Whole Heart Mesh Generation for Image-Based Computational Simulations by Learning Free-From Deformations

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.

Automated summary

The study introduces a novel deep learning approach to reconstruct simulation-ready whole heart meshes from volumetric image data, aiming to efficiently create meshes for computational fluid dynamics simulations of cardiac flow.