2021/07/22 by Kong, Fanwei, Shadden, Shawn C. · 2 citations
#Computational Engineering #FOS: Computer and information sciences #FOS: Electrical engineering #FOS: Physical sciences #Finance #Image and Video Processing (eess.IV) #Medical Physics (physics.med-ph) #and Science (cs.CE) #electronic engineering #information engineering
paper · doi:10.48550/arxiv.2107.10839
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.