Estimation of the zero-pressure computational start shape of atherosclerotic plaques: Improving the backward displacement method with deformation gradient tensor

J Biomech. 2022 Jan:131:110910. doi: 10.1016/j.jbiomech.2021.110910. Epub 2021 Dec 17.

Abstract

Advances in medical imaging have enabled patient-specific biomechanical modelling of arterial lesions such as atherosclerosis and aneurysm. Geometry acquired from in-vivo imaging is already pressurized and a zero-pressure computational start shape needs to be identified. The backward displacement algorithm was proposed to solve this inverse problem, utilizing fixed-point iterations to gradually approach the start shape. However, classical fixed-point implementations were reported with suboptimal convergence properties under large deformations. In this paper, a dynamic learning rate guided by the deformation gradient tensor was introduced to control the geometry update. The effectiveness of this new algorithm was demonstrated for both idealized and patient-specific models. The proposed algorithm led to faster convergence by accelerating the initial steps and helped to avoid the non-convergence in large-deformation problems.

Keywords: Aneurysm; Artery; Atherosclerosis; Deformation gradient; Inverse problem; Zero-pressure.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Algorithms
  • Atherosclerosis* / diagnostic imaging
  • Humans
  • Patient-Specific Modeling
  • Plaque, Atherosclerotic* / diagnostic imaging