Towards Patient Specific Mitral Valve Modelling via Dynamic 3D Transesophageal Echocardiography

Mitral valve disease is a common pathologic problem occurring increasingly in an aging population, and many patients suffering from mitral valve disease require surgical intervention. Planning an interventional approach from diagnostic imaging alone remains a significant clinical challenge. Transesophageal echocardiography (TEE) is the primary imaging modality used diagnostically, it has limitations in image quality and field-of-view. Recently, developments have been made towards modelling patient-specific deformable mitral valves from TEE imaging, however, a major barrier to producing accurate valve models is the need to derive the leaflet geometry through segmentation of diagnostic TEE imaging. This work explores the development of volume compounding and automated image analysis to more accurately and quickly capture the relevant valve geometry needed to produce patient-specific mitral valve models. Volume compounding enables multiple ultrasound acquisitions from different orientations and locations to be aligned and blended to form a single volume with improved resolution and field-of-view. A series of overlapping transgastric views are acquired that are then registered together with the standard en-face image and are combined using a blending function. The resulting compounded ultrasound volumes allow the visualization of a wider range of anatomical features within the left heart, enhancing the capabilities of a standard TEE probe. In this thesis, I first describe a semi-automatic segmentation algorithm based on active contours designed to produce segmentations from end-diastole suitable for deriving 3D printable molds. Subsequently I describe the development of DeepMitral, a fully automatic segmentation pipeline which leverages deep learning to produce very accurate segmentations with a runtime of less than ten seconds. DeepMitral is the first reported method using convolutional neural networks (CNNs) on 3D TEE for mitral valve segmentations. The results demonstrate very accurate leaflet segmentations, and a reduction in the time and complexity to produce a patient-specific mitral valve replica. Finally, a real-time annulus tracking system using CNNs to predict the annulus coordinates in the spatial frequency domain was developed. This method facilitates the use of mitral annulus tracking in real-time guidance systems, and further simplifies mitral valve modelling through the automatic detection of the annulus, which is a key structure for valve quantification, and reproducing accurate leaflet dynamics

Recent Applications of Three Dimensional Printing in Cardiovascular Medicine

Three dimensional (3D) printing, which consists in the conversion of digital images into a 3D physical model, is a promising and versatile field that, over the last decade, has experienced a rapid development in medicine. Cardiovascular medicine, in particular, is one of the fastest growing area for medical 3D printing. In this review, we firstly describe the major steps and the most common technologies used in the 3D printing process, then we present current applications of 3D printing with relevance to the cardiovascular field. The technology is more frequently used for the creation of anatomical 3D models useful for teaching, training, and procedural planning of complex surgical cases, as well as for facilitating communication with patients and their families. However, the most attractive and novel application of 3D printing in the last years is bioprinting, which holds the great potential to solve the ever-increasing crisis of organ shortage. In this review, we then present some of the 3D bioprinting strategies used for fabricating fully functional cardiovascular tissues, including myocardium, heart tissue patches, and heart valves. The implications of 3D bioprinting in drug discovery, development, and delivery systems are also briefly discussed, in terms of in vitro cardiovascular drug toxicity. Finally, we describe some applications of 3D printing in the development and testing of cardiovascular medical devices, and the current regulatory frameworks that apply to manufacturing and commercialization of 3D printed products

Biomimetic phantom with anatomical accuracy for evaluating brain volumetric measurements with magnetic resonance imaging

Purpose: Brain image volumetric measurements (BVM) methods have been used to quantify brain tissue volumes using magnetic resonance imaging (MRI) when investigating abnormalities. Although BVM methods are widely used, they need to be evaluated to quantify their reliability. Currently, the gold-standard reference to evaluate a BVM is usually manual labeling measurement. Manual volume labeling is a time-consuming and expensive task, but the confidence level ascribed to this method is not absolute. We describe and evaluate a biomimetic brain phantom as an alternative for the manual validation of BVM. Methods: We printed a three-dimensional (3D) brain mold using an MRI of a three-year-old boy diagnosed with Sturge-Weber syndrome. Then we prepared three different mixtures of styrene-ethylene/butylene-styrene gel and paraffin to mimic white matter (WM), gray matter (GM), and cerebrospinal fluid (CSF). The mold was filled by these three mixtures with known volumes. We scanned the brain phantom using two MRI scanners, 1.5 and 3.0 Tesla. Our suggestion is a new challenging model to evaluate the BVM which includes the measured volumes of the phantom compartments and its MRI. We investigated the performance of an automatic BVM, i.e., the expectation–maximization (EM) method, to estimate its accuracy in BVM. Results: The automatic BVM results using the EM method showed a relative error (regarding the phantom volume) of 0.08, 0.03, and 0.13 (±0.03 uncertainty) percentages of the GM, CSF, and WM volume, respectively, which was in good agreement with the results reported using manual segmentation. Conclusions: The phantom can be a potential quantifier for a wide range of segmentation methods

Stem Cell Research on Cardiology

Even today, cardiovascular diseases are the main cause of death worldwide, and therapeutic approaches are very restricted. Due to the limited regenerative capabilities of terminally differentiated cardiomyocytes post injury, new strategies to treat cardiac patients are urgently needed. Post myocardial injury, resident fibroblasts begin to generate the extracellular matrix, resulting in fibrosis, and finally, cardiac failure. Recently, preclinical investigations and clinical trials raised hope in stem cell-based approaches, to be an effective therapy option for these diseases. So far, several types of stem cells have been identified to be promising candidates to be applied for treatment: cardiac progenitor cells, bone marrow derived stem cells, embryonic and induced pluripotent stem cells, as well as their descendants. Furthermore, the innovative techniques of direct cardiac reprogramming of cells offered promising options for cardiovascular research, in vitro and in vivo. Hereby, the investigation of underlying and associated mechanisms triggering the therapeutic effects of stem cell application is of particular importance to improve approaches for heart patients. This Special Issue of Cells provides the latest update in the rapidly developing field of regenerative medicine in cardiology