Abstract
Advances in structural finite element analysis (FEA) and medical imaging have made it possible to investigate the in vivo biomechanics of human organs such as blood vessels, for which organ geometries at the zero‐pressure level need to be recovered. Although FEA‐based inverse methods are available for zero‐pressure geometry estimation, these methods typically require iterative computation, which are time‐consuming and may be not suitable for time‐sensitive clinical applications. In this study, by using machine learning (ML) techniques, we developed an ML model to estimate the zero‐pressure geometry of human thoracic aorta given 2 pressurized geometries of the same patient at 2 different blood pressure levels. For the ML model development, a FEA‐based method was used to generate a dataset of aorta geometries of 3125 virtual patients. The ML model, which was trained and tested on the dataset, is capable of recovering zero‐pressure geometries consistent with those generated by the FEA‐based method. Thus, this study demonstrates the feasibility and great potential of using ML techniques as a fast surrogate of FEA‐based inverse methods to recover zero‐pressure geometries of human organs.
This paper presents the first machine learning (ML) approach to estimate zero‐pressure geometry of human organs, which was demonstrated for the application of the human thoracic aorta. The trained ML model takes as input 2 pressurized aorta geometries of the same patient at 2 different blood pressure levels and outputs the zero‐pressure geometry in 1 second. This approach may greatly expedite finite element analysis processes for time‐sensitive clinical applications.
