Computational Heart Modeling for Evaluating Efficacy of MRI Techniques in Predicting Appropriate ICD Therapy
The objective of this study is to use individualized heart computer models in evaluating efficacy of myocardial infarct (MI) mass determined by two different MRI techniques in predicting patient risk for post-MI ventricular tachycardia (VT). 27 patients with MI underwent late gadolinium-enhanced MRI using inversion-recovery fast gradient echo (IR-FGRE) and multi-contrast late enhancement (MCLE) prior to implantable cardioverter defibrillators (ICD) implantation and were followed up for 6–46 months. The myocardium, MI core (IC), and border zone (BZ) were segmented from the images using previously validated techniques. The segmented structures were then reconstructed as a high-resolution label map in 3D. Individualized image-based computational models were built separately for each imaging technique; simulations of propensity to VT were conducted with each model. The imaging methods were evaluated for sensitivity and specificity by comparing simulated inducibility of VT to clinical outcome (appropriate ICD therapy) in patients. Twelve patients had at least one appropriate ICD therapy for VT at follow-up. For both MCLE and IR-FGRE, the outcomes of the simulations of VT were significantly different between the groups with and without ICD therapy. Between the IR-FGRE and MCLE, the virtual models built using the latter may have yielded higher sensitivity and specificity in predicting appropriate ICD therapy.
KeywordsT1 mapping Computational modeling Myocardial infarct
- 7.Vadakkumpadan, F., Arevalo, H., Jebb, A., Wu, K.C., Trayanova, N.: Image-based patient-specific simulations of ventricular electrophysiology for sudden arrhythmic death risk stratification. In: Circulation, vol. 128, no. 22 (2013)Google Scholar
- 8.Ukwatta, E., et al.: Myocardial infarct segmentation from magnetic resonance images for personalized modeling of cardiac electrophysiology. IEEE TMI 35(6), 1408–1419 (2016)Google Scholar
- 9.Detsky, J.S., Paul, G., Dick, A.J., Wright, G.A.: Reproducible classification of infarct heterogeneity using fuzzy clustering on multicontrast delayed enhancement magnetic resonance images. IEEE TMI 28(10), 1606–1614 (2009)Google Scholar