Learning associations between clinical information and motion-based descriptors using a large scale MR-derived cardiac motion atlas

The availability of large scale databases containing imaging and non-imaging data, such as the UK Biobank, represents an opportunity to improve our understanding of healthy and diseased bodily function. Cardiac motion atlases provide a space of reference in which the motion fields of a cohort of subjects can be directly compared. In this work, a cardiac motion atlas is built from cine MR data from the UK Biobank (~ 6000 subjects). Two automated quality control strategies are proposed to reject subjects with insufficient image quality. Based on the atlas, three dimensionality reduction algorithms are evaluated to learn data-driven cardiac motion descriptors, and statistical methods used to study the association between these descriptors and non-imaging data. Results show a positive correlation between the atlas motion descriptors and body fat percentage, basal metabolic rate, hypertension, smoking status and alcohol intake frequency. The proposed method outperforms the ability to identify changes in cardiac function due to these known cardiovascular risk factors compared to ejection fraction, the most commonly used descriptor of cardiac function. In conclusion, this work represents a framework for further investigation of the factors influencing cardiac health.Comment: 2018 International Workshop on Statistical Atlases and Computational Modeling of the Hear

Deep Learning using K-space Based Data Augmentation for Automated Cardiac MR Motion Artefact Detection

Quality assessment of medical images is essential for complete automation of image processing pipelines. For large population studies such as the UK Biobank, artefacts such as those caused by heart motion are problematic and manual identification is tedious and time-consuming. Therefore, there is an urgent need for automatic image quality assessment techniques. In this paper, we propose a method to automatically detect the presence of motion-related artefacts in cardiac magnetic resonance (CMR) images. As this is a highly imbalanced classification problem (due to the high number of good quality images compared to the low number of images with motion artefacts), we propose a novel k-space based training data augmentation approach in order to address this problem. Our method is based on 3D spatio-temporal Convolutional Neural Networks, and is able to detect 2D+time short axis images with motion artefacts in less than 1ms. We test our algorithm on a subset of the UK Biobank dataset consisting of 3465 CMR images and achieve not only high accuracy in detection of motion artefacts, but also high precision and recall. We compare our approach to a range of state-of-the-art quality assessment methods.Comment: Accepted for MICCAI2018 Conferenc

Preoperative evaluation of pulmonary artery morphology and pulmonary circulation in neonates with pulmonary atresia - usefulness of MR angiography in clinical routine

BACKGROUND: To explore the role of contrast-enhanced magnetic resonance angiography (CE-MRA) in clinical routine for evaluating neonates with pulmonary atresia (PA) and to describe their pulmonary artery morphology and blood supply.CE-MRA studies of 15 neonates with PA (12 female; median weight: 2900 g) were retrospectively evaluated by two radiologists in consensus. Each study was judged to be either diagnostic or non-diagnostic depending on the potential to evaluate pulmonary artery morphology and pulmonary blood supply. In those cases where surgery or conventional angiocardiography was performed results were compared. RESULTS: CE-MRA was considered diagnostic in 87%. Pulmonary artery morphology was classified as "confluent with (n = 1) and without (n = 1) main pulmonary artery", "non-confluent" (n = 6) or "absent" (n = 7). Source of pulmonary blood supply was "a persistent arterial duct" (n = 12), "a direct" (n = 22) or "indirect (n = 9) aortopulmonary collateral artery (APCA)" or "an APCA from the ascending aorta" (n = 2). In no patient were there any additional findings at surgery or conventional angiocardiography which would have changed the therapeutic or surgical approach. CONCLUSIONS: CE-MRA is a useful diagnostic tool for the preoperative evaluation of the morphology of pulmonary arteries and blood supply in neonates with PA. In most cases diagnostic cardiac catheterization can be avoided

Artificial intelligence and automation in valvular heart diseases

Artificial intelligence (AI) is gradually changing every aspect of social life, and healthcare is no exception. The clinical procedures that were supposed to, and could previously only be handled by human experts can now be carried out by machines in a more accurate and efficient way. The coming era of big data and the advent of supercomputers provides great opportunities to the development of AI technology for the enhancement of diagnosis and clinical decision-making. This review provides an introduction to AI and highlights its applications in the clinical flow of diagnosing and treating valvular heart diseases (VHDs). More specifically, this review first introduces some key concepts and subareas in AI. Secondly, it discusses the application of AI in heart sound auscultation and medical image analysis for assistance in diagnosing VHDs. Thirdly, it introduces using AI algorithms to identify risk factors and predict mortality of cardiac surgery. This review also describes the state-of-the-art autonomous surgical robots and their roles in cardiac surgery and intervention

Quantitative imaging in cardiovascular CT angiography

In de afgelopen decennia is computertomografie (CT) een prominente niet-invasieve modaliteit om hart- en vaatziekten te evalueren geworden. Dit proefschrift heeft als doel de rol van CT in de therapeutische behandeling van coronaire hartziekte (CAD) en klepaandoeningen te onderzoeken.De relatie tussen kransslagadergeometrie (statisch en dynamisch) en aanwezigheid en omvang van CAD met CT werd onderzocht. De resultaten suggereren dat de statische geometrie van de kransslagader significant gerelateerd is aan de aanwezigheid van plaque en stenose. Er was echter geen verband tussen dynamische verandering van de coronaire arterie-geometrie en de ernst van CAD. Een algoritme om de invloed van intraluminair contrastmiddel op niet-verkalkte atherosclerotische plaque Hounsfield-Unit-waarden te corrigeren werd gepresenteerd en gevalideerd met behulp van fantomen.Diagnose en operatieplanning kunnen cruciale gevolgen hebben voor de klinische uitkomst van chirurgische ingrepen. In dit proefschrift wordt beschreven dat halfautomatische softwareprogramma’s het kwantificeren van het aortaklepgebied betere reproduceerbare resultaten toonden in vergelijking met handmatige metingen, en vergelijkbare resultaten met de huidige gouden standaard, de echocardiografie. Een systematische review over het dynamische gedrag van de aorta-annulus toont aan dat de vorm van de aorta-annulus tijdens de hartcyclus verandert, wat impliceert dat er bij het bepalen van een prothese rekening moet worden gehouden met meerdere fasen. Een andere review beschrijft het gebruik van 3D-printen in de chirurgische planning samen met andere toepassingen voor de behandeling van hartklepaandoeningen.CT is de belangrijkste beeldvormingsmodaliteit in deze onderzoeken, die gericht waren op de therapeutische behandeling van hart- en vaatziekten, van vroege risicobepaling tot diagnose en chirurgische planning.In the recent decades computed tomography (CT) has emerged as a dominant non-invasive modality to evaluate cardiovascular diseases. This thesis aimed to explore the role of CT in the therapeutic management of coronary artery disease (CAD) and valvular diseases.The relationship between both static and dynamic coronary artery geometry and presence and extent of CAD using CT was investigated. The results suggest that the static coronary artery geometry is significantly related to presence of plaque and significant stenosis. However, there were no such relationship between dynamic change of coronary artery geometry and severity of CAD. As part of this thesis an algorithm to correct the influence of lumen contrast enhancement on non-calcified atherosclerotic plaque Hounsfield-Unit values was presented. The algorithm was validated using phantoms. The diagnosis and surgical planning may have crucial impact on clinical outcome. Semi-automatic software for aortic valve area quantification presented in this thesis was proven to be more repeatable and similar to gold standard echocardiography in comparison to manual measurements. The systematic review regarding the dynamic behavior of aortic annulus revealed that aortic annulus geometry changes throughout the cardiac cycle which implies that multiple phases should be taken into account for prosthesis sizing. Another review in this thesis discusses the use of 3D printing in the surgical planning along with other applications for the treatment of valvular diseases.CT is the main imaging modality in these studies which were focused on the therapeutic management of cardiovascular diseases from early risk determination to diagnosis and surgical planning

3D MODELLING AND RAPID PROTOTYPING FOR CARDIOVASCULAR SURGICAL PLANNING – TWO CASE STUDIES

In the last years, cardiovascular diagnosis, surgical planning and intervention have taken advantages from 3D modelling and rapid prototyping techniques. The starting data for the whole process is represented by medical imagery, in particular, but not exclusively, computed tomography (CT) or multi-slice CT (MCT) and magnetic resonance imaging (MRI). On the medical imagery, regions of interest, i.e. heart chambers, valves, aorta, coronary vessels, etc., are segmented and converted into 3D models, which can be finally converted in physical replicas through 3D printing procedure. In this work, an overview on modern approaches for automatic and semiautomatic segmentation of medical imagery for 3D surface model generation is provided. The issue of accuracy check of surface models is also addressed, together with the critical aspects of converting digital models into physical replicas through 3D printing techniques. A patient-specific 3D modelling and printing procedure (Figure 1), for surgical planning in case of complex heart diseases was developed. The procedure was applied to two case studies, for which MCT scans of the chest are available. In the article, a detailed description on the implemented patient-specific modelling procedure is provided, along with a general discussion on the potentiality and future developments of personalized 3D modelling and printing for surgical planning and surgeons practice

Deep learning tools for outcome prediction in a trial fibrilation from cardiac MRI

Tese de mestrado integrado em Engenharia Biomédica e Biofísica (Engenharia Clínica e Instrumentação Médica), Universidade de Lisboa, Faculdade de Ciências, 2021Atrial fibrillation (AF), is the most frequent sustained cardiac arrhythmia, described by an irregular and rapid contraction of the two upper chambers of the heart (the atria). AF development is promoted and predisposed by atrial dilation, which is a consequence of atria adaptation to AF. However, it is not clear whether atrial dilation appears similarly over the cardiac cycle and how it affects ventricular volumes. Catheter ablation is arguably the AF gold standard treatment. In their current form, ablations are capable of directly terminating AF in selected patients but are only first-time effective in approximately 50% of the cases. In the first part of this work, volumetric functional markers of the left atrium (LA) and left ventricle (LV) of AF patients were studied. More precisely, a customised convolutional neural network (CNN) was proposed to segment, across the cardiac cycle, the LA from short axis CINE MRI images acquired with full cardiac coverage in AF patients. Using the proposed automatic LA segmentation, volumetric time curves were plotted and ejection fractions (EF) were automatically calculated for both chambers. The second part of the project was dedicated to developing classification models based on cardiac MR images. The EMIDEC STACOM 2020 challenge was used as an initial project and basis to create binary classifiers based on fully automatic classification neural networks (NNs), since it presented a relatively simple binary classification task (presence/absence of disease) and a large dataset. For the challenge, a deep learning NN was proposed to automatically classify myocardial disease from delayed enhancement cardiac MR (DE-CMR) and patient clinical information. The highest classification accuracy (100%) was achieved with Clinic-NET+, a NN that used information from images, segmentations and clinical annotations. For the final goal of this project, the previously referred NNs were re-trained to predict AF recurrence after catheter ablation (CA) in AF patients using pre-ablation LA short axis in CINE MRI images. In this task, the best overall performance was achieved by Clinic-NET+ with a test accuracy of 88%. This work shown the potential of NNs to interpret and extract clinical information from cardiac MRI. If more data is available, in the future, these methods can potentially be used to help and guide clinical AF prognosis and diagnosis

Assessment of mitral valve regurgitation by cardiovascular magnetic resonance imaging

Mitral regurgitation (MR) is a common valvular heart disease and is the second most frequent indication for heart valve surgery in Western countries. Echocardiography is the recommended first-line test for the assessment of valvular heart disease, but cardiovascular magnetic resonance imaging (CMR) provides complementary information, especially for assessing MR severity and to plan the timing of intervention. As new CMR techniques for the assessment of MR have arisen, standardizing CMR protocols for research and clinical studies has become important in order to optimize diagnostic utility and support the wider use of CMR for the clinical assessment of MR. In this Consensus Statement, we provide a detailed description of the current evidence on the use of CMR for MR assessment, highlight its current clinical utility, and recommend a standardized CMR protocol and report for MR assessment