I propose to develop a highly innovative patient-specific MRI-based heart modeling environment that represents cardiac functions from molecular processes to electrophysiological and electromechanical interactions at the organ level. I term this environment """"""""virtual electrophysiology lab"""""""", and propose to translate it into th clinic and apply it to the non-invasive diagnosis and treatment of heart rhythm and contractile disorders in patients with structural heart disease. This pioneering effort offers to integrate, fo the first time, computational modeling of the heart, traditionally a basic-science discipline, withn the milieu of contemporary patient care. The robust and inexpensive non-invasive approaches for individualized arrhythmia risk stratification and guidance of electrophysiological therapies proposed here will lead to optimized therapy delivery and reduction in health care costs, and will have a dramatic personal, medical and economic impact on society. This project seeks to shift the paradigm of cardiac patient care by utilizing the virtual electrophysiology laboratory environment in three applications pertinent to patients with myocardial infarction: 1. Noninvasive prediction of the optimal ablation targets for infarct-related ventricular tachycardia. The vital electrophysiology lab will be used to accurately identify the optimal targets of ablation in each patient heart non-invasively prior to the clinical procedure. Delivery of ablation will then be swit and precise, eradicating all infarct-related ventricular tachycardias with minimum lesion sizes. This will result in a dramatic improvement in the efficacy of and tolerance for the therapy, as wel as in reduction of post-procedure complications. 2. Arrhythmia risk assessment to determine the need for implantable defibrillator deployment. Personalized simulations of arrhythmia inducibility will be used as a noninvasive, inexpensive, and risk-free surrogate for a clinical elect

Agency
National Institute of Health (NIH)
Institute
National Heart, Lung, and Blood Institute (NHLBI)
Type
NIH Director’s Pioneer Award (NDPA) (DP1)
Project #
1DP1HL123271-01
Application #
8561487
Study Section
Special Emphasis Panel (ZRG1-BCMB-N (50))
Program Officer
Lee, Albert
Project Start
2013-09-25
Project End
2018-07-31
Budget Start
2013-09-25
Budget End
2014-07-31
Support Year
1
Fiscal Year
2013
Total Cost
$810,000
Indirect Cost
$310,000
Name
Johns Hopkins University
Department
Biostatistics & Other Math Sci
Type
Schools of Engineering
DUNS #
001910777
City
Baltimore
State
MD
Country
United States
Zip Code
21218
Aboelkassem, Yasser; Trayanova, Natalia (2018) Tropomyosin dynamics during cardiac muscle contraction as governed by a multi-well energy landscape. Prog Biophys Mol Biol :
Pashakhanloo, Farhad; Herzka, Daniel A; Halperin, Henry et al. (2018) Role of 3-Dimensional Architecture of Scar and Surviving Tissue in Ventricular Tachycardia: Insights From High-Resolution Ex Vivo Porcine Models. Circ Arrhythm Electrophysiol 11:e006131
Boyle, Patrick M; Karathanos, Thomas V; Trayanova, Natalia A (2018) Cardiac Optogenetics: 2018. JACC Clin Electrophysiol 4:155-167
Cochet, Hubert; Dubois, Rémi; Yamashita, Seigo et al. (2018) Relationship Between Fibrosis Detected on Late Gadolinium-Enhanced Cardiac Magnetic Resonance and Re-Entrant Activity Assessed With Electrocardiographic Imaging in Human Persistent Atrial Fibrillation. JACC Clin Electrophysiol 4:17-29
Dhamala, Jwala; Arevalo, Hermenegild J; Sapp, John et al. (2018) Quantifying the uncertainty in model parameters using Gaussian process-based Markov chain Monte Carlo in cardiac electrophysiology. Med Image Anal 48:43-57
Deng, Dongdong; Nikolov, Plamen; Arevalo, Hermenegild J et al. (2018) Optimal contrast-enhanced MRI image thresholding for accurate prediction of ventricular tachycardia using ex-vivo high resolution models. Comput Biol Med 102:426-432
Trayanova, Natalia A; Boyle, Patrick M; Nikolov, Plamen P (2018) Personalized Imaging and Modeling Strategies for Arrhythmia Prevention and Therapy. Curr Opin Biomed Eng 5:21-28
Deng, Dongdong; Murphy, Michael J; Hakim, Joe B et al. (2017) Sensitivity of reentrant driver localization to electrophysiological parameter variability in image-based computational models of persistent atrial fibrillation sustained by a fibrotic substrate. Chaos 27:093932
Dhamala, Jwala; Arevalo, Hermenegild J; Sapp, John et al. (2017) Spatially Adaptive Multi-Scale Optimization for Local Parameter Estimation in Cardiac Electrophysiology. IEEE Trans Med Imaging 36:1966-1978
Zile, Melanie A; Trayanova, Natalia A (2017) Myofilament protein dynamics modulate EAD formation in human hypertrophic cardiomyopathy. Prog Biophys Mol Biol 130:418-428

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