Left atrial trajectory impairment in hypertrophic cardiomyopathy disclosed by geometric morphometrics and parallel transport

The analysis of full Left Atrium (LA) deformation and whole LA deformational trajectory in time has been poorly investigated and, to the best of our knowledge, seldom discussed in patients with Hypertrophic Cardiomyopathy. Therefore, we considered 22 patients with Hypertrophic Cardiomyopathy (HCM) and 46 healthy subjects, investigated them by three-dimensional Speckle Tracking Echocardiography, and studied the derived landmark clouds via Geometric Morphometrics with Parallel Transport. Trajectory shape and trajectory size were different in Controls versus HCM and their classification powers had high AUC (Area Under the Receiving Operator Characteristic Curve) and accuracy. The two trajectories were much different at the transition between LA conduit and booster pump functions. Full shape and deformation analyses with trajectory analysis enabled a straightforward perception of pathophysiological consequences of HCM condition on LA functioning. It might be worthwhile to apply these techniques to look for novel pathophysiological approaches that may better define atrio-ventricular interaction

Atlas-Based Quantification of Cardiac Remodeling Due to Myocardial Infarction

Myocardial infarction leads to changes in the geometry (remodeling) of the left ventricle (LV) of the heart. The degree and type of remodeling provides important diagnostic information for the therapeutic management of ischemic heart disease. In this paper, we present a novel analysis framework for characterizing remodeling after myocardial infarction, using LV shape descriptors derived from atlas-based shape models. Cardiac magnetic resonance images from 300 patients with myocardial infarction and 1991 asymptomatic volunteers were obtained from the Cardiac Atlas Project. Finite element models were customized to the spatio-temporal shape and function of each case using guide-point modeling. Principal component analysis was applied to the shape models to derive modes of shape variation across all cases. A logistic regression analysis was performed to determine the modes of shape variation most associated with myocardial infarction. Goodness of fit results obtained from end-diastolic and end-systolic shapes were compared against the traditional clinical indices of remodeling: end-diastolic volume, end-systolic volume and LV mass. The combination of end-diastolic and endsystolic shape parameter analysis achieved the lowest deviance, Akaike information criterion and Bayesian information criterion, and the highest area under the receiver operating characteristic curve. Therefore, our framework quantitatively characterized remodeling features associated with myocardial infarction, better than current measures. These features enable quantification of the amount of remodeling, the progression of disease over time, and the effect of treatments designed to reverse remodeling effects

Apprentissage automatique pour simplifier l’utilisation de banques d’images cardiaques

The recent growth of data in cardiac databases has been phenomenal. Cleveruse of these databases could help find supporting evidence for better diagnosis and treatment planning. In addition to the challenges inherent to the large quantity of data, the databases are difficult to use in their current state. Data coming from multiple sources are often unstructured, the image content is variable and the metadata are not standardised. The objective of this thesis is therefore to simplify the use of large databases for cardiology specialists withautomated image processing, analysis and interpretation tools. The proposed tools are largely based on supervised machine learning techniques, i.e. algorithms which can learn from large quantities of cardiac images with groundtruth annotations and which automatically find the best representations. First, the inconsistent metadata are cleaned, interpretation and visualisation of images is improved by automatically recognising commonly used cardiac magnetic resonance imaging views from image content. The method is based on decision forests and convolutional neural networks trained on a large image dataset. Second, the thesis explores ways to use machine learning for extraction of relevant clinical measures (e.g. volumes and masses) from3D and 3D+t cardiac images. New spatio-temporal image features are designed andclassification forests are trained to learn how to automatically segment the main cardiac structures (left ventricle and left atrium) from voxel-wise label maps. Third, a web interface is designed to collect pairwise image comparisons and to learn how to describe the hearts with semantic attributes (e.g. dilation, kineticity). In the last part of the thesis, a forest-based machinelearning technique is used to map cardiac images to establish distances and neighborhoods between images. One application is retrieval of the most similar images.L'explosion récente de données d'imagerie cardiaque a été phénoménale. L'utilisation intelligente des grandes bases de données annotées pourrait constituer une aide précieuse au diagnostic et à la planification de thérapie. En plus des défis inhérents à la grande taille de ces banques de données, elles sont difficilement utilisables en l'état. Les données ne sont pas structurées, le contenu des images est variable et mal indexé, et les métadonnées ne sont pas standardisées. L'objectif de cette thèse est donc le traitement, l'analyse et l'interprétation automatique de ces bases de données afin de faciliter leur utilisation par les spécialistes de cardiologie. Dans ce but, la thèse explore les outils d'apprentissage automatique supervisé, ce qui aide à exploiter ces grandes quantités d'images cardiaques et trouver de meilleures représentations. Tout d'abord, la visualisation et l'interprétation d'images est améliorée en développant une méthode de reconnaissance automatique des plans d'acquisition couramment utilisés en imagerie cardiaque. La méthode se base sur l'apprentissage par forêts aléatoires et par réseaux de neurones à convolution, en utilisant des larges banques d'images, où des types de vues cardiaques sont préalablement établies. La thèse s'attache dans un deuxième temps au traitement automatique des images cardiaques, avec en perspective l'extraction d'indices cliniques pertinents. La segmentation des structures cardiaques est une étape clé de ce processus. A cet effet une méthode basée sur les forêts aléatoires qui exploite des attributs spatio-temporels originaux pour la segmentation automatique dans des images 3Det 3D+t est proposée. En troisième partie, l'apprentissage supervisé de sémantique cardiaque est enrichi grâce à une méthode de collecte en ligne d'annotations d'usagers. Enfin, la dernière partie utilise l'apprentissage automatique basé sur les forêts aléatoires pour cartographier des banques d'images cardiaques, tout en établissant les notions de distance et de voisinage d'images. Une application est proposée afin de retrouver dans une banque de données, les images les plus similaires à celle d'un nouveau patient

Early Detection of Doxorubicin-Induced Cardiotoxicity Using Combined Biomechanical Modeling and Multi-Parametric Cardiovascular MRI

RÉSUMÉ La chimiothérapie à la doxorubicine est efficace et est largement utilisée pour traiter la leucémie lymphoblastique aiguë. Toutefois, son efficacité est entravée par un large spectre de cardiotoxicités incluant des changements affectant à la fois la morphologie et la fonction du myocarde. Ces changements dépendent principalement de la dose cumulée administrée au patient. Actuellement, très peu de techniques sont disponibles pour détecter de telles cardiotoxicités. L'utilisation d’images de fibres musculaires (par exemple, à l’aide de l’imagerie des tenseurs de diffusion par IRM) ou des techniques d'imagerie 3D (par exemple, ciné DENSE IRM) sont des alternatives prometteuses, cependant, leur application en clinique est limitée en raison du temps d'acquisition d’images et les erreurs d'estimation qui en résultent. En revanche, l'utilisation de l'IRM multi-paramétrique ainsi que le ciné IRM sont des alternatives prometteuses, puisque ces techniques sont déjà disponibles au niveau clinique. L’IRM multiparamétrique incluant l’imagerie des temps de relaxation T1 et T2 peut être utile dans la détection des lésions dans le tissu du myocarde alors que l’imagerie ciné IRM peut être plus appropriée pour détecter les changements fonctionnels au sein du myocarde. La combinaison de ces deux techniques peut également permettre une caractérisation complète de la fonction du tissu myocardique. Dans ce projet, l'utilisation des temps de relaxation T1 pré- et post-gadolinium et T2 est d'abord évaluée et proposée pour détecter les dommages myocardiques induits par la chimiothérapie à la doxorubicine. En second lieu, l'utilisation de patrons 2D de déplacements myocardiques est évaluée dans le cadre de la détection des dommages myocardiques et altération fonctionnelle due au traitement à la doxorubicine. Enfin, l'utilisation de la modélisation par éléments finis, incluant les contraintes et déformations mécaniques est proposée pour évaluer les changements dans les propriétés mécaniques au niveau du myocarde, avec l’hypothèse que le traitement à base de doxorubicine induit des changements importants à la fois dans le tissu et au niveau de la fonction myocardique. Dans notre cohorte de survivants de cancer, des changements myocardiques locaux ont été trouvés entre le groupe à risque standard et le groupe à risque élevé lorsque le T1 pré-gadolinium fut utilisé. Ces changements ont été amplifiés avec l’utilisation d’agent de contraste tel que confirmé par le coefficient de partition, ce qui suggère que l’utilisation du T1 post-gadolonium et le coefficient de----------ABSTRACT Doxorubicin chemotherapy is effective and widely used to treat acute lymphoblastic leukemia. However, its effectiveness is hampered by a wide spectrum of dose-dependent cardiotoxicity including both morphological and functional changes affecting the myocardium. Currently, very few techniques are available for detecting such cardiotoxic effect. The use of muscle fibers orientation (e.g., diffusion tensor imaging DT-MRI) or 3D imaging techniques (e.g., cine DENSE MRI) are possible alternatives, however, their clinical application is limited due to the acquisition time and their estimation errors. In contrast, the use of multi-parametric MRI along with cine MRI is a promising alternative, since theses techniques are already available at a clinical level. Multiparametric MRI including T1 and T2 imaging may be helpful in detecting myocardial tissue damage, while cine MRI may be more appropriate to detect functional changes within the myocardium. The combination of these two techniques may further allow an extensive characterization of myocardial tissue function. In this doctoral project, the use of pre- and post-gadolinium T1 and T2 relaxation times is firstly assessed and proposed to detect myocardial damage induced by doxorubicin chemotherapy. Secondly, the use of 2D myocardial displacement patterns is assessed in detecting myocardial damage and functional alteration due to doxorubicin-based treatment. Finally, the use of finite element modeling including mechanical strains and stresses to evaluate mechanical properties changes within the myocardium is alternatively proposed, assuming that doxorubicin-based treatment induces significant changes to both myocardial tissue morphology and function. In our cohort of cancer survivors, local myocardial changes were found between standard risk and high risks group using pre-gadolinium T1 relaxation times. These changes were further amplified with gadolinium enhancement, as confirmed by the use of partition coefficient, suggesting this MRI parameter along with partition coefficient as candidates imaging markers of doxorubicin induced cardiomyopathy. The use of T2 on the other hand showed that the high risk group of cancer survivors had higher T2 relaxation times compared to the standard risk group and similar to reported values. Though, a larger cohort of cancer survivors may be required to assess the use of T1 and T2 relaxation time as possible indices for myocardial tissue damage in the onset of doxorubicin-induced cardiotoxicity

Novel cardiovascular magnetic resonance phenotyping of the myocardium

INTRODUCTION Left ventricular (LV) microstructure is unique, composed of a winding helical pattern of myocytes and rotating aggregations of myocytes called sheetlets. Hypertrophic cardiomyopathy (HCM) is a cardiovascular disease characterised by left ventricular hypertrophy (LVH), however the link between LVH and underlying microstructural aberration is poorly understood. In vivo cardiovascular diffusion tensor imaging (cDTI) is a novel cardiovascular MRI (CMR) technique, capable of characterising LV microstructural dynamics non-invasively. In vivo cDTI may therefore improve our understanding microstructural-functional relationships in health and disease. METHODS AND RESULTS The monopolar diffusion weighted stimulated echo acquisition mode (DW-STEAM) sequence was evaluated for in vivo cDTI acquisitions at 3Tesla, in healthy volunteers (HV), patients with hypertensive LVH, and HCM patients. Results were contextualised in relation to extensively explored technical limitations. cDTI parameters demonstrated good intra-centre reproducibility in HCM, and good inter-centre reproducibility in HV. In all subjects, cDTI was able to depict the winding helical pattern of myocyte orientation known from histology, and the transmural rate of change in myocyte orientation was dependent on LV size and thickness. In HV, comparison of cDTI parameters between systole and diastole revealed an increase in transmural gradient, combined with a significant re-orientation of sheetlet angle. In contrast, in HCM, myocyte gradient increased between phases, however sheetlet angulation retained a systolic-like orientation in both phases. Combined analysis with hypertensive patients revealed a proportional decrease in sheetlet mobility with increasing LVH. CONCLUSION In vivo DW-STEAM cDTI can characterise LV microstructural dynamics non-invasively. The transmural rate of change in myocyte angulation is dependent on LV size and wall thickness, however inter phase changes in myocyte orientation are unaffected by LVH. In contrast, sheetlet dynamics demonstrate increasing dysfunction, in proportion to the degree of LVH. Resolving technical limitations is key to advancing this technique, and improving the understanding of the role of microstructural abnormalities in cardiovascular disease expression.Open Acces

2014 Touro College & University System Faculty Publications

This is the 2014 edition of the Faculty Publications Book of the Touro College & University System. It includes all eligible 2014 publication citations of faculty within the Touro College & University System, including New York Medical College (NYMC). It was produced as a joint effort of the Touro College Libraries and the Health Sciences Library at NYMC.https://touroscholar.touro.edu/facpubs/1002/thumbnail.jp

Molecular Imaging

The present book gives an exceptional overview of molecular imaging. Practical approach represents the red thread through the whole book, covering at the same time detailed background information that goes very deep into molecular as well as cellular level. Ideas how molecular imaging will develop in the near future present a special delicacy. This should be of special interest as the contributors are members of leading research groups from all over the world