Transient wall shear stress estimation in coronary bifurcations using convolutional neural networks

Background and Objective: Haemodynamic metrics, such as blood flow induced shear stresses at the inner vessel lumen, are associated with the development and progression of coronary artery disease. Understanding these metrics may therefore improve the assessment of an individual's coronary disease risk. However, the calculation of such luminal Wall Shear Stress (WSS) using traditional Computational Fluid Dynamics (CFD) methods is relatively slow and computationally expensive. As a result, CFD based haemodynamic computation is not suitable for integrated and large-scale use in clinical settings. Methods: In this work, deep learning techniques are proposed as an alternative method to CFD, whereby luminal WSS magnitude can be predicted in coronary bifurcations throughout the cardiac cycle based on the steady state solution (which takes <120 seconds to calculate including preprocessing), vessel geometry and additional global features. The deep learning model is trained on a dataset of 101 patient-specific and 2626 synthetic left main bifurcation models with 26 separate patient-specific cases used as the test set. Results: The model showed high fidelity predictions with <5% (normalised against mean WSS magnitude) deviation to CFD derived values as the gold-standard method, while being orders of magnitude faster with on average <2 minutes versus 3 hours computation for transient CFD. Conclusions: This method therefore offers a new approach to substantially reduce the computational cost involved in, for example, large-scale population studies of coronary haemodynamic metrics, and may therefore open the pathway for future clinical integration

Learning Left Main Bifurcation Shape Features with an Autoencoder

Geometric characteristics of the coronary arteries have been suggested as potential markers for disease risk. However, evaluation of such characteristics rely on judgement by human experts, and are thus variable and may lack sophistication. Here we apply recent advances in 3D deep learning to automatically obtain shape representation of the Left Main Bifurcation (LMB) of the coronary artery. We train a Variational Auto-Encoder based on the FoldingNet architecture to encode LMB shape features in a 450-dimension feature vector. The geometric features of patient-specific LMBs can then be manipulated by modifying, combining or interpolating the feature vectors before decoding. We also show that these vectors, on average, perform better than hand-crafted features in predicting measures of adverse blood flow (oscillating shear index or 'OSI', relative residence time 'RRT' and time averaged wall shear stress 'TAWSS') with a R2 goodness of fit value of 84.1% compared to 79.7%. These learned representations can also be used in other downstream predictive modelling tasks where an encoded version of a LMB is needed

Helical Flow in Healthy and Diseased Patient-specific Coronary Bifurcations

Helical flow (HF) exists in healthy and diseased coronary bifurcations and was found to have a protective atherosclerotic vascular effect in other vessels. However, the role of HF in patient-specific human coronary arteries still needs further study, and is therefore the objective of this study in both healthy and diseased bifurcations. Computational studies were conducted on 16 patient-specific coronary bifurcations, including eight healthy and eight identical cases with idealized narrowing to represent disease. In general, higher HF intensity may have a favorable effect as it corelated to the reduction of the percentage vessel area exposed to adverse time averaged wall shear stress (TAWSS%) in both healthy and diseased models. The HF intensity and distribution of each model varies due to the complex shape of patient-specific models. The presence of disease appears to have an important impact on the downstream HF patterns and the TAWSS distributions. Clinical Relevance - By understanding the relationship between HF and hemodynamics, HF may be used as a predictor for the formation and progression of atherosclerotic plaque in coronary arteries instead of near-wall WSS measures, which can be determined with higher accuracy in vivo

A new and automated risk prediction of coronary artery disease using clinical endpoints and medical imaging-derived patient-specific insights: protocol for the retrospective GeoCAD cohort study

INTRODUCTION: Coronary artery disease (CAD) is the leading cause of death worldwide. More than a quarter of cardiovascular events are unexplained by current absolute cardiovascular disease risk calculators, and individuals without clinical risk factors have been shown to have worse outcomes. The 'anatomy of risk' hypothesis recognises that adverse anatomical features of coronary arteries enhance atherogenic haemodynamics, which in turn mediate the localisation and progression of plaques. We propose a new risk prediction method predicated on CT coronary angiography (CTCA) data and state-of-the-art machine learning methods based on a better understanding of anatomical risk for CAD. This may open new pathways in the early implementation of personalised preventive therapies in susceptible individuals as a potential key in addressing the growing burden of CAD. METHODS AND ANALYSIS: GeoCAD is a retrospective cohort study in 1000 adult patients who have undergone CTCA for investigation of suspected CAD. It is a proof-of-concept study to test the hypothesis that advanced image-derived patient-specific data can accurately predict long-term cardiovascular events. The objectives are to (1) profile CTCA images with respect to variations in anatomical shape and associated haemodynamic risk expressing, at least in part, an individual's CAD risk, (2) develop a machine-learning algorithm for the rapid assessment of anatomical risk directly from unprocessed CTCA images and (3) to build a novel CAD risk model combining traditional risk factors with these novel anatomical biomarkers to provide a higher accuracy CAD risk prediction tool. ETHICS AND DISSEMINATION: The study protocol has been approved by the St Vincent's Hospital Human Research Ethics Committee, Sydney-2020/ETH02127 and the NSW Population and Health Service Research Ethics Committee-2021/ETH00990. The project outcomes will be published in peer-reviewed and biomedical journals, scientific conferences and as a higher degree research thesis

Automated segmentation of normal and diseased coronary arteries – The ASOCA challenge

Cardiovascular disease is a major cause of death worldwide. Computed Tomography Coronary Angiography (CTCA) is a non-invasive method used to evaluate coronary artery disease, as well as evaluating and reconstructing heart and coronary vessel structures. Reconstructed models have a wide array of for educational, training and research applications such as the study of diseased and non-diseased coronary anatomy, machine learning based disease risk prediction and in-silico and in-vitro testing of medical devices. However, coronary arteries are difficult to image due to their small size, location, and movement, causing poor resolution and artefacts. Segmentation of coronary arteries has traditionally focused on semi-automatic methods where a human expert guides the algorithm and corrects errors, which severely limits large-scale applications and integration within clinical systems. International challenges aiming to overcome this barrier have focussed on specific tasks such as centreline extraction, stenosis quantification, and segmentation of specific artery segments only. Here we present the results of the first challenge to develop fully automatic segmentation methods of full coronary artery trees and establish the first large standardized dataset of normal and diseased arteries. This forms a new automated segmentation benchmark allowing the automated processing of CTCAs directly relevant for large-scale and personalized clinical applications

Secondary flow in bifurcations – Important effects of curvature, bifurcation angle and stents

Coronary bifurcations have complex flow patterns including secondary flow zones and helical flow, which directly affect pathophysiological mechanisms such as the development of atherosclerosis. The objective of this study was to generate insights into the effects of curvature, bifurcation angle and the presence of stents on flow patterns and resulting haemodynamics in coronary left main bifurcations. The blood flow and associated metrics were modelled in both idealised and patient-specific bifurcations with varying curvature and bifurcation angles with and without stents, resulting in a total of 128 geometries considered. The results showed that larger curvature of bifurcating vessels has a significant influence on secondary flow, especially with distance to the bifurcation region, causing a skew, spin and asymmetry of Dean vortices, an increase in helical flow intensity with symmetry loss, and a decrease in adversely low time-average wall shear stress (TAWSS). Generally, asymmetric flow patterns coincided with adversely low TAWSS regions. In identical stented geometries, the presence of the stents induced local recirculation immediately adjacent to the stent struts, thus generating adversely low TAWSS in these areas, with some effect on the overall secondary flow. Overall, the effect of stents outweighed the effect of curvature and BA. This new knowledge contributes to a better understanding of the joint effects of curvature, bifurcation angle, and stents on flow patterns and haemodynamics in coronary bifurcations

A multi-objective optimization of stent geometries

Stents are scaffolding cardiovascular implants used to restore blood flow in narrowed arteries. However, the presence of the stent alters local blood flow and shear stresses on the surrounding arterial wall, which can cause adverse tissue responses and increase the risk of adverse outcomes. There is a need for optimization of stent designs for hemodynamic performance. We used multi-objective optimization to identify ideal combinations of design variables by assessing potential trade-offs based on common hemodynamic indices associated with clinical risk and mechanical performance of the stents. We studied seven design variables including strut cross-section, strut dimension, strut angle, cell alignment, cell height, connector type and connector arrangement. Optimization objectives were the percentage of vessel area exposed to adversely low time averaged WSS (TAWSS) and adversely high Wall Shear Stress (WSS) assessed using computational fluid dynamics modeling, as well as radial stiffness of the stent using FEA simulation. Two multi-objective optimization algorithms were used and compared to iteratively predict ideal designs. Out of 50 designs, three best designs with respect to each of the three objectives, and two designs in regard to overall performance were identified