Toward Enabling Cardiac Digital Twins of Myocardial Infarction Using Deep Computational Models for Inverse Inference

• Published in: IEEE transactions on medical imaging Vol. 43; no. 7; pp. 2466 - 2478
• Main Authors: Li, Lei, Camps, Julia, Jenny Wang, Zhinuo, Beetz, Marcel, Banerjee, Abhirup, Rodriguez, Blanca, Grau, Vicente
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.07.2024; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Cardiac digital twins; cardiac MRI; Computational modeling; Computer applications; Computer Simulation; Deep Learning; Digital twins; EKG; Electrocardiography; Electrocardiography - methods; electrophysiology; Heart; Heart - diagnostic imaging; Heart attacks; Humans; Image reconstruction; Inference; inverse problem; Inverse problems; Magnetic resonance imaging; Magnetic Resonance Imaging - methods; Mathematical models; Modal data; multi-modal integration; Myocardial infarction; Myocardial Infarction - diagnostic imaging; Myocardial Infarction - physiopathology; Myocardium; Sensitivity analysis; Sensitivity enhancement
• ISSN: 0278-0062; 1558-254X; 1558-254X
• DOI: 10.1109/TMI.2024.3367409

Cardiac digital twins (CDTs) have the potential to offer individualized evaluation of cardiac function in a non-invasive manner, making them a promising approach for personalized diagnosis and treatment planning of myocardial infarction (MI). The inference of accurate myocardial tissue properties is crucial in creating a reliable CDT of MI. In this work, we investigate the feasibility of inferring myocardial tissue properties from the electrocardiogram (ECG) within a CDT platform. The platform integrates multi-modal data, such as cardiac MRI and ECG, to enhance the accuracy and reliability of the inferred tissue properties. We perform a sensitivity analysis based on computer simulations, systematically exploring the effects of infarct location, size, degree of transmurality, and electrical activity alteration on the simulated QRS complex of ECG, to establish the limits of the approach. We subsequently present a novel deep computational model, comprising a dual-branch variational autoencoder and an inference model, to infer infarct location and distribution from the simulated QRS. The proposed model achieves mean Dice scores of <inline-formula> <tex-math notation="LaTeX"> {0}.{457} \pm {0}.{317} </tex-math></inline-formula> and <inline-formula> <tex-math notation="LaTeX"> {0}.{302} \pm {0}.{273} </tex-math></inline-formula> for the inference of left ventricle scars and border zone, respectively. The sensitivity analysis enhances our understanding of the complex relationship between infarct characteristics and electrophysiological features. The in silico experimental results show that the model can effectively capture the relationship for the inverse inference, with promising potential for clinical application in the future. The code is available at https://github.com/lileitech/MI_inverse_inference .