2025 Theses Doctoral

Development of optical and electroanatomic methods for improved ablation of cardiac arrhythmias

Majumder, Jonah A.

Cardiac arrhythmias represent a major cause of morbidity and mortality worldwide, contributing substantially to hospitalizations, healthcare costs, and reduced quality of life. Catheter ablation is a common treatment for atrial and ventricular arrhythmias, yet long-term success is limited by inability to reliably assess lesion formation and challenges identifying arrhythmogenic substrates. This dissertation addresses these challenges through the development and validation of optical and computational methods for cardiac tissue characterization and mapping.

The first portion of this work focuses on catheter-based near-infrared spectroscopy (NIRS) as a means to assess tissue composition and radiofrequency ablation (RFA) efficacy. Monte Carlo simulations are used to characterize diffuse reflectance and elucidate how NIRS probes underlying tissue composition. Informed by these results, custom NIRS-equipped catheters are fabricated and tested on porcine and human cardiac tissue to demonstrate localization of ablation lesions, epicardial adipose tissue, and epicardial coronary vessels. Results show that NIRS measurements retain sensitivity in the presence of ambient blood and at oblique catheter angles. Large animal validation studies are then used to assess the performance of catheter-based NIRS in vivo. Ongoing work extends measurements to diseased human hearts, establishing a pipeline for histologic labeling of spectra and developing algorithms for NIRS-based structural substrate mapping. Together, this work affirms the ability of NIRS to capture clinically relevant biochemical and structural features of cardiac tissue, representing a significant step towards its implementation to improve the safety and efficacy of catheter ablation.

The second portion of this work advances quantitative electroanatomic mapping (EAM) to enhance interpretation of arrhythmias and targeting of ablation. A novel catheter-agnostic framework is developed to compute omnipolar conduction velocity, activation direction, and voltage from arbitrarily positioned electrodes and validated in a series of scar-related reentrant atrial tachycardias. Building upon this framework, a vector estimation, resampling, and smoothing algorithm (VERSA) is introduced to generate data-driven, informative maps of wavefront propagation that reveal local conduction patterns relevant to ablation target selection. These VERSA maps clearly illustrate circuits in reentrant arrhythmias, overcoming key limitations of existing visualization methods. Finally, a convolutional neural network is trained on intracardiac electrograms to demonstrate the feasibility of end-to-end feature extraction for critical isthmus localization. These statistical, data-driven approaches may facilitate more accurate, operator-independent identification of ablation targets in reentrant arrhythmias.

Together, these optical and computational advances establish a unified framework for structural and functional characterization of arrhythmias and underlying cardiac tissue. This quantitative interpretation of tissue composition and electrical propagation enables precise, real-time guidance of ablation to improve treatment of cardiac arrhythmias.