Deep Learning Meets Computational Fluid Dynamics to Assess CAD in CCTA

Early diagnosis and effective monitoring of the coronary artery disease are critical in ensuring its effective treatment. Although there are established invasive examinations to assess this condition, the current research focus is put on non-invasive procedures. Here, the coronary computed tomography angiography is the first-choice modality, but its manual analysis is cost-inefficient, lacks reproducibility, and suffers from significant inter- and intra-rater disagreement. We tackle those issues and introduce an end-to-end deep learning-powered pipeline for automated analysis of such imagery which additionally exploits computational fluid dynamics to capture the functional vessel characteristics. Our experiments, performed over clinically acquired scans, revealed that the suggested segmentation approaches not only outperform state-of-the-art nnU-Nets, but also lead to the blood-flow parameters which are in strong agreement with those elaborated for the ground-truth delineations.

Automated summary

Early diagnosis and effective monitoring of coronary artery disease are crucial for successful treatment. This study introduces an automated deep learning-powered pipeline for the analysis of coronary computed tomography angiography images, with the additional use of computational fluid dynamics to capture vessel characteristics. Experimental results show that this approach outperforms existing methods and produces blood-flow parameters strongly correlated to ground-truth delineations.