Deep Learning Surrogate of Computational Fluid Dynamics for Thrombus Formation Risk in the Left Atrial Appendage

Xabier Morales, Jordi Mill, Kristine Aavild Juhl, Andy Olivares, Guillermo Jimenez-Perez, Rasmus Reinhold Paulsen, Oscar Camara

Research output: Chapter in Book/Report/Conference proceedingArticle in proceedingsResearchpeer-review

Abstract

Recently, the risk of thrombus formation in the left atrium (LA) has been assessed through patient-specific computational fluid dynamic (CFD) simulations, characterizing the complex 4D nature of blood flow in the left atrial appendage (LAA). Nevertheless, the vast computational resources and long computing times required by traditional CFD methods prevents its embedding in the clinical workflow of time-sensitive applications. In this study, two distinct deep learning (DL) architectures have been developed to receive the patient-specific LAA geometry as an input and predict the endothelial cell activation potential (ECAP), which is linked to the risk of thrombosis. The first network is based on a simple fully-connected network, while the latter also performs a dimensionality reduction of the variables. Both models have been trained with a synthetic dataset of 210 LAA geometries being able to accurately predict the ECAP distributions with an average error of 4.72% for the fully-connected approach and 5.75% for its counterpart. Most importantly, the obtention of the ECAP predictions was quasi-instantaneous, orders of magnitude faster than conventional CFD.
Original languageEnglish
Title of host publicationStatistical Atlases and Computational Models of the Heart. Multi-Sequence CMR Segmentation, CRT-EPiggy and LV Full Quantification Challenges.
PublisherSpringer
Publication date2020
Pages157-166
ISBN (Print)978-3-030-39073-0
DOIs
Publication statusPublished - 2020
Event10th Workshop on Statistical Atlases and Computational Modelling of the Heart - InterContinental Shenzhen, Shenzhen, China
Duration: 13 Oct 201913 Oct 2019

Conference

Conference10th Workshop on Statistical Atlases and Computational Modelling of the Heart
LocationInterContinental Shenzhen
CountryChina
CityShenzhen
Period13/10/201913/10/2019
SeriesLecture Notes in Computer Science
Volume12009
ISSN0302-9743

Keywords

  • Deep learning
  • Computational Fluid Dynamics
  • Thrombus formation
  • Hemodynamics
  • Left Atrial Appendage

Cite this

Morales, X., Mill, J., Juhl, K. A., Olivares, A., Jimenez-Perez, G., Paulsen, R. R., & Camara, O. (2020). Deep Learning Surrogate of Computational Fluid Dynamics for Thrombus Formation Risk in the Left Atrial Appendage. In Statistical Atlases and Computational Models of the Heart. Multi-Sequence CMR Segmentation, CRT-EPiggy and LV Full Quantification Challenges. (pp. 157-166). Springer. Lecture Notes in Computer Science, Vol.. 12009 https://doi.org/10.1007/978-3-030-39074-7_17