Traitement et exploration d'images TDM pour l'évaluation des bioprothèses valvulaires aortiques

Le but de cette étude est d évaluer la faisabilité de l analyse tomodensitométrique 3D des bioprothèses aortiques pour faciliter leur évaluation morphologique durant le suivi et d aider la sélection de cas et améliorer la planification d une procédure valvein-valve. Le challenge était représenté par le rehaussement des feuillets valvulaires, en raison d images très bruitées. Un angio-scanner synchronisé était réalisé chez des patients porteurs d une bioprotèses aortique dégénérée avant réintervention (images in-vivo). Différentes méthodes pour la réduction du bruit étaient proposées. La reconstruction tridimensionnelle des bioprothèses était réalisée en utilisant des méthodes de segmentation de régions par "sticks". Après réopération ces méthodes étaient appliquées aux images scanner des bioprothèses explantées (images ex-vivo) et utilisées comme référence. La réduction du bruit obtenue par le filtre stick modifié montrait meilleurs résultats en rapport signal/bruit en comparaison aux filtres de diffusion anisotropique. Toutes les méthodes de segmentation ont permis une reconstruction 3D des feuillets. L analyse qualitative a montré une bonne concordance entre les images obtenues in-vivo et les altérations des bioprothèses. Les résultats des différentes méthodes étaient comparés par critères volumétriques et discutés. Les bases d'une première approche de visualisation spatio-temporelle d'images TDM 3D+T de la prothèse valvulaire ont été proposés. Elle implique des techniques de rendu volumique et de compensation de mouvement. Son application à la valve native a aussi été envisagée. Les images scanner des bioprothèses aortiques nécessitent un traitement de débruitage et de réduction des artéfacts de façon à permettre le rehaussement des feuillets prothétiques. Les méthodes basées sticks semblent constituer une approche pertinente pour caractériser morphologiquement la dégénérescence des bioprothèses.The aim of the study was to assess the feasibility of CT based 3D analysis of degenerated aortic bioprostheses to make easier their morphological assessment. This could be helpful during regular follow-up and for case selection, improved planning and mapping of valve-in-valve procedure. The challenge was represented by leaflets enhancement because of highly noised CT images. Contrast-enhanced ECG-gated CT scan was performed in patients with degenerated aortic bioprostheses before reoperation (in-vivo images). Different methods for noise reduction were tested and proposed. 3D reconstruction of bioprostheses components was achieved using stick based region segmentation methods. After reoperation, segmentation methods were applied to CT images of the explanted prostheses (exvivo images). Noise reduction obtained by improved stick filter showed best results in terms of signal to noise ratio comparing to anisotropic diffusion filters. All segmentation methods applied to the best phase of in-vivo images allowed 3D bioprosthetic leaflets reconstruction. Explanted bioprostheses CT images were also processed and used as reference. Qualitative analysis revealed a good concordance between the in-vivo images and the bioprostheses alterations. Results from different methods were compared by means of volumetric criteria and discussed. A first approach for spatiotemporal visualization of 3D+T images of valve bioprosthesis has been proposed. Volume rendering and motion compensation techniques were applied to visualize different phases of CT data. Native valve was also considered. ECG-gated CT images of aortic bioprostheses need a preprocessing to reduce noise and artifacts in order to enhance prosthetic leaflets. Stick based methods seems to provide an interesting approach for the morphological characterization of degenerated bioprostheses.RENNES1-Bibl. électronique (352382106) / SudocSudocFranceF

Computational Methods for Segmentation of Multi-Modal Multi-Dimensional Cardiac Images

Segmentation of the heart structures helps compute the cardiac contractile function quantified via the systolic and diastolic volumes, ejection fraction, and myocardial mass, representing a reliable diagnostic value. Similarly, quantification of the myocardial mechanics throughout the cardiac cycle, analysis of the activation patterns in the heart via electrocardiography (ECG) signals, serve as good cardiac diagnosis indicators. Furthermore, high quality anatomical models of the heart can be used in planning and guidance of minimally invasive interventions under the assistance of image guidance. The most crucial step for the above mentioned applications is to segment the ventricles and myocardium from the acquired cardiac image data. Although the manual delineation of the heart structures is deemed as the gold-standard approach, it requires significant time and effort, and is highly susceptible to inter- and intra-observer variability. These limitations suggest a need for fast, robust, and accurate semi- or fully-automatic segmentation algorithms. However, the complex motion and anatomy of the heart, indistinct borders due to blood flow, the presence of trabeculations, intensity inhomogeneity, and various other imaging artifacts, makes the segmentation task challenging. In this work, we present and evaluate segmentation algorithms for multi-modal, multi-dimensional cardiac image datasets. Firstly, we segment the left ventricle (LV) blood-pool from a tri-plane 2D+time trans-esophageal (TEE) ultrasound acquisition using local phase based filtering and graph-cut technique, propagate the segmentation throughout the cardiac cycle using non-rigid registration-based motion extraction, and reconstruct the 3D LV geometry. Secondly, we segment the LV blood-pool and myocardium from an open-source 4D cardiac cine Magnetic Resonance Imaging (MRI) dataset by incorporating average atlas based shape constraint into the graph-cut framework and iterative segmentation refinement. The developed fast and robust framework is further extended to perform right ventricle (RV) blood-pool segmentation from a different open-source 4D cardiac cine MRI dataset. Next, we employ convolutional neural network based multi-task learning framework to segment the myocardium and regress its area, simultaneously, and show that segmentation based computation of the myocardial area is significantly better than that regressed directly from the network, while also being more interpretable. Finally, we impose a weak shape constraint via multi-task learning framework in a fully convolutional network and show improved segmentation performance for LV, RV and myocardium across healthy and pathological cases, as well as, in the challenging apical and basal slices in two open-source 4D cardiac cine MRI datasets. We demonstrate the accuracy and robustness of the proposed segmentation methods by comparing the obtained results against the provided gold-standard manual segmentations, as well as with other competing segmentation methods

Analysis of Blood Flow in Patient-specific Models of Type B Aortic Dissection

Aortic dissection is the most common acute catastrophic event affecting the aorta. The majority of patients presenting with an uncomplicated type B dissection are treated medically, but 25% of these patients develop subsequent dilatation and aortic aneurysm formation. The reasons behind the long‐term outcomes of type B aortic dissection are poorly understood. As haemodynamic factors have been involved in the development and progression of a variety of cardiovascular diseases, the flow phenomena and environment in patient‐specific models of type B aortic dissection have been studied in this thesis by applying computational fluid dynamics (CFD) to in vivo data. The present study aims to gain more detailed knowledge of the links between morphology, flow characteristics and clinical outcomes in type B dissection patients. The thesis includes two parts of patient‐specific study: a multiple case cross‐sectional study and a single case longitudinal study. The multiple cases study involved a group of ten patients with classic type B aortic dissection with a focus on examining the flow characteristics as well as the role of morphological factors in determining the flow patterns and haemodynamic parameters. The single case study was based on a series of follow‐up scans of a patient who has a stable dissection, with an aim to identify the specified haemodynamic factors that are associated with the progression of aortic dissection. Both studies were carried out based on computed tomography images acquired from the patients. 4D Phase‐contrast magnetic resonance imaging was performed on a typical type B aortic dissection patient to provide detailed flow data for validation purpose. This was achieved by qualitative and quantitative comparisons of velocity‐encoded images with simulation results of the CFD model. The analysis of simulation results, including velocity, wall shear stress and turbulence intensity profiles, demonstrates certain correlations between the morphological features and haemodynamic factors, and also their effects on long‐term outcomes of type B aortic dissections. The simulation results were in good agreement with in vivo MR flow data in the patient‐specific validation case, giving credence to the application of the computational model to the study of flow conditions in aortic dissection. This study made an important contribution by identifying the role of certain morphological and haemodynamic factors in the development of type B aortic dissection, which may help provide a better guideline to assist surgeons in choosing optimal treatment protocol for individual patient

Development of a Surgical Assistance System for Guiding Transcatheter Aortic Valve Implantation

Development of image-guided interventional systems is growing up rapidly in the recent years. These new systems become an essential part of the modern minimally invasive surgical procedures, especially for the cardiac surgery. Transcatheter aortic valve implantation (TAVI) is a recently developed surgical technique to treat severe aortic valve stenosis in elderly and high-risk patients. The placement of stented aortic valve prosthesis is crucial and typically performed under live 2D fluoroscopy guidance. To assist the placement of the prosthesis during the surgical procedure, a new fluoroscopy-based TAVI assistance system has been developed. The developed assistance system integrates a 3D geometrical aortic mesh model and anatomical valve landmarks with live 2D fluoroscopic images. The 3D aortic mesh model and landmarks are reconstructed from interventional angiographic and fluoroscopic C-arm CT system, and a target area of valve implantation is automatically estimated using these aortic mesh models. Based on template-based tracking approach, the overlay of visualized 3D aortic mesh model, landmarks and target area of implantation onto fluoroscopic images is updated by approximating the aortic root motion from a pigtail catheter motion without contrast agent. A rigid intensity-based registration method is also used to track continuously the aortic root motion in the presence of contrast agent. Moreover, the aortic valve prosthesis is tracked in fluoroscopic images to guide the surgeon to perform the appropriate placement of prosthesis into the estimated target area of implantation. An interactive graphical user interface for the surgeon is developed to initialize the system algorithms, control the visualization view of the guidance results, and correct manually overlay errors if needed. Retrospective experiments were carried out on several patient datasets from the clinical routine of the TAVI in a hybrid operating room. The maximum displacement errors were small for both the dynamic overlay of aortic mesh models and tracking the prosthesis, and within the clinically accepted ranges. High success rates of the developed assistance system were obtained for all tested patient datasets. The results show that the developed surgical assistance system provides a helpful tool for the surgeon by automatically defining the desired placement position of the prosthesis during the surgical procedure of the TAVI.Die Entwicklung bildgeführter interventioneller Systeme wächst rasant in den letzten Jahren. Diese neuen Systeme werden zunehmend ein wesentlicher Bestandteil der technischen Ausstattung bei modernen minimal-invasiven chirurgischen Eingriffen. Diese Entwicklung gilt besonders für die Herzchirurgie. Transkatheter Aortenklappen-Implantation (TAKI) ist eine neue entwickelte Operationstechnik zur Behandlung der schweren Aortenklappen-Stenose bei alten und Hochrisiko-Patienten. Die Platzierung der Aortenklappenprothese ist entscheidend und wird in der Regel unter live-2D-fluoroskopischen Bildgebung durchgeführt. Zur Unterstützung der Platzierung der Prothese während des chirurgischen Eingriffs wurde in dieser Arbeit ein neues Fluoroskopie-basiertes TAKI Assistenzsystem entwickelt. Das entwickelte Assistenzsystem überlagert eine 3D-Geometrie des Aorten-Netzmodells und anatomischen Landmarken auf live-2D-fluoroskopische Bilder. Das 3D-Aorten-Netzmodell und die Landmarken werden auf Basis der interventionellen Angiographie und Fluoroskopie mittels eines C-Arm-CT-Systems rekonstruiert. Unter Verwendung dieser Aorten-Netzmodelle wird das Zielgebiet der Klappen-Implantation automatisch geschätzt. Mit Hilfe eines auf Template Matching basierenden Tracking-Ansatzes wird die Überlagerung des visualisierten 3D-Aorten-Netzmodells, der berechneten Landmarken und der Zielbereich der Implantation auf fluoroskopischen Bildern korrekt überlagert. Eine kompensation der Aortenwurzelbewegung erfolgt durch Bewegungsverfolgung eines Pigtail-Katheters in Bildsequenzen ohne Kontrastmittel. Eine starrere Intensitätsbasierte Registrierungsmethode wurde verwendet, um kontinuierlich die Aortenwurzelbewegung in Bildsequenzen mit Kontrastmittelgabe zu detektieren. Die Aortenklappenprothese wird in die fluoroskopischen Bilder eingeblendet und dient dem Chirurg als Leitfaden für die richtige Platzierung der realen Prothese. Eine interaktive Benutzerschnittstelle für den Chirurg wurde zur Initialisierung der Systemsalgorithmen, zur Steuerung der Visualisierung und für manuelle Korrektur eventueller Überlagerungsfehler entwickelt. Retrospektive Experimente wurden an mehreren Patienten-Datensätze aus der klinischen Routine der TAKI in einem Hybrid-OP durchgeführt. Hohe Erfolgsraten des entwickelten Assistenzsystems wurden für alle getesteten Patienten-Datensätze erzielt. Die Ergebnisse zeigen, dass das entwickelte chirurgische Assistenzsystem ein hilfreiches Werkzeug für den Chirurg bei der Platzierung Position der Prothese während des chirurgischen Eingriffs der TAKI bietet

Dynamic Image Processing for Guidance of Off-pump Beating Heart Mitral Valve Repair

Compared to conventional open heart procedures, minimally invasive off-pump beating heart mitral valve repair aims to deliver equivalent treatment for mitral regurgitation with reduced trauma and side effects. However, minimally invasive approaches are often limited by the lack of a direct view to surgical targets and/or tools, a challenge that is compounded by potential movement of the target during the cardiac cycle. For this reason, sophisticated image guidance systems are required in achieving procedural efficiency and therapeutic success. The development of such guidance systems is associated with many challenges. For example, the system should be able to provide high quality visualization of both cardiac anatomy and motion, as well as augmenting it with virtual models of tracked tools and targets. It should have the capability of integrating pre-operative images to the intra-operative scenario through registration techniques. The computation speed must be sufficiently fast to capture the rapid cardiac motion. Meanwhile, the system should be cost effective and easily integrated into standard clinical workflow. This thesis develops image processing techniques to address these challenges, aiming to achieve a safe and efficient guidance system for off-pump beating heart mitral valve repair. These techniques can be divided into two categories, using 3D and 2D image data respectively. When 3D images are accessible, a rapid multi-modal registration approach is proposed to link the pre-operative CT images to the intra-operative ultrasound images. The ultrasound images are used to display the real time cardiac motion, enhanced by CT data serving as high quality 3D context with annotated features. I also developed a method to generate synthetic dynamic CT images, aiming to replace real dynamic CT data in such a guidance system to reduce the radiation dose applied to the patients. When only 2D images are available, an approach is developed to track the feature of interest, i.e. the mitral annulus, based on bi-plane ultrasound images and a magnetic tracking system. The concept of modern GPU-based parallel computing is employed in most of these approaches to accelerate the computation in order to capture the rapid cardiac motion with desired accuracy. Validation experiments were performed on phantom, animal and human data. The overall accuracy of registration and feature tracking with respect to the mitral annulus was about 2-3mm with computation time of 60-400ms per frame, sufficient for one update per cardiac cycle. It was also demonstrated in the results that the synthetic CT images can provide very similar anatomical representations and registration accuracy compared to that of the real dynamic CT images. These results suggest that the approaches developed in the thesis have good potential for a safer and more effective guidance system for off-pump beating heart mitral valve repair

Image based approach for early assessment of heart failure.

In diagnosing heart diseases, the estimation of cardiac performance indices requires accurate segmentation of the left ventricle (LV) wall from cine cardiac magnetic resonance (CMR) images. MR imaging is noninvasive and generates clear images; however, it is impractical to manually process the huge number of images generated to calculate the performance indices. In this dissertation, we introduce a novel, fast, robust, bi-directional coupled parametric deformable models that are capable of segmenting the LV wall borders using first- and second-order visual appearance features. These features are embedded in a new stochastic external force that preserves the topology of the LV wall to track the evolution of the parametric deformable models control points. We tested the proposed segmentation approach on 15 data sets in 6 infarction patients using the Dice similarity coefficient (DSC) and the average distance (AD) between the ground truth and automated segmentation contours. Our approach achieves a mean DSC value of 0.926±0.022 and mean AD value of 2.16±0.60 mm compared to two other level set methods that achieve mean DSC values of 0.904±0.033 and 0.885±0.02; and mean AD values of 2.86±1.35 mm and 5.72±4.70 mm, respectively. Also, a novel framework for assessing both 3D functional strain and wall thickening from 4D cine cardiac magnetic resonance imaging (CCMR) is introduced. The introduced approach is primarily based on using geometrical features to track the LV wall during the cardiac cycle. The 4D tracking approach consists of the following two main steps: (i) Initially, the surface points on the LV wall are tracked by solving a 3D Laplace equation between two subsequent LV surfaces; and (ii) Secondly, the locations of the tracked LV surface points are iteratively adjusted through an energy minimization cost function using a generalized Gauss-Markov random field (GGMRF) image model in order to remove inconsistencies and preserve the anatomy of the heart wall during the tracking process. Then the circumferential strains are straight forward calculated from the location of the tracked LV surface points. In addition, myocardial wall thickening is estimated by co-allocation of the corresponding points, or matches between the endocardium and epicardium surfaces of the LV wall using the solution of the 3D laplace equation. Experimental results on in vivo data confirm the accuracy and robustness of our method. Moreover, the comparison results demonstrate that our approach outperforms 2D wall thickening estimation approaches

Virtual and Augmented Reality Techniques for Minimally Invasive Cardiac Interventions: Concept, Design, Evaluation and Pre-clinical Implementation

While less invasive techniques have been employed for some procedures, most intracardiac interventions are still performed under cardiopulmonary bypass, on the drained, arrested heart. The progress toward off-pump intracardiac interventions has been hampered by the lack of adequate visualization inside the beating heart. This thesis describes the development, assessment, and pre-clinical implementation of a mixed reality environment that integrates pre-operative imaging and modeling with surgical tracking technologies and real-time ultrasound imaging. The intra-operative echo images are augmented with pre-operative representations of the cardiac anatomy and virtual models of the delivery instruments tracked in real time using magnetic tracking technologies. As a result, the otherwise context-less images can now be interpreted within the anatomical context provided by the anatomical models. The virtual models assist the user with the tool-to-target navigation, while real-time ultrasound ensures accurate positioning of the tool on target, providing the surgeon with sufficient information to ``see\u27\u27 and manipulate instruments in absence of direct vision. Several pre-clinical acute evaluation studies have been conducted in vivo on swine models to assess the feasibility of the proposed environment in a clinical context. Following direct access inside the beating heart using the UCI, the proposed mixed reality environment was used to provide the necessary visualization and navigation to position a prosthetic mitral valve on the the native annulus, or to place a repair patch on a created septal defect in vivo in porcine models. Following further development and seamless integration into the clinical workflow, we hope that the proposed mixed reality guidance environment may become a significant milestone toward enabling minimally invasive therapy on the beating heart