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Dissertation (Ph.D.)--Auburn University, 2006. / Abstract. Vita. Includes bibliographic references.

Methods for measuring deformation and motion of the human heart in-vivo are crucial in the assessment of cardiac function. Applications ranging from basic physiological research, through early detection of disease to follow-up studies, all rely on the quality of the measurements of heart dynamics. This thesis presents new improved magnetic resonance imaging methods for acquisition, image reconstruction and visualization of cardiac motion and deformation.As the heart moves and changes shape during the acquisition, synchronization to the heart dynamics is necessary. Here, a method to resolve not only the cardiac cycle but also the respiratory cycle is presented. Combined with volumetric imaging, this produces a five-dimensional data set with two cyclic temporal dimensions. This type of data reveals unique physiological information, such as interventricular coupling in the heart in different phases of the respiratory cycle.The acquisition can also be sensitized to motion, measuring not only the magnitude of the magnetization but also a signal proportional to local velocity or displacement. This allows for quantification of the motion which is especially suitable for functional study of the cardiac deformation. In this work, an evaluation of the influence of several factors on the signal-to-noise ratio is presented for in-vivo displacement encoded imaging. Additionally, an extension of the method to acquire multiple displacement encoded slices in a single breath hold is also presented.Magnetic resonance imaging is usually associated with long scan times, and many methods exist to shorten the acquisition time while maintaining acceptable image quality. One class of such methods involves acquiring only a sparse subset of k-space. A special reconstruction is then necessary in order to obtain an artifact-free image. One family of these reconstruction techniques tailored for dynamic imaging is the k-t BLAST approach, which incorporates data-driven prior knowledge to suppress aliasing artifacts that otherwise occur with the sparse sampling. In this work, an extension of the original k-t BLAST method to two temporal dimensions is presented and applied to data acquired with full coverage of the cardio-respiratory cycles. Using this technique, termed k-t2 BLAST, simultaneous reduction of scan time and improved spatial resolution is demonstrated. Further, the loss of temporal fidelity when using the k-t BLAST approach is investigated, and an improved reconstruction is proposed for the application of cardiac function analysis.Visualization is a crucial part of the imaging chain. Scalar data, such as regular anatomical images, are straightforward to display. Myocardial strain and strain-rate, however, are tensor quantities which do not lend themselves to direct visualization. The problem of visualizing the tensor field is approached in this work by combining a local visualization that displays all degrees of freedom for a single tensor with an overview visualization using a scalar field representation of the complete tensor field. The scalar field is obtained by iterated adaptive filtering of a noise field, creating a continuous geometrical representation of the myocardial strain-rate tensor field.The results of the work presented in this thesis provide opportunities for improved imaging of myocardial function, in all areas of the imaging chain; acquisition, reconstruction and visualization.

This thesis focuses on the analysis of the cardiac electrical and kinematic function for heart failure patients. An expected outcome is a set of computational tools that may help a clinician in understanding, diagnosing and treating patients suffering from cardiac motion asynchrony, a specific aspect of heart failure. Understanding the inverse electro-kinematic coupling relationship is the main task of this study. With this knowledge, the widely available cardiac image sequences acquired non-invasively at clinics could be used to estimate the cardiac electrophysiology (EP) without having to perform the invasive cardiac EP mapping procedures. To this end, we use real clinical cardiac sequence and a cardiac electromechanical model to create controlled synthetic sequence so as to produce a training set in an attempt to learn the cardiac electro-kinematic relationship. Creating patient-specific database of synthetic sequences allows us to study this relationship using a machine learning approach. A first contribution of this work is a non-linear registration method applied and evaluated on cardiac sequences to estimate the cardiac motion. Second, a new approach in the generation of the synthetic but virtually realistic cardiac sequence which combines a biophysical model and clinical images is developed. Finally, we present the cardiac electrophysiological activation time estimation from medical images using a patient-specific database of synthetic image sequences.

Quantification of cardiac left ventricular (LV) motion from medical images provides a non-invasive method for diagnosing cardiovascular disease (CVD). The proposed study continues our group\'s line of research in quantification of LV motion by applying optical flow (OF) techniques to quantify LV motion in gated Rubidium Chloride-82Rb (82Rb) and Fluorodeoxyglucose-18F (FDG) PET image sequences. The following challenges arise from this work: (i) the motion vector field (MVF) should be made as accurate as possible to maximise sensitivity and specificity; (ii) the MVF is large and composed of 3D vectors in 3D space, making visual extraction of information for medical diagnosis dffcult by human observers. Approaches to improve the accuracy of motion quantification were developed. While the volume of interest is the region of the MVF corresponding to the LV myocardium, non-zero values of motion exist outside this volume due to artefacts in the motion detection method or from neighbouring structures, such as the right ventricle. Improvements in accuracy can be obtained by segmenting the LV and setting the MVF to zero outside the LV. The LV myocardium was automatically segmented in short-axis slices using the Hough circle transform to provide an initialisation to the distance regularised level set evolution algorithm. Our segmentation method attained Dice similarity measure of 93.43% when tested over 395 FDG slices, compared with manual segmentation. Strategies for improving OF performance at motion boundaries were investigated using spatially varying averaging filters, applied to synthetic image sequences. Results showed improvements in motion quantification accuracy using these methods. Kinetic Energy Index (KEf), an indicator of cardiac motility, was used to assess 63 individuals with normal and altered/low cardiac function from a 82Rb PET image database. Sensitivity and specificity tests were performed to evaluate the potential of KEf as a classifier of cardiac function, using LV ejection fraction as gold standard. A receiver operating characteristics curve was constructed, which provided an area under the curve of 0.906. Analysis of LV motion can be simplified by visualisation of directional motion field components, namely radial, rotational (or circumferential) and linear, obtained through automated decomposition. The Discrete Helmholtz Hodge Decomposition (DHHD) was used to generate these components in an automated manner, with a validation performed using synthetic cardiac motion fields from the Extended Cardiac Torso phantom. Finally, the DHHD was applied to OF fields from gated FDG images, allowing an analysis of directional components from an individual with normal cardiac function and a patient with low function and a pacemaker fitted. Motion field quantification from PET images allows the development of new indicators to diagnose CVDs. The ability of these motility indicators depends on the accuracy of the quantification of movement, which in turn can be determined by characteristics of the input images, such as noise. Motion analysis provides a promising and unprecedented approach to the diagnosis of CVDs. / A quantificação do movimento cardíaco do ventrículo esquerdo (VE) a partir de imagens médicas fornece um método não invasivo para o diagnóstico de doenças cardiovasculares (DCV). O estudo aqui proposto continua na mesma linha de pesquisa do nosso grupo sobre quantificação do movimento do VE por meio de técnicas de fluxo óptico (FO), aplicando estes métodos para quantificar o movimento do VE em sequências de imagens associadas às substâncias de cloreto de rubídio-82Rb (82Rb) e fluorodeoxiglucose-18F (FDG) PET. Com a extração dos campos vetoriais surgiram os seguintes desafios: (i) o campo vetorial de movimento (motion vector field, MVF) deve ser feito da forma mais precisa possível para maximizar a sensibilidade e especificidade; (ii) o MVF é extenso e composto de vetores 3D no espaço 3D, dificultando a análise visual de informações por observadores humanos para o diagnóstico médico. Foram desenvolvidas abordagens para melhorar a precisão da quantificação de movimento, considerando que o volume de interesse seja a região do MVF correspondente ao miocárdio do VE, em que valores de movimento não nulos existem fora deste volume devido aos artefatos do método de detecção de movimento ou de estruturas vizinhas, como o ventrículo direito. As melhorias na precisão foram obtidas segmentando o VE e ajustando os valores de MVF para zero fora do VE. O miocárdio VE foi segmentado automaticamente em fatias de eixo curto usando a Transformada de Hough na detecção de círculos para fornecer uma inicialização ao algoritmo de curvas de nível, um tipo de modelo deformável. A segmentação automática do VE atingiu 93,43% de medida de similaridade Dice, quando foi testado em 395 fatias de eixo menor de FDG, comparado com a segmentação manual. Estratégias para melhorar o desempenho do algoritmo OF nas bordas de movimento foram investigadas usando spatially varying averaging filters, aplicados em seqüências de imagens sintéticas. Os resultados mostraram melhorias na precisão de quantificação de movimento utilizando estes métodos. O Índice de Energia Cinética (KEf), um indicador de motilidade cardíaca, foi utilizado para avaliar 63 sujeitos com função cardíaca normal e alterada / baixa de uma base de dados de imagens PET de 82Rb. Foram realizados testes de sensibilidade e especificidade para avaliar o potencial de KEf para classificar a função cardíaca, utilizando a fração de ejeção do VE como padrão ouro. Foi construída uma curva ROC, que proporcionou uma área sob a curva de 0,906. A análise do movimento do VE pode ser simplificada pela visualização de componentes de campo de movimento direcional, ou seja, radial, rotacional (ou circunferencial) e linear, obtidos por decomposição automatizada. A decomposição discreta de Helmholtz Hodge (DHHD) foi utilizada para gerar estes componentes de forma automatizada, com uma validação utilizando campos de movimento cardíaco sintéticos a partir do conjunto Extended Cardiac Torso Phantom. Finalmente, o método DHHD foi aplicado a campos de FO, criado a partir de imagens FDG, permitindo uma análise de componentes direcionais de um indivíduo com função cardíaca normal e um paciente com baixa função e utilizando um marca-passo. A quantificação do campo de movimento a partir de imagens PET possibilita o desenvolvimento de novos indicadores para diagnosticar DCVs. A capacidade destes indicadores de motilidade depende na precisão da quantificação de movimento que, por sua vez, pode ser determinado por características das imagens de entrada como ruído. A análise de movimento fornece um promissor e sem precedente método para o diagnóstico de DCVs.

Quantification of cardiac left ventricular (LV) motion from medical images provides a non-invasive method for diagnosing cardiovascular disease (CVD). The proposed study continues our group\'s line of research in quantification of LV motion by applying optical flow (OF) techniques to quantify LV motion in gated Rubidium Chloride-82Rb (82Rb) and Fluorodeoxyglucose-18F (FDG) PET image sequences. The following challenges arise from this work: (i) the motion vector field (MVF) should be made as accurate as possible to maximise sensitivity and specificity; (ii) the MVF is large and composed of 3D vectors in 3D space, making visual extraction of information for medical diagnosis dffcult by human observers. Approaches to improve the accuracy of motion quantification were developed. While the volume of interest is the region of the MVF corresponding to the LV myocardium, non-zero values of motion exist outside this volume due to artefacts in the motion detection method or from neighbouring structures, such as the right ventricle. Improvements in accuracy can be obtained by segmenting the LV and setting the MVF to zero outside the LV. The LV myocardium was automatically segmented in short-axis slices using the Hough circle transform to provide an initialisation to the distance regularised level set evolution algorithm. Our segmentation method attained Dice similarity measure of 93.43% when tested over 395 FDG slices, compared with manual segmentation. Strategies for improving OF performance at motion boundaries were investigated using spatially varying averaging filters, applied to synthetic image sequences. Results showed improvements in motion quantification accuracy using these methods. Kinetic Energy Index (KEf), an indicator of cardiac motility, was used to assess 63 individuals with normal and altered/low cardiac function from a 82Rb PET image database. Sensitivity and specificity tests were performed to evaluate the potential of KEf as a classifier of cardiac function, using LV ejection fraction as gold standard. A receiver operating characteristics curve was constructed, which provided an area under the curve of 0.906. Analysis of LV motion can be simplified by visualisation of directional motion field components, namely radial, rotational (or circumferential) and linear, obtained through automated decomposition. The Discrete Helmholtz Hodge Decomposition (DHHD) was used to generate these components in an automated manner, with a validation performed using synthetic cardiac motion fields from the Extended Cardiac Torso phantom. Finally, the DHHD was applied to OF fields from gated FDG images, allowing an analysis of directional components from an individual with normal cardiac function and a patient with low function and a pacemaker fitted. Motion field quantification from PET images allows the development of new indicators to diagnose CVDs. The ability of these motility indicators depends on the accuracy of the quantification of movement, which in turn can be determined by characteristics of the input images, such as noise. Motion analysis provides a promising and unprecedented approach to the diagnosis of CVDs. / A quantificação do movimento cardíaco do ventrículo esquerdo (VE) a partir de imagens médicas fornece um método não invasivo para o diagnóstico de doenças cardiovasculares (DCV). O estudo aqui proposto continua na mesma linha de pesquisa do nosso grupo sobre quantificação do movimento do VE por meio de técnicas de fluxo óptico (FO), aplicando estes métodos para quantificar o movimento do VE em sequências de imagens associadas às substâncias de cloreto de rubídio-82Rb (82Rb) e fluorodeoxiglucose-18F (FDG) PET. Com a extração dos campos vetoriais surgiram os seguintes desafios: (i) o campo vetorial de movimento (motion vector field, MVF) deve ser feito da forma mais precisa possível para maximizar a sensibilidade e especificidade; (ii) o MVF é extenso e composto de vetores 3D no espaço 3D, dificultando a análise visual de informações por observadores humanos para o diagnóstico médico. Foram desenvolvidas abordagens para melhorar a precisão da quantificação de movimento, considerando que o volume de interesse seja a região do MVF correspondente ao miocárdio do VE, em que valores de movimento não nulos existem fora deste volume devido aos artefatos do método de detecção de movimento ou de estruturas vizinhas, como o ventrículo direito. As melhorias na precisão foram obtidas segmentando o VE e ajustando os valores de MVF para zero fora do VE. O miocárdio VE foi segmentado automaticamente em fatias de eixo curto usando a Transformada de Hough na detecção de círculos para fornecer uma inicialização ao algoritmo de curvas de nível, um tipo de modelo deformável. A segmentação automática do VE atingiu 93,43% de medida de similaridade Dice, quando foi testado em 395 fatias de eixo menor de FDG, comparado com a segmentação manual. Estratégias para melhorar o desempenho do algoritmo OF nas bordas de movimento foram investigadas usando spatially varying averaging filters, aplicados em seqüências de imagens sintéticas. Os resultados mostraram melhorias na precisão de quantificação de movimento utilizando estes métodos. O Índice de Energia Cinética (KEf), um indicador de motilidade cardíaca, foi utilizado para avaliar 63 sujeitos com função cardíaca normal e alterada / baixa de uma base de dados de imagens PET de 82Rb. Foram realizados testes de sensibilidade e especificidade para avaliar o potencial de KEf para classificar a função cardíaca, utilizando a fração de ejeção do VE como padrão ouro. Foi construída uma curva ROC, que proporcionou uma área sob a curva de 0,906. A análise do movimento do VE pode ser simplificada pela visualização de componentes de campo de movimento direcional, ou seja, radial, rotacional (ou circunferencial) e linear, obtidos por decomposição automatizada. A decomposição discreta de Helmholtz Hodge (DHHD) foi utilizada para gerar estes componentes de forma automatizada, com uma validação utilizando campos de movimento cardíaco sintéticos a partir do conjunto Extended Cardiac Torso Phantom. Finalmente, o método DHHD foi aplicado a campos de FO, criado a partir de imagens FDG, permitindo uma análise de componentes direcionais de um indivíduo com função cardíaca normal e um paciente com baixa função e utilizando um marca-passo. A quantificação do campo de movimento a partir de imagens PET possibilita o desenvolvimento de novos indicadores para diagnosticar DCVs. A capacidade destes indicadores de motilidade depende na precisão da quantificação de movimento que, por sua vez, pode ser determinado por características das imagens de entrada como ruído. A análise de movimento fornece um promissor e sem precedente método para o diagnóstico de DCVs.

Le travail de thèse porte sur l'analyse de la fonction électrique et mécanique du cœur afin d'étudier les effets de l'insuffisance cardiaque. Il débouche sur un ensemble d'outils qui peuvent aider le clinicien à mieux comprendre et traiter l'asynchronisme cardiaque, un des aspects de l'insuffisance cardiaque. Il a pour principal objectif de résoudre le problème inverse du couplage électro-cinématique : estimer l'électrophysiologie cardiaque sans avoir à effectuer des procédures invasives de cartographie cardiaque. Les séquences cardiaques acquises de manière non-invasive sont déjà largement utilisées dans les centres cliniques et pourraient permettre de caractériser l'électrophysiologie cardiaque sans procédure invasive. La première contribution de ce travail est l'évaluation d'une méthode de recalage non-linéaire appliquée sur des séquences cardiaques pour l'estimation du mouvement. La deuxième est une nouvelle approche de simulation de séquences synthétiques d'images cardiaque. Nous utilisons des séquences réelles et un modèle électromécanique du cœur pour créer des séquences synthétiques contrôlées. Le réalisme des séquences générées repose sur l'utilisation conjointe d'un modèle biophysique et d'images réelles lors de la simulation. Enfin, la troisième contribution concerne une méthode d'estimation de la carte d'activation électrique du cœur à partir d'images médicales. Pour ce faire, nous utilisons une base de données d'images synthétiques cardiaques personnalisée à chaque patient. Ces images et les cartes d'activation électrique utilisées lors de la simulation fournissent une base d'entrainement pour apprendre la relation électro-cinématique du cœur. / This thesis focuses on the analysis of the cardiac electrical and kinematic function for heart failure patients. An expected outcome is a set of computational tools that may help a clinician in understanding, diagnosing and treating patients suffering from cardiac motion asynchrony, a specific aspect of heart failure. Understanding the inverse electro-kinematic coupling relationship is the main task of this study. With this knowledge, the widely available cardiac image sequences acquired non-invasively at clinics could be used to estimate the cardiac electrophysiology (EP) without having to perform the invasive cardiac EP mapping procedures. To this end, we use real clinical cardiac sequence and a cardiac electromechanical model to create controlled synthetic sequence so as to produce a training set in an attempt to learn the cardiac electro-kinematic relationship. Creating patient-specific database of synthetic sequences allows us to study this relationship using a machine learning approach. A first contribution of this work is a non-linear registration method applied and evaluated on cardiac sequences to estimate the cardiac motion. Second, a new approach in the generation of the synthetic but virtually realistic cardiac sequence which combines a biophysical model and clinical images is developed. Finally, we present the cardiac electrophysiological activation time estimation from medical images using a patient-specific database of synthetic image sequences.

Dans cette étude, nous abordons l'utilisation de la représentation parcimonieuse et l'apprentissage de dictionnaires pour l'aide au diagnostic dans le contexte de Maladies Cardiovasculaires. Spécifiquement, notre travail se concentre : 1) sur l'évaluation du mouvement des parois du Ventricule Gauche (VG) chez des patients souffrant d'Insuffisance Cardiaque (IC) ; 2) la détection de fibrose chez des patients présentant une Cardiomyopathie Hypertrophique (CMH). Ces types de pathologies sont étudiées par ailleurs en Imagerie par Résonance Magnétique Cardiaque (IRMC).Dans le contexte de l'IC notre contribution porte sur l'évaluation de mouvement du VG dans des séquences cine-IRMC. Nous proposons dans un premier temps, une méthode d'extraction de caractéristiques qui exploite les informations partielles obtenues à partir de toutes les phases cardiaques temporelles et des segments anatomiques, dans une représentation spatio-temporelle en cine-IRM petit axe (SAX). Les représentations proposées exploitent les informations du mouvement des parois du VG sans avoir recours à la segmentation et disposent des informations discriminatoires qui pourraient contribuer à la détection et à la caractérisation de l'asynchronisme cardiaque. L'extraction d'images spatio-temporelles a été proposée permettant la construction de trois nouveaux types de représentations : 1) profils spatio-temporels diamétraux qui montrent l'évolution temporelle de l’épicarde et de l'endocarde en même temps dans deux segments anatomiques opposés du VG, 2) profils spatio-temporels radiaux où le mouvement pariétal est observé pour chaque segment de la cavité du VG et 3) courbes de signal temps-intensité directement des profils spatio-temporels radiaux dans chaque segment anatomique. Des paramètres différents sont alors définis de ces courbes qui reflètent les informations dynamiques de la contraction du VG. Deuxièmement, nous proposons l'utilisation de ces caractéristiques comme des atomes d'entrée dans l'apprentissage de dictionnaires discriminatoires pour classifier le mouvement régional du VG dans les cas normaux ou anormaux. Nous avons proposé une évaluation globale en utilisant le statut global du sujet : Normal/Pathologique, comme l'étiquette de référence des profils spatio-temporels et une évaluation locale en utilisant les informations de déformation locales fournies par l'analyse des images échographiques de référence en clinique (2D-STE). Dans le contexte de la CMH, nous abordons le problème de détection de la fibrose en LGE-IRM-SAX en utilisant une approche de partitionnement de donnés et d'apprentissage de dictionnaires. Dans ce cadre, les caractéristiques extraites d'images de LGE-SAX sont prises comme des atomes d'entrée pour former un classifieur basé sur les codes parcimonieux obtenus avec une approche d'apprentissage de dictionnaires. Une étape de post-traitement permet la délimitation du myocarde et la localisation spatiale de la fibrose par segment anatomique. / This work concerns the use of sparse representation and Dictionary Learning (DL) in order to get insights about the diseased heart in the context of Cardiovascular Diseases (CVDs). Specifically, this work focuses on 1) assessment of Left Ventricle (LV) wall motion in patients with heart failure and 2) fibrosis detection in patients with hypertrophic cardiomyopathy (HCM). In the context of heart failure (HF) patients, the work focuses on LV wall motion analysis in cardiac cine-MRI. The first contribution in this topic is a feature extraction method that exploits the partial information obtained from all temporal cardiac phases and anatomical segments in a spatio-temporal representation from sequences cine-MRI in short-axis view. These features correspond to spatio-temporal profiles in different anatomical segments of the LV. The proposed representations exploit information of the LV wall motion without segmentation needs. Three representations are proposed : 1) diametrical spatio-temporal profiles where radial motions of LV’s walls are observed at the same time in opposite anatomical segments 2) radial spatiotemporal profiles where motion of LV’s walls is observed for each segment of the LV cavity and 3) quantitative parameters extracted from the radial spatio-temporal profiles. A second contribution involves the use of these features as input atoms in the training of discriminative dictionaries to classify normal or abnormal regional LV motion. We propose two levels of evaluation, a first one where the global status of the subject (normal/pathologic) is used as ground truth to label the proposed spatio-temporal representations, and a second one where local strain information obtained from 2D Speckle Tracking Echocardiography (STE), is taken as ground truth to label the proposed features, where a profile is classified as normal or abnormal (akinetic or hypokinetic cases). In the context of Hypertrophic cardiomyopathy (HCM), we address the problem of fibrosis detection in Late Gadolinium Enhanced LGE-Short axis (SAX) images by using a sparse-based clustering approach and DL. In this framework, random image patches are taken as input atoms in order to train a classifier based on the sparse coefficients obtained with a DL approach based on kernels. For a new test LG-SAX image, the label of each pixel is predicted by using the trained classifier allowing the detection of fibrosis. A subsequent postprocessing step allows the spatial localization of fibrosis that is represented according to the American Heart Association (AHA) 17-segment model and a quantification of fibrosis in the LV myocardium.

Cette thèse porte sur un problème de calibration d'un modèle électromécanique de cœur, personnalisé à partir de données d'imagerie médicale 3D+t ; et sur celui - en amont - de suivi du mouvement cardiaque. A cette fin, nous adoptons une méthodologie fondée sur l'apprentissage statistique. Pour la calibration du modèle mécanique, nous introduisons une méthode efficace mêlant apprentissage automatique et une description statistique originale du mouvement cardiaque utilisant la représentation des courants 3D+t. Notre approche repose sur la construction d'un modèle statistique réduit reliant l'espace des paramètres mécaniques à celui du mouvement cardiaque. L'extraction du mouvement à partir d'images médicales avec quantification d'incertitude apparaît essentielle pour cette calibration, et constitue l'objet de la seconde partie de cette thèse. Plus généralement, nous développons un modèle bayésien parcimonieux pour le problème de recalage d'images médicales. Notre contribution est triple et porte sur un modèle étendu de similarité entre images, sur l'ajustement automatique des paramètres du recalage et sur la quantification de l'incertitude. Nous proposons une technique rapide d'inférence gloutonne, applicable à des données cliniques 4D. Enfin, nous nous intéressons de plus près à la qualité des estimations d'incertitude fournies par le modèle. Nous comparons les prédictions du schéma d'inférence gloutonne avec celles données par une procédure d'inférence fidèle au modèle, que nous développons sur la base de techniques MCMC. Nous approfondissons les propriétés théoriques et empiriques du modèle bayésien parcimonieux et des deux schémas d'inférence / This thesis focuses on the calibration of an electromechanical model of the heart from patient-specific, image-based data; and on the related task of extracting the cardiac motion from 4D images. Long-term perspectives for personalized computer simulation of the cardiac function include aid to the diagnosis, aid to the planning of therapy and prevention of risks. To this end, we explore tools and possibilities offered by statistical learning. To personalize cardiac mechanics, we introduce an efficient framework coupling machine learning and an original statistical representation of shape & motion based on 3D+t currents. The method relies on a reduced mapping between the space of mechanical parameters and the space of cardiac motion. The second focus of the thesis is on cardiac motion tracking, a key processing step in the calibration pipeline, with an emphasis on quantification of uncertainty. We develop a generic sparse Bayesian model of image registration with three main contributions: an extended image similarity term, the automated tuning of registration parameters and uncertainty quantification. We propose an approximate inference scheme that is tractable on 4D clinical data. Finally, we wish to evaluate the quality of uncertainty estimates returned by the approximate inference scheme. We compare the predictions of the approximate scheme with those of an inference scheme developed on the grounds of reversible jump MCMC. We provide more insight into the theoretical properties of the sparse structured Bayesian model and into the empirical behaviour of both inference schemes