la moelle épinière et de sa structure interne (i.e., matière blanche et matière grise), l’identification et la labellisation des niveaux vertébraux, le recalage d’images IRM multimodales sur un template générique de la moelle épinière (précédemment le template MNI-Poly-AMU, maintenant le template PAM50, proposé içi). En se basant sur un atlas de la moelle, SCT intègre également des outils pour extraire des données IRM de régions spécifiques de la moelle épinière, comme la matière blanche et grise et les tracts de la matière blanche, ainsi que sur des niveaux vertébraux spécifiques. D’autres outils additionnels ont aussi été proposés, comme des outils de correction de mouvement et de traitement basiques d’images appliqués le long de la moelle épinière. Chaque outil intégré à SCT a été validé sur un jeu de données multimodales. La deuxième contribution de ce projet est le développement d’une nouvelle méthode de recalage d’images IRM de la moelle épinière (De Leener, Mangeat, et al., 2017). Cette méthode a été développée pour un usage particulier : le redressement d’images IRM de la moelle épinière, mais peut également être utilisé pour recaler plusieurs images de la moelle épinière entre elles, tout en tenant compte de la distribution vertébrale de chaque sujet. La méthode proposée se base sur une approximation globale de la courbure de la moelle épinière dans l’espace et sur la résolution analytique des champs de déformation entre les deux images. La validation de cette nouvelle méthode a été réalisée sur une population de sujets sains et de patients touchés par une compression de la moelle épinière. La contribution majeure de ce projet est le développement d’un système de création de template IRM de la moelle épinière et la proposition du template PAM50 comme template de référence pour les études d’analyse par template de données IRM de la moelle épinière. Le template PAM50 a été créé à partir d’images IRM tiré de 50 sujets sains, et a été généré en utilisant le redressement d’images présenté ci-dessus et une méthode de recalage d’images itératif non linéaire, après plusieurs étapes de prétraitement d’images. Ces étapes de prétraitement incluent la segmentation automatique de la moelle épinière, l’extraction manuelle du bord antérieur du tronc cérébral, la détection et l’identification des disques intervertébraux, et la normalisation d’intensité le long de la moelle. Suite au prétraitement, la ligne centrale moyenne de la moelle et la distribution vertébrale ont été calculées sur la population entière de sujets et une image initiale de template a été générée. Après avoir recalé toutes les images sur ce template initial, le template PAM50 a été créé en utilisant un processus itératif de recalage d’image, utilisé pour générer des templates de cerveau. Le PAM50 couvre le tronc cérébral et la moelle épinière en entier, est disponible pour les contrastes IRM pondérés en T1, T2 et T2*, et intègre des cartes probabilistes et atlas de la structure interne de la moelle épinière. De plus, le PAM50 a été recalé sur le template ICBM152 du cerveau, permettant ainsi la tenue d’analyse par template simultanément dans le cerveau et dans la moelle épinière. Finalement, plusieurs résultats complémentaires ont été présentés dans cette dissertation. Premièrement, une étude de validation de la répétabilité et reproductibilité de mesures de l’aire de section de la moelle épinière a été menée sur une population de patients touchés par la sclérose en plaques. Les résultats démontrent une haute fiabilité des mesures ainsi que la possibilité de détecter des changements très subtiles de l’aire de section transverse de la moelle, importants pour mesurer l’atrophie de la moelle épinière précoce due à des maladies neurodégénératives comme la sclérose en plaques. Deuxièmement, un nouveau biomarqueur IRM des lésions de la moelle épinière a été proposé, en collaboration avec Allan Martin, de l’Université de Toronto. Ce biomarqueur, calculé à partir du ratio d’intensité entre la matière blanche et grise sur des images IRM pondérées en T2*, utilise directement les développements proposés dans ce projet, notamment en utilisant le recalage du template de la moelle épinière et les méthodes de segmentation de la moelle. La faisabilité d’extraire des mesures de données IRM multiparamétrique dans des régions spécifiques de la moelle épinière a également été démontrée, permettant d’améliorer le diagnostic et pronostic de lésions et compression de la moelle épinière. Finalement, une nouvelle méthode d’extraction de la morphométrie de la moelle épinière a été proposée et utilisée sur une population de patients touchés par une compression asymptomatique de la moelle épinière, démontrant de grandes capacités de diagnostic (> 99%). Le développement du template PAM50 comble le manque de template de la moelle épinière dans la littérature mais présente cependant plusieurs limitations. En effet, le template proposé se base sur une population de 50 sujets sains et jeunes (âge moyen = 27 +- 6.5) et est donc biaisée vers cette population particulière. Adapter les analyses par template pour un autre type de population (âge, race ou maladie différente) peut être réalisé directement sur les méthodes d’analyse mais aussi sur le template en lui-même. Tous le code pour générer le template a en effet été mis en ligne (https://github.com/neuropoly/template) pour permettre à tout groupe de recherche de développer son propre template. Une autre limitation de ce projet est le choix d’un système de coordonnées basé sur la position des vertèbres. En effet, les vertèbres ne représentent pas complètement le caractère fonctionnel de la moelle épinière, à cause de la différence entre les niveaux vertébraux et spinaux. Le développement d’un système de coordonnées spinal, bien que difficile à caractériser dans des images IRM, serait plus approprié pour l’analyse fonctionnelle de la moelle épinière. Finalement, il existe encore de nombreux défis pour automatiser l’ensemble des outils développés dans ce projet et les rendre robuste pour la majorité des contrastes et champs de vue utilisés en IRM conventionnel et clinique. Ce projet a présenté plusieurs développements importants pour l’analyse de données IRM de la moelle épinière. De nombreuses améliorations du travail présenté sont cependant requises pour amener ces outils dans un contexte clinique et pour permettre d’améliorer notre compréhension des maladies affectant la moelle épinière. Les applications cliniques requièrent notamment l’amélioration de la robustesse et de l’automatisation des méthodes d’analyse d’images proposées. La caractérisation de la structure interne de la moelle épinière, incluant la matière blanche et la matière grise, présente en effet de grands défis, compte tenu de la qualité et la résolution des images IRM standard acquises en clinique. Les outils développés et validés au cours de ce projet ont un grand potentiel pour la compréhension et la caractérisation des maladies affectant la moelle épinière et aura un impact significatif sur la communauté de la neuroimagerie.----------ABSTRACT The spinal cord plays a fundamental role in the human body, as part of the central nervous system and being the vector between the brain and the peripheral nervous system. Damaging the spinal cord, through traumatic injuries or neurodegenerative diseases, can significantly affect the quality of life of patients. Indeed, spinal cord injuries and diseases can affect the integrity of neurons, and induce neurological impairments and/or functional disabilities. While various treatment procedures exist, assessing the extent of damages and understanding the underlying mechanisms of diseases would improve treatment efficiency and clinical decisions. Over the last decades, magnetic resonance imaging (MRI) has demonstrated a high potential for the diagnosis and prognosis of spinal cord injury and neurodegenerative diseases. Particularly, template-based analysis of brain MRI data has been very helpful for the understanding of neurological diseases, using automated analysis of large groups of patients. However, extracting MRI information within specific regions of the spinal cord with minimum bias and using automated tools is still a challenge. Indeed, only a limited number of MRI template of the spinal cord exists, and none covers the full spinal cord, thereby preventing large multi-centric template-based analysis of the spinal cord. Moreover, no template integrates both the spinal cord and the brain region, thereby preventing simultaneous cerebrospinal studies. The objective of this project was to propose a new MRI template of the full spinal cord, which allows simultaneous brain and spinal cord studies, that integrates atlases of the spinal cord internal structures (e.g., white and gray matter, white matter pathways) and that comes with tools for extracting information within these subregions. More particularly, the general research question of the project was “How to create generic MRI templates of the spinal cord that would enable unbiased and reproducible template-based analysis of spinal cord MRI data?”. Several original contributions have been made to answer this question and to enable template-based analysis of spinal cord MRI data. The first contribution was the development of the Spinal Cord Toolbox (SCT), a comprehensive and open-source software for processing multi-parametric MRI data of the spinal cord (De Leener, Lévy, et al., 2016). SCT includes tools for the automatic segmentation of the spinal cord and its internal structure (white and gray matter), vertebral labeling, registration of multimodal MRI data (structural and non-structural) on a spinal cord MRI template (initially the MNI-Poly-AMU template, later the PAM50 template), co-registration of spinal cord MRI images, as well as the robust extraction of MRI metric within specific regions of the spinal cord (i.e., white and gray matter, white matter tracts, gray matter subregions) and specific vertebral levels using a spinal cord atlas (Lévy et al., 2015). Additional tools include robust motion correction and image processing along the spinal cord. Each tool included in SCT has been validated on a multimodal dataset. The second contribution of this project was the development of a novel registration method dedicated to spinal cord images, with an interest in the straightening of the spinal cord, while preserving its topology (De Leener, Mangeat et al., 2017). This method is based on the global approximation of the spinal cord and the analytical computation of deformation fields perpendicular to the centerline. Validation included calculation of distance measurements after straightening on a population of healthy subjects and patients with spinal cord compression. The major contribution of this project was the development of a framework for generating MRI template of the spinal cord and the PAM50 template, an unbiased and symmetrical MRI template of the brainstem and full spinal cord. Based on 50 healthy subjects, the PAM50 template was generated using an iterative nonlinear registration process, after applying normalization and straightening of all images. Pre-processing included segmentation of the spinal cord, manual delineation of the brainstem anterior edge, detection and identification of intervertebral disks, and normalization of intensity along the spinal cord. Next, the average centerline and vertebral distribution was computed to create an initial straight template space. Then, all images were registered to the initial template space and an iterative nonlinear registration framework was applied to create the final symmetrical template. The PAM50 covers the brainstem and the full spinal cord, from C1 to L2, is available for T1-, T2- and T2*-weighted contrasts, and includes probabilistic maps of the white and the gray matter and atlases of the white matter pathways and gray matter subregions. Additionally, the PAM50 template has been merged with the ICBM152 brain template, thereby allowing for simultaneous cerebrospinal template-based analysis. Finally, several complementary results, focused on clinical validation and applications, are presented. First, a reproducibility and repeatability study of cross-sectional area measurements using SCT (De Leener, Granberg, Fink, Stikov, & Cohen-Adad, 2017) was performed on a Multiple Sclerosis population (n=9). The results demonstrated the high reproducibility and repeatability of SCT and its ability to detect very subtle atrophy of the spinal cord. Second, a novel biomarker of spinal cord injury has been proposed. Based on the T2*-weighted intensity ratio between the white and the gray matter, this new biomarker is computed by registering MRI images with the PAM50 template and extracting metrics using probabilistic atlases. Additionally, the feasibility of extracting multiparametric MRI metrics from subregions of the spinal cord has been demonstrated and the diagnostic potential of this approach has been assessed on a degenerative cervical myelopathy (DCM) population. Finally, a method for extracting shape morphometrics along the spinal cord has been proposed, including spinal cord flattening, indentation and torsion. These metrics demonstrated high capabilities for the diagnostic of asymptomatic spinal cord compression (AUC=99.8% for flattening, 99.3% for indentation, and 98.4% for torsion). The development of the PAM50 template enables unbiased template-based analysis of the spinal cord. However, the PAM50 template has several limitations. Indeed, the proposed template has been generated with multimodal MRI images from 50 healthy and young individuals (age = 27+/- 6.5 y.o.). Therefore, the template is specific to this particular population and could not be directly usable for age- or disease-specific populations. One solution is to open-source the templategeneration code so that research groups can generate and use their own spinal cord MRI template. The code is available on https://github.com/neuropoly/template. While this project introduced a generic referential coordinate system, based on vertebral levels and the pontomedullary junction as origin, one limitation is the choice of this coordinate system. Another coordinate system, based spinal segments would be more suitable for functional analysis. However, the acquisition of MRI images with high enough resolution to delineate the spinal roots is still challenging. Finally, several challenges in the automation of spinal cord MRI processing remains, including the robust detection and identification of vertebral levels, particularly in case of small fields-of-view. This project introduced key developments for the analysis of spinal cord MRI data. Many more developments are still required to bring them into clinics and to improve our understanding of diseases affecting the spinal cord. Indeed, clinical applications require the improvement of the robustness and the automation of the proposed processing and analysis tools. Particularly, the detection and segmentation of spinal cord structures, including vertebral labeling and white/gray matter segmentation, is still challenging, given the lowest quality and resolution of standard clinical MRI acquisition. The tools developed and validated here have the potential to improve our understanding and the characterization of diseases affecting the spinal cord and will have a significant impact on the neuroimaging community