Whole Heart Mesh Generation For Image-Based Computational Simulations By Learning Free-From Deformations

Abstract

Image-based computer simulation of cardiac function can be used to probe the mechanisms of (patho)physiology, and guide diagnosis and personalized treatment of cardiac diseases. This paradigm requires constructing simulation-ready meshes of cardiac structures from medical image data-a process that has traditionally required significant time and human effort, limiting large-cohort analyses and potential clinical translations. We propose a novel deep learning approach to reconstruct simulation-ready whole heart meshes from volumetric image data. Our approach learns to deform a template mesh to the input image data by predicting displacements of multi-resolution control point grids. We discuss the methods of this approach and demonstrate its application to efficiently create simulation-ready whole heart meshes for computational fluid dynamics simulations of the cardiac flow. Our source code is available at https://github.com/fkong7/HeartFFDNet.

Computational Methods for Fluid-Structure Interaction Simulation of Heart Valves in Patient-Specific Left Heart Anatomies

“…One main challenge in patient-specific modeling for the left heart is the dynamic nature of the geometry Γ LV . With the advance of machine learning algorithms, various groups [139] have proposed an automated process for delineating the LV geometrical deformation for hemodynamic simulations [140]. In addition, recent developments in ultrasound techniques [70,141] and 4D CT scans [142] can provide higher temporal and spatial resolutions for Γ LV dynamics as well as the intraventricular flows.…”