Automating Blood Flow Simulation Through the Aorta in Patient-specific CT Images

Abstract

Computational fluid dynamics (CFD) modeling of blood flow is significant for obtaining patient-specific hemodynamics information for functional assessment of the cardiovascular system.In this work, we present a framework for fully automatic CFD simulation through the aorta. The proposed framework consists of four main stages: (1) automatic segmentation of the aorta, (2) model generation, (3) mesh creation, and (4) blood flow simulation. In the segmentation part, we utilized a 3D MultiResUnet network for automatic segmentation of organs at risk from the CodaLab SegThor Challenge. After that, we extract ascending and descending aorta and further proceed with model and mesh generation. Finally, we simulate the pressure along the surface of the aorta, the displacement, and the velocity. The entire framework 1 was implemented in Python with open-sourced dependencies (Pytorch, VTK, SimVascular, SimpleITK), can be executed from the command line, and does not require user intervention, significantly reducing aorta simulation time.

Machine Learning Empowering Personalized Medicine: A Comprehensive Review of Medical Image Analysis Methods

“…Beyond clinical diagnosis and treatment, cardiac imaging plays a pivotal role in advancing cardiovascular research. It enables scientists to explore cardiac functions, blood flow dynamics [211], and the interaction between different heart chambers [158][159][160]. This deeper understanding of cardiac physiology and pathology helps researchers uncover the mechanisms behind heart-related conditions and develop innovative treatment approaches.…”