In non-pharmacological treatment of cardiac arrhythmias such as catheter ablation therapy, long-term treatment effectiveness is related in part to the quality of lesion generation. Superficial lesions may lead to arrhythmia recurrence by allowing recovery along conduction channels for arrhythmic impulses to propagate; conversely transmural lesions inhibit conduction. Conventional techniques rely on measurement of surrogate parameters such as change in bioelectrical impedance, or electrogram amplitude dampening as a qualitative assessment for lesion size. In previous work, we’ve demonstrated a relationship between lesion dimensions and spectroscopic parameters extracted using an optically-integrated ablation catheter. Though these metrics present some trend, a method to directly assess lesion transmurality maybe better suited. In this work, we report a method for direct recovery of lesion depth in cardiac tissue using diffusely reflected optical measurements and present initial in silico validation.
Photon transport throughout a heterogeneous volume was simulated for a series of source-detector pairs and optical properties using a GPU-based Monte Carlo (MC) code. Results were used to generate a multi-dimensional look-up table for each collection geometry for partial to transmural lesions. A genetic algorithm-based two-step inversion method was employed to extract lesion transmurality. MC simulated optical measurements for various lesion sizes were generated using optical properties for ablated and normal cardiac tissue found in literature and were fitted using our algorithm. Recovered lesion depths ranged between 2-10% for lesions less than 3mm and were within 20% for lesions greater than 4mm. These results support the application of this technique for lesion validation for atrial tissue.
Rajinder P. Singh-Moon and Christine P. Hendon, "Atrial lesion transmurality assessment using multi-fiber diffuse reflectance (Conference Presentation)," Proc. SPIE 10060, Optical Biopsy XV: Toward Real-Time Spectroscopic Imaging and Diagnosis, 100600R (Presented at SPIE BiOS: February 01, 2017; Published: 1 May 2017); https://doi.org/10.1117/12.2254078.5409312277001.
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