arXiv subjects

Robin P. Choudhury

Publications and source records attributed to Robin P. Choudhury.

3 recordsLinked to original sources

arXiv2025

Anatomical basis of sex differences in the electrocardiogram identified by three-dimensional torso-heart imaging reconstruction pipeline

The electrocardiogram (ECG) is used for diagnosis and risk stratification in myocardial infarction (MI). Women have a higher incidence of missed MI diagnosis and complications following infarction, and to address this we aim to provide quantitative information on sex-differences in ECG and torso-ventricular anatomical features and their interdependence. A novel computational automated pipeline is presented enabling the three-dimensional reconstruction of torso-ventricular anatomies for 425 post-MI subjects and 1051 healthy controls from UK Biobank clinical images. Regression models were created relating torso-ventricular and ECG parameters. We found that female hearts were positioned more posteriorly and superiorly than male, and in MI hearts were oriented more horizontally, especially for women. Post-MI women exhibited less QRS prolongation, requiring 27% more prolongation than men to exceed 120ms. Only half of the sex difference in QRS duration was associated with smaller female cavities. Lower STj amplitude in women was striking, associated with smaller ventricles, but also more superior and posterior cardiac position. Post-MI, T wave amplitude and R axis deviations were more strongly associated with posterior and horizontal cardiac positioning in women than in men. Our study highlights the need to quantify sex differences in anatomical features, their implications in ECG interpretation, and the application of clinical ECG thresholds in post-MI.

physics.med-ph↗

arXiv2025

AngioDG: Interpretable Channel-informed Feature-modulated Single-source Domain Generalization for Coronary Vessel Segmentation in X-ray Angiography

Cardiovascular diseases are the leading cause of death globally, with X-ray Coronary Angiography (XCA) as the gold standard during real-time cardiac interventions. Segmentation of coronary vessels from XCA can facilitate downstream quantitative assessments, such as measurement of the stenosis severity and enhancing clinical decision-making. However, developing generalizable vessel segmentation models for XCA is challenging due to variations in imaging protocols and patient demographics that cause domain shifts. These limitations are exacerbated by the lack of annotated datasets, making Single-source Domain Generalization (SDG) a necessary solution for achieving generalization. Existing SDG methods are largely augmentation-based, which may not guarantee the mitigation of overfitting to augmented or synthetic domains. We propose a novel approach, ``AngioDG", to bridge this gap by channel regularization strategy to promote generalization. Our method identifies the contributions of early feature channels to task-specific metrics for DG, facilitating interpretability, and then reweights channels to calibrate and amplify domain-invariant features while attenuating domain-specific ones. We evaluate AngioDG on 6 x-ray angiography datasets for coronary vessels segmentation, achieving the best out-of-distribution performance among the compared methods, while maintaining consistent in-domain test performance.

cs.CV↗

arXiv2024

DeepCA: Deep Learning-based 3D Coronary Artery Tree Reconstruction from Two 2D Non-simultaneous X-ray Angiography Projections

Cardiovascular diseases (CVDs) are the most common cause of death worldwide. Invasive x-ray coronary angiography (ICA) is one of the most important imaging modalities for the diagnosis of CVDs. ICA typically acquires only two 2D projections, which makes the 3D geometry of coronary vessels difficult to interpret, thus requiring 3D coronary artery tree reconstruction from two projections. State-of-the-art approaches require significant manual interactions and cannot correct the non-rigid cardiac and respiratory motions between non-simultaneous projections. In this study, we propose a novel deep learning pipeline named \emph{DeepCA}. We leverage the Wasserstein conditional generative adversarial network with gradient penalty, latent convolutional transformer layers, and a dynamic snake convolutional critic to implicitly compensate for the non-rigid motion and provide 3D coronary artery tree reconstruction. Through simulating projections from coronary computed tomography angiography (CCTA), we achieve the generalisation of 3D coronary tree reconstruction on real non-simultaneous ICA projections. We incorporate an application-specific evaluation metric to validate our proposed model on both a CCTA dataset and a real ICA dataset, together with Chamfer $\ell_2$ distance. The results demonstrate promising performance of our DeepCA model in vessel topology preservation, recovery of missing features, and generalisation ability to real ICA data. To the best of our knowledge, this is the first study that leverages deep learning to achieve 3D coronary tree reconstruction from two real non-simultaneous x-ray angiographic projections.

eess.IV↗