Abstract
Patient-specific cardiac computational models are essential for the efficient realization of precision medicine and in-silico clinical trials using digital twins. Cardiac digital twins can provide non-invasive characterizations of cardiac functions for individual patients, and therefore are promising for the patient-specific diagnosis and therapy stratification. However, current workflows for both the anatomical and functional twinning phases, referring to the inference of model anatomy and parameter from clinical data, are not sufficiently efficient, robust, and accurate. In this work, we propose a deep learning-based patient-specific computational model, which can fuse both anatomical and electrophysiological information for the inference of ventricular activation properties, i.e., conduction velocities and root nodes. The activation properties can provide a quantitative assessment of cardiac electrophysiological function for the guidance of interventional procedures. We employ the Eikonal model to generate simulated electrocardiograms (ECGs) with ground truth properties to train the inference model, where patient-specific information has also been considered. For evaluation, we test the model on the simulated data and obtain generally promising results with fast computational time.
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Acknowledgement
This research has been conducted using the UK Biobank Resource under Application Number ‘40161’. The authors express no conflict of interest. This work was funded by the CompBioMed 2 Centre of Excellence in Computational Biomedicine (European Commission Horizon 2020 research and innovation programme, grant agreement No. 823712). L. Li was partially supported by the SJTU 2021 Outstanding Doctoral Graduate Development Scholarship. A. Banerjee is a Royal Society University Research Fellow and is supported by the Royal Society Grant No. URF
. The work of A. Banerjee and V. Grau was supported by the British Heart Foundation (BHF) Project under Grant HSR01230. The work of M. Beetz was supported by the Stiftung der Deutschen Wirtschaft (Foundation of German Business).
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Li, L., Camps, J., Banerjee, A., Beetz, M., Rodriguez, B., Grau, V. (2022). Deep Computational Model for the Inference of Ventricular Activation Properties. In: Camara, O., et al. Statistical Atlases and Computational Models of the Heart. Regular and CMRxMotion Challenge Papers. STACOM 2022. Lecture Notes in Computer Science, vol 13593. Springer, Cham. https://doi.org/10.1007/978-3-031-23443-9_34
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DOI: https://doi.org/10.1007/978-3-031-23443-9_34
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