Atrial fibrillation (AF) and ventricular tachycardia (VT) affect millions of patients in the United States. These arrhythmias can be cured with catheter ablation, but the arrhythmias often recur, and these recurrences are generally due to reversible conduction block from incomplete ablation. The inability to confirm the presence of completely ablated lesions in the desired locations is the major factor in the greater than 40% recurrence of VT after ablation, and the greater than 30 % recurrence of AF after ablation. In addition, it is not possible with current technology to adequately predict the pathways of VT through scar, which are the targets for ablation. The overall goal of this project is to combine high resolution Magnetic Resonance Imaging (MRI) and limited invasive mapping, with fast computational modeling, to predict arrhythmia circuits and targets for ablation. This goal includes using this technology to update ablation targets during a procedure to allow for identification and ablation of any remaining arrhythmogenic substrate as ablation is proceeding. We hypothesize that computational modeling, optimized with high-resolution MRI, and limited invasive mapping, can (1) aid in predicting the locations of arrhythmia circuits (2) aid in predicting the locations of critical ablation targets, and (3) aid in assessing the completeness of ablation. Once validated, these enhanced capabilities could help to dramatically improve the outcomes from complex ablations, become part of ablation methods of the future, and become a platform for improving outcomes from other interventions. We have already developed improved high resolution imaging methods that allow accurate differentiation of infarct scar and border zone from normal tissue. This high resolution imaging may also allow for detection of conducting channels that may be present in otherwise dense scar, and which may be a critical part of some VT circuits. We are also pursuing limited invasive mapping as a means to detect the presence of late potentials in scar to aid in the detection and/or verification of conducting channels, which may be difficult to identify with current MRI methods. We will further improve high resolution imaging for input for a computational model that along with the detection and/or confirmation of conduction channels from invasive mapping, will predict arrhythmia circuit locations, and allow the fast and accurate determination of optimal targets for ablation. In addition, since the model can be run in near real time, and since we can perform intra-procedure MRI, we will also study the use of the computational model for predicting when additional ablation is needed to complete the ablation of all arrhythmogenic substrate. Finally, we have developed imaging methods that differentiate incompletely ablated (reversibly damaged) tissue from completely ablated (necrotic) tissue. If ablation of some lesions is found to be incomplete during a procedure, additional ablation can be performed to complete the ablation, and likely substantially reduce arrhythmia recurrences. This project is a collaboration between the Johns Hopkins University (High Resolution MRI, invasive mapping), and Siemens (computational modeling).

Public Health Relevance

Rhythm abnormalities of the heart, where the heart beats too fast, affect millions of people in the United States. These abnormalities can cause substantial symptoms and/or death, and some can be cured by cauterizing (ablating) a small portion of the heart. Methods are being developed to use the real-time, and high quality imaging of Magnetic Resonance Imaging (MRI) with limited invasive electrogram mapping and computational modeling, to improve the identification of arrhythmia circuits, to improve the targeting of ablations, to assess the completeness of ablation, and extend these methods to other organ systems.

Agency
National Institute of Health (NIH)
Institute
National Institute of Biomedical Imaging and Bioengineering (NIBIB)
Type
Research Project (R01)
Project #
5R01EB022011-02
Application #
9789881
Study Section
Biomedical Imaging Technology Study Section (BMIT)
Program Officer
Duan, Qi
Project Start
2018-09-30
Project End
2022-06-30
Budget Start
2019-07-01
Budget End
2020-06-30
Support Year
2
Fiscal Year
2019
Total Cost
Indirect Cost
Name
Johns Hopkins University
Department
Internal Medicine/Medicine
Type
Schools of Medicine
DUNS #
001910777
City
Baltimore
State
MD
Country
United States
Zip Code
21205