title: Accuracy of prediction of infarct-related arrhythmic circuits from image-based models reconstructed from low and high resolution MRI.
abstract: Identification of optimal ablation sites in hearts with infarct-related ventricular tachycardia (VT) remains difficult to achieve with the current catheter-based mapping techniques. Limitations arise from the ambiguities in determining the reentrant pathways location(s). The goal of this study was to develop experimentally validated, individualized computer models of infarcted swine hearts, reconstructed from high-resolution ex-vivo MRI and to examine the accuracy of the reentrant circuit location prediction when models of the same hearts are instead reconstructed from low clinical-resolution MRI scans. To achieve this goal, we utilized retrospective data obtained from four pigs ~10 weeks post infarction that underwent VT induction via programmed stimulation and epicardial activation mapping via a multielectrode epicardial sock. After the experiment, high-resolution ex-vivo MRI with late gadolinium enhancement was acquired. The Hi-res images were downsampled into two lower resolutions (Med-res and Low-res) in order to replicate image quality obtainable in the clinic. The images were segmented and models were reconstructed from the three image stacks for each pig heart. VT induction similar to what was performed in the experiment was simulated. Results of the reconstructions showed that the geometry of the ventricles including the infarct could be accurately obtained from Med-res and Low-res images. Simulation results demonstrated that induced VTs in the Med-res and Low-res models were located close to those in Hi-res models. Importantly, all models, regardless of image resolution, accurately predicted the VT morphology and circuit location induced in the experiment. These results demonstrate that MRI-based computer models of hearts with ischemic cardiomyopathy could provide a unique opportunity to predict and analyze VT resulting for from specific infarct architecture, and thus may assist in clinical decisions to identify and ablate the reentrant circuit(s).
authors.affiliation: Department of Biomedical Engineering, Institute for Computational Medicine, Johns Hopkins University Baltimore, MD, USA.
authors.forename: Dongdong
authors.initials: D
authors.lastname: Deng
authors.affiliation: Department of Biomedical Engineering, Institute for Computational Medicine, Johns Hopkins University Baltimore, MD, USA.
authors.forename: Hermenegild
authors.initials: H
authors.lastname: Arevalo
authors.affiliation: Department of Biomedical Engineering, Institute for Computational Medicine, Johns Hopkins University Baltimore, MD, USA.
authors.forename: Farhad
authors.initials: F
authors.lastname: Pashakhanloo
authors.affiliation: Department of Biomedical Engineering, Institute for Computational Medicine, Johns Hopkins University Baltimore, MD, USA.
authors.forename: Adityo
authors.initials: A
authors.lastname: Prakosa
authors.affiliation: Division of Cardiology, Department of Medicine, Johns Hopkins Medical Institute Baltimore, MD, USA ; Department of Biomedical Engineering, Johns Hopkins University Baltimore, MD, USA.
authors.forename: Hiroshi
authors.initials: H
authors.lastname: Ashikaga
authors.affiliation: Department of Biomedical Engineering, Johns Hopkins University Baltimore, MD, USA.
authors.forename: Elliot
authors.initials: E
authors.lastname: McVeigh
authors.affiliation: Division of Cardiology, Department of Medicine, Johns Hopkins Medical Institute Baltimore, MD, USA.
authors.forename: Henry
authors.initials: H
authors.lastname: Halperin
authors.affiliation: Department of Biomedical Engineering, Institute for Computational Medicine, Johns Hopkins University Baltimore, MD, USA.
authors.forename: Natalia
authors.initials: N
authors.lastname: Trayanova
keywords: MRI reconstruction; arrhythmia; computational modeling; myocardial infarction; reentry