TY - GEN
T1 - Graph Convolutional Regression of Cardiac Depolarization from Sparse Endocardial Maps
AU - Meister, Felix
AU - Passerini, Tiziano
AU - Audigier, Chloé
AU - Lluch, Èric
AU - Mihalef, Viorel
AU - Ashikaga, Hiroshi
AU - Maier, Andreas
AU - Halperin, Henry
AU - Mansi, Tommaso
N1 - Publisher Copyright:
© 2021, Springer Nature Switzerland AG.
PY - 2021
Y1 - 2021
N2 - Electroanatomic mapping as routinely acquired in ablation therapy of ventricular tachycardia is the gold standard method to identify the arrhythmogenic substrate. To reduce the acquisition time and still provide maps with high spatial resolution, we propose a novel deep learning method based on graph convolutional neural networks to estimate the depolarization time in the myocardium, given sparse catheter data on the left ventricular endocardium, ECG, and magnetic resonance images. The training set consists of data produced by a computational model of cardiac electrophysiology on a large cohort of synthetically generated geometries of ischemic hearts. The predicted depolarization pattern has good agreement with activation times computed by the cardiac electrophysiology model in a validation set of five swine heart geometries with complex scar and border zone morphologies. The mean absolute error hereby measures 8 ms on the entire myocardium when providing 50% of the endocardial ground truth in over 500 computed depolarization patterns. Furthermore, when considering a complete animal data set with high density electroanatomic mapping data as reference, the neural network can accurately reproduce the endocardial depolarization pattern, even when a small percentage of measurements are provided as input features (mean absolute error of 7 ms with 50% of input samples). The results show that the proposed method, trained on synthetically generated data, may generalize to real data.
AB - Electroanatomic mapping as routinely acquired in ablation therapy of ventricular tachycardia is the gold standard method to identify the arrhythmogenic substrate. To reduce the acquisition time and still provide maps with high spatial resolution, we propose a novel deep learning method based on graph convolutional neural networks to estimate the depolarization time in the myocardium, given sparse catheter data on the left ventricular endocardium, ECG, and magnetic resonance images. The training set consists of data produced by a computational model of cardiac electrophysiology on a large cohort of synthetically generated geometries of ischemic hearts. The predicted depolarization pattern has good agreement with activation times computed by the cardiac electrophysiology model in a validation set of five swine heart geometries with complex scar and border zone morphologies. The mean absolute error hereby measures 8 ms on the entire myocardium when providing 50% of the endocardial ground truth in over 500 computed depolarization patterns. Furthermore, when considering a complete animal data set with high density electroanatomic mapping data as reference, the neural network can accurately reproduce the endocardial depolarization pattern, even when a small percentage of measurements are provided as input features (mean absolute error of 7 ms with 50% of input samples). The results show that the proposed method, trained on synthetically generated data, may generalize to real data.
KW - Cardiac computational modeling
KW - Deep learning
KW - Electroanatomical contact mapping
UR - http://www.scopus.com/inward/record.url?scp=85101521890&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85101521890&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-68107-4_3
DO - 10.1007/978-3-030-68107-4_3
M3 - Conference contribution
AN - SCOPUS:85101521890
SN - 9783030681067
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 23
EP - 34
BT - Statistical Atlases and Computational Models of the Heart. MandMs and EMIDEC Challenges - 11th International Workshop, STACOM 2020, Held in Conjunction with MICCAI 2020, Revised Selected Papers
A2 - Puyol Anton, Esther
A2 - Pop, Mihaela
A2 - Sermesant, Maxime
A2 - Campello, Victor
A2 - Lalande, Alain
A2 - Lekadir, Karim
A2 - Suinesiaputra, Avan
A2 - Camara, Oscar
A2 - Young, Alistair
PB - Springer Science and Business Media Deutschland GmbH
T2 - 11th International Workshop on Statistical Atlases and Computational Models of the Heart, STACOM 2020 held in Conjunction with MICCAI 2020
Y2 - 4 October 2020 through 4 October 2020
ER -