Ramtin Gharleghi

Objective: Studies have shown marked sex disparities in Coronary Artery Diseases (CAD) epidemiology, yet the underlying mechanisms remain unclear. We explored the coronary anatomy and the resulting haemodynamics in patients with suspected but no significant CAD. Left Main (LM) bifurcations were 3D-reconstructed from 127 Computed Tomography Angiography images (42 males and 85 females, aged 38-81). Methods: Shape parameters including bifurcation angles, curvature, and diameters were measured, before solving the clinically relevant haemodynamic metrics. The severity and location of the vascular area exposed to adverse haemodynamics were compared between sexes. Results: Females were more likely than males to exhibit adversely low Time Averaged Endothelial Shear Stress (TAESS) along the inner wall of a bifurcation (16.8% vs. 10.7%). Males had a higher percentage of areas exposed to both adversely high Relative Residence Time (6.1% vs 4.2%, p = 0.001) and high Oscillatory Shear Index OSI (4.6% vs 2.3%, p<0.001). However, the OSI values were generally small and should be interpreted cautiously. Males had larger arteries (M vs F, LM: 4.0±0.5mm vs 3.3±0.6mm, LAD: 3.6±0.5mm vs 3.0±0.5mm, LCX:3.5±0.5mm vs 2.9±0.6mm), and females exhibited higher curvatures in all three branches (M vs F, LM: 0.40 vs 0.46, LAD: 0.45 vs 0.51, LCx: 0.47 vs 0.55,p<0.001). There was no difference in bifurcation angles. Conclusion: These differences may in part contribute to differences in CAD risk between sexes. Significance: This work facilitates a better understanding of sex differences in factors contributing to CAD, ultimately improving screening and therapeutic strategies particularly for women who currently have worse predictive outcomes.

Coronary artery diseases are among the leading causes of mortality worldwide. Timely and accurate diagnosis, facilitated by precise coronary artery segmentation, is pivotal in changing patient outcomes. In the realm of biomedical imaging, convolutional neural networks, especially the U-Net architecture, have revolutionised segmentation processes. However, one of the primary challenges remains the lack of benchmarking datasets specific to coronary arteries. However through the use of the recently published public dataset ASOCA, the potential of deep learning for accurate coronary segmentation can be improved. This paper delves deep into examining the performance of 25 distinct encoder-decoder combinations. Through analysis of the 40 cases provided to ASOCA participants, it is revealed that the EfficientNet-LinkNet combination, serving as encoder and decoder, stands out. It achieves a Dice coefficient of 0.882 and a 95th percentile Hausdorff distance of 4.753. These findings not only underscore the superiority of our model in comparison to those presented at the MICCAI 2020 challenge but also set the stage for future advancements in coronary artery segmentation, opening doors to enhanced diagnostic and treatment strategies.

Computed Tomography Coronary Angiography (CTCA) is a non-invasive method to evaluate coronary artery anatomy and disease. CTCA is ideal for geometry reconstruction to create virtual models of coronary arteries. To our knowledge there is no public dataset that includes centrelines and segmentation of the full coronary tree. We provide anonymized CTCA images, voxel-wise annotations and associated data in the form of centrelines, calcification scores and meshes of the coronary lumen in 20 normal and 20 diseased cases. Images were obtained along with patient information with informed, written consent as part of the Coronary Atlas. Cases were classified as normal (zero calcium score with no signs of stenosis) or diseased (confirmed coronary artery disease). Manual voxel-wise segmentations by three experts were combined using majority voting to generate the final annotations. Provided data can be used for a variety of research purposes, such as 3D printing patient-specific models, development and validation of segmentation algorithms, education and training of medical personnel and in-silico analyses such as testing of medical devices.

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 R ² 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.