Computer Vision Techniques for Transcatheter Intervention

Minimally invasive transcatheter technologies have demonstrated substantial promise for the diagnosis and treatment of cardiovascular diseases. For example, TAVI is an alternative to AVR for the treatment of severe aortic stenosis and TAFA is widely used for the treatment and cure of atrial fibrillation. In addition, catheter-based IVUS and OCT imaging of coronary arteries provides important information about the coronary lumen, wall and plaque characteristics. Qualitative and quantitative analysis of these cross-sectional image data will be beneficial for the evaluation and treatment of coronary artery diseases such as atherosclerosis. In all the phases (preoperative, intraoperative, and postoperative) during the transcatheter intervention procedure, computer vision techniques (e.g., image segmentation, motion tracking) have been largely applied in the field to accomplish tasks like annulus measurement, valve selection, catheter placement control, and vessel centerline extraction. This provides beneficial guidance for the clinicians in surgical planning, disease diagnosis, and treatment assessment. In this paper, we present a systematical review on these state-of-the-art methods.We aim to give a comprehensive overview for researchers in the area of computer vision on the subject of transcatheter intervention. Research in medical computing is multi-disciplinary due to its nature, and hence it is important to understand the application domain, clinical background, and imaging modality so that methods and quantitative measurements derived from analyzing the imaging data are appropriate and meaningful. We thus provide an overview on background information of transcatheter intervention procedures, as well as a review of the computer vision techniques and methodologies applied in this area

Combinatorial optimisation for arterial image segmentation.

Cardiovascular disease is one of the leading causes of the mortality in the western world. Many imaging modalities have been used to diagnose cardiovascular diseases. However, each has different forms of noise and artifacts that make the medical image analysis field important and challenging. This thesis is concerned with developing fully automatic segmentation methods for cross-sectional coronary arterial imaging in particular, intra-vascular ultrasound and optical coherence tomography, by incorporating prior and tracking information without any user intervention, to effectively overcome various image artifacts and occlusions. Combinatorial optimisation methods are proposed to solve the segmentation problem in polynomial time. A node-weighted directed graph is constructed so that the vessel border delineation is considered as computing a minimum closed set. A set of complementary edge and texture features is extracted. Single and double interface segmentation methods are introduced. Novel optimisation of the boundary energy function is proposed based on a supervised classification method. Shape prior model is incorporated into the segmentation framework based on global and local information through the energy function design and graph construction. A combination of cross-sectional segmentation and longitudinal tracking is proposed using the Kalman filter and the hidden Markov model. The border is parameterised using the radial basis functions. The Kalman filter is used to adapt the inter-frame constraints between every two consecutive frames to obtain coherent temporal segmentation. An HMM-based border tracking method is also proposed in which the emission probability is derived from both the classification-based cost function and the shape prior model. The optimal sequence of the hidden states is computed using the Viterbi algorithm. Both qualitative and quantitative results on thousands of images show superior performance of the proposed methods compared to a number of state-of-the-art segmentation methods

Automatic segmentation of cross-sectional coronary arterial images

We present a novel approach to segment coronary cross-sectional images acquired using catheterization imaging techniques, i.e. intra-vascular ultrasound (IVUS) and optical coherence tomography (OCT). The proposed approach combines cross-sectional segmentation with longitudinal tracking in order to tackle various forms of imaging artifacts and to achieve consistent segmentation. A node-weighted directed graph is constructed on two consecutive cross-sectional frames with embedded shape constraints within individual cross-sections or frames and between consecutive frames. The intra-frame constraints are derived from a set of training samples and are embedded in both graph construction and its cost function. The inter-frame constraints are imposed by tracking the borders of interest across multiple frames. The coronary images are transformed from Cartesian coordinates to polar coordinates. Graph partition can then be formulated as searching an optimal interface in the node-weighted directed graph without user initialization. It also allows efficient parametrization of the border using radial basis function (RBF) and thus reduces the tracking of a large number of border points to a very few RBF centers. Moreover, we carry out supervised column-wise tissue classification in order to automatically optimize the feature selection. Instead of empirically assigning weights to different feature detectors, we dynamically and automatically adapt those weighting depending on the tissue compositions in each individual column of pixels

2D and 3D segmentation of medical images.

"Cardiovascular disease is one of the leading causes of the morbidity and mortality in the western world today. Many different imaging modalities are in place today to diagnose and investigate cardiovascular diseases. Each of these, however, has strengths and weaknesses. There are different forms of noise and artifacts in each image modality that combine to make the field of medical image analysis both important and challenging. The aim of this thesis is develop a reliable method for segmentation of vessel structures in medical imaging, combining the expert knowledge of the user in such a way as to maintain efficiency whilst overcoming the inherent noise and artifacts present in the images. We present results from 2D segmentation techniques using different methodologies, before developing 3D techniques for segmenting vessel shape from a series of images. The main drive of the work involves the investigation of medical images obtained using catheter based techniques, namely Intra Vascular Ultrasound (IVUS) and Optical Coherence Tomography (OCT). We will present a robust segmentation paradigm, combining both edge and region information to segment the media-adventitia, and lumenal borders in those modalities respectively. By using a semi-interactive method that utilizes "soft" constraints, allowing imprecise user input which provides a balance between using the user's expert knowledge and efficiency. In the later part of the work, we develop automatic methods for segmenting the walls of lymph vessels. These methods are employed on sequential images in order to obtain data to reconstruct the vessel walls in the region of the lymph valves. We investigated methods to segment the vessel walls both individually and simultaneously, and compared the results both quantitatively and qualitatively in order obtain the most appropriate for the 3D reconstruction of the vessel wall. Lastly, we adapt the semi-interactive method used on vessels earlier into 3D to help segment out the lymph valve. This involved the user interactive method to provide guidance to help segment the boundary of the lymph vessel, then we apply a minimal surface segmentation methodology to provide segmentation of the valve.

Intravascular Ultrasound

Intravascular ultrasound (IVUS) is a cardiovascular imaging technology using a specially designed catheter with a miniaturized ultrasound probe for the assessment of vascular anatomy with detailed visualization of arterial layers. Over the past two decades, this technology has developed into an indispensable tool for research and clinical practice in cardiovascular medicine, offering the opportunity to gather diagnostic information about the process of atherosclerosis in vivo, and to directly observe the effects of various interventions on the plaque and arterial wall. This book aims to give a comprehensive overview of this rapidly evolving technique from basic principles and instrumentation to research and clinical applications with future perspectives

Segmentation d’images intravasculaires ultrasonores

L'imagerie intravasculaire ultrasonore (IVUS) est une technologie médicale par cathéter qui produit des images de coupe des vaisseaux sanguins. Elle permet de quantifier et d'étudier la morphologie de plaques d'athérosclérose en plus de visualiser la structure des vaisseaux sanguins (lumière, intima, plaque, média et adventice) en trois dimensions. Depuis quelques années, cette méthode d'imagerie est devenue un outil de choix en recherche aussi bien qu'en clinique pour l'étude de la maladie athérosclérotique. L'imagerie IVUS est par contre affectée par des artéfacts associés aux caractéristiques des capteurs ultrasonores, par la présence de cônes d'ombre causés par les calcifications ou des artères collatérales, par des plaques dont le rendu est hétérogène ou par le chatoiement ultrasonore (speckle) sanguin. L'analyse automatisée de séquences IVUS de grande taille représente donc un défi important. Une méthode de segmentation en trois dimensions (3D) basée sur l'algorithme du fast-marching à interfaces multiples est présentée. La segmentation utilise des attributs des régions et contours des images IVUS. En effet, une nouvelle fonction de vitesse de propagation des interfaces combinant les fonctions de densité de probabilité des tons de gris des composants de la paroi vasculaire et le gradient des intensités est proposée. La segmentation est grandement automatisée puisque la lumière du vaisseau est détectée de façon entièrement automatique. Dans une procédure d'initialisation originale, un minimum d'interactions est nécessaire lorsque les contours initiaux de la paroi externe du vaisseau calculés automatiquement sont proposés à l'utilisateur pour acceptation ou correction sur un nombre limité d'images de coupe longitudinale. La segmentation a été validée à l'aide de séquences IVUS in vivo provenant d'artères fémorales provenant de différents sous-groupes d'acquisitions, c'est-à-dire pré-angioplastie par ballon, post-intervention et à un examen de contrôle 1 an suivant l'intervention. Les résultats ont été comparés avec des contours étalons tracés manuellement par différents experts en analyse d'images IVUS. Les contours de la lumière et de la paroi externe du vaisseau détectés selon la méthode du fast-marching sont en accord avec les tracés manuels des experts puisque les mesures d'aire sont similaires et les différences point-à-point entre les contours sont faibles. De plus, la segmentation par fast-marching 3D s'est effectuée en un temps grandement réduit comparativement à l'analyse manuelle. Il s'agit de la première étude rapportée dans la littérature qui évalue la performance de la segmentation sur différents types d'acquisition IVUS. En conclusion, la segmentation par fast-marching combinant les informations des distributions de tons de gris et du gradient des intensités des images est précise et efficace pour l'analyse de séquences IVUS de grandes tailles. Un outil de segmentation robuste pourrait devenir largement répandu pour la tâche ardue et fastidieuse qu'est l'analyse de ce type d'images.Intravascular ultrasound (IVUS) is a catheter based medical imaging technique that produces cross-sectional images of blood vessels. These images provide quantitative assessment of the vascular wall, information about the nature of atherosclerotic lesions as well as the plaque shape and size. Over the past few years, this medical imaging modality has become a useful tool in research and clinical applications, particularly in atherosclerotic disease studies. However, IVUS imaging is subject to catheter ring-down artifacts, missing vessel parts due to calcification shadowing or side-branches, heterogeneously looking plaques and ultrasonic speckle from blood. The automated analysis of large IVUS data sets thus represents an important challenge. A three-dimensional segmentation algorithm based on the multiple interface fast-marching method is presented. The segmentation is based on region and contour features of the IVUS images: a new speed fonction for the interface propagation that combines the probability density functions (PDFs) of the vessel wall components and the intensity gradients is proposed. The segmentation is highly automated with the detection of the lumen boundary that is fully automatic. Minimal interactions are necessary with a novel initialization procedure since initial contours of the external vessel wall border are also computed automatically on a limited number of longitudinal images and then proposed to the user for acceptance or correction. The segmentation method was validated with in-vivo IVUS data sets acquired from femoral arteries. This database contained 3 subgroups: pullbacks acquired before balloon angioplasty, after the intervention and at a 1 year follow-up examination. Results were compared with validation contours that were manually traced by different experts in IVUS image analysis. The lumen and external wall boundaries detected with the fast-marching method are in agreement with the experts' manually traced contours with similarly found area measurements and small point-to-point contour differences. In addition, the 3D fast-marching segmentation method dramatically reduced the analysis time compared to manual tracing. Such a valdiation study, with comparison between pre- and post-intervention data, has never been reported in the IVUS segmentation literature. In conclusion, the fast-marching method combining the information on the gray level distributions and intensity gradients of the images is precise and efficient to analyze large IVUS sequences. It is hoped that the fast-marching method will become a widely used tool for the fastidious and difficult task of IVUS image processing

Non-invasive ultrasound monitoring of regional carotid wall structure and deformation in atherosclerosis

Thesis (Ph. D.)--Harvard--Massachusetts Institute of Technology Division of Health Sciences and Technology, 2001.Includes bibliographical references (p. 223-242).Atherosclerosis is characterized by local remodeling of arterial structure and distensibility. Developing lesions either progress gradually to compromise tissue perfusion or rupture suddenly to cause catastrophic myocardial infarction or stroke. Reliable measurement of changes in arterial structure and composition is required for assessment of disease progression. Non-invasive carotid ultrasound can image the heterogeneity of wall structure and distensibility caused by atherosclerosis. However, this capability has not been utilized for clinical monitoring because of speckle noise and other artifacts. Clinical measures focus instead on average wall thickness and diameter distension in the distal common carotid to reduce sensitivity to noise. The goal of our research was to develop an effective system for reliable regional structure and deformation measurements since these are more sensitive indicators of disease progression. We constructed a system for freehand ultrasound scanning based on custom software which simultaneously acquires real-time image sequences and 3D frame localization data from an electromagnetic spatial localizer. With finite element modeling, we evaluated candidate measures of regional wall deformation.(cont.) Finally, we developed a multi-step scheme for robust estimation of local wall structure and deformation. This new strategy is based on a directionally-sensitive segmentation functional and a motion-region-of-interest constrained optical flow algorithm. We validated this estimator with simulated images and clinical ultrasound data. The results show structure estimates that are accurate and precise, with inter- and intra-observer reproducibility surpassing existing methods. Estimates of wall velocity and deformation likewise show good overall accuracy and precision. We present results from a proof-of-principle evaluation conducted in a pilot study of normal subjects and clinical patients. For one example, we demonstrate the combination of 2D image processing with 3D frame localization for visualization of the carotid volume. With slice localization, estimates of carotid wall structure and deformation can be derived for all axial positions along the carotid artery. The elements developed here provide the tools necessary for reliable quantification of regional wall structure and composition changes which result from atherosclerosis.by Raymond C. Chan.Ph.D