Electroanatomical mapping (EAM) is an invaluable tool for guiding cardiac radiofrequency ablation (RFA) therapy. The principle roles of EAM is the identification of candidate ablation sites by detecting regions of abnormal electrogram activity and lesion validation subsequent to RF energy delivery. However, incomplete lesions may present interim electrical inactivity similar to effective treatment in the acute setting, despite efforts to reveal them with pacing or drugs, such as adenosine. Studies report that the misidentification and recovery of such lesions is a leading cause of arrhythmia recurrence and repeat procedures. In previous work, we demonstrated spectroscopic characterization of cardiac tissues using a fiber optic-integrated RF ablation catheter. In this work, we introduce OSAM (optical spectroscopic anatomical mapping), the application of this spectroscopic technique to obtain 2-dimensional biodistribution maps. We demonstrate its diagnostic potential as an auxiliary method for lesion validation in treated swine preparations.
Endocardial lesion sets were created on fresh swine cardiac samples using a commercial RFA system. An optically-integrated catheter console fabricated in-house was used for measurement of tissue optical spectra between 600-1000nm. Three dimensional, Spatio-spectral datasets were generated by raster scanning of the optical catheter across the treated sample surface in the presence of whole blood. Tissue optical parameters were recovered at each spatial position using an inverse Monte Carlo method. OSAM biodistribution maps showed stark correspondence with gross examination of tetrazolium chloride stained tissue specimens. Specifically, we demonstrate the ability of OSAM to readily distinguish between shallow and deeper lesions, a limitation faced by current EAM techniques. These results showcase the OSAMs potential for lesion validation strategies for the treatment of cardiac arrhythmias.
Using light-based catheters for radiofrequency ablation (RFA) therapies grants the ability to accurately derive tissue
properties such as lesion depth and overtreatment from spectroscopic information. However, this information is heavily
reliant on contact quality with the treatment area and the orientation of the catheter. Thus to improve assessments of
tissue properties, this work utilizes Bayesian modelling to classify whether the catheter is indeed in proper contact with
the tissue. Initially in-laboratory experiments were conducted with ten fresh swine hearts submerged in blood. A total of
1555 unique near infrared spectra were collected from a spectrometer using a light-based catheter and manually tagged
as “full perpendicular contact,” “angled contact,” and “no contact,” between the catheter and heart tissue. Three features
were prominent in all spectra for distinguishing purposes: area underneath the spectra, an intensity “valley” between 730
nm and 800 nm, along with the slope between 850 nm and 1150 nm. A classifier featuring bootstrapping, adaboost, and
k-means techniques was thus created and achieved a 96.05% accuracy in classifying full contact, 98.33% accuracy in
classifying angled contact, and 100% accuracy in classifying no contact.
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