TY - GEN

T1 - Deep Learning Surrogate of Computational Fluid Dynamics for Thrombus Formation Risk in the Left Atrial Appendage

AU - Morales, Xabier

AU - Mill, Jordi

AU - Juhl, Kristine Aavild

AU - Olivares, Andy

AU - Jimenez-Perez, Guillermo

AU - Paulsen, Rasmus Reinhold

AU - Camara, Oscar

PY - 2020

Y1 - 2020

N2 - 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.

AB - 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.

KW - Deep learning

KW - Computational Fluid Dynamics

KW - Thrombus formation

KW - Hemodynamics

KW - Left Atrial Appendage

U2 - 10.1007/978-3-030-39074-7_17

DO - 10.1007/978-3-030-39074-7_17

M3 - Article in proceedings

SN - 978-3-030-39073-0

T3 - Lecture Notes in Computer Science

SP - 157

EP - 166

BT - Statistical Atlases and Computational Models of the Heart. Multi-Sequence CMR Segmentation, CRT-EPiggy and LV Full Quantification Challenges.

PB - Springer

T2 - 10th Workshop on Statistical Atlases and Computational Modelling of the Heart

Y2 - 13 October 2019 through 13 October 2019

ER -