Shape-driven deep neural networks for fast acquisition of aortic 3D pressure and velocity flow fields.

ABSTRACT: Computational fluid dynamics (CFD) can be used to simulate vascular haemodynamics and analyse potential treatment options. CFD has shown to be beneficial in improving patient outcomes. However, the implementation of CFD for routine clinical use is yet to be realised. Barriers for CFD include high computational resources, specialist experience needed for designing simulation set-ups, and long processing times. The aim of this study was to explore the use of machine learning (ML) to replicate conventional aortic CFD with automatic and fast regression models. Data used to train/test the model consisted of 3,000 CFD simulations performed on synthetically generated 3D aortic shapes. These subjects were generated from a statistical shape model (SSM) built on real patient-specific aortas (N = 67)

SUBMITTER: Pajaziti E 

PROVIDER: S-EPMC10159343 | biostudies-literature | 2023 Apr

REPOSITORIES: biostudies-literature