8 March 2013 Image-based modeling and characterization of RF ablation lesions in cardiac arrhythmia therapy
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Abstract
In spite of significant efforts to enhance guidance for catheter navigation, limited research has been conducted to consider the changes that occur in the tissue during ablation as means to provide useful feedback on the progression of therapy delivery. We propose a technique to visualize lesion progression and monitor the effects of the RF energy delivery using a surrogate thermal ablation model. The model incorporates both physical and physiological tissue parameters, and uses heat transfer principles to estimate temperature distribution in the tissue and geometry of the generated lesion in near real time. The ablation model has been calibrated and evaluated using ex vivo beef muscle tissue in a clinically relevant ablation protocol. To validate the model, the predicted temperature distribution was assessed against that measured directly using fiberoptic temperature probes inserted in the tissue. Moreover, the model-predicted lesions were compared to the lesions observed in the post-ablation digital images. Results showed an agreement within 5°C between the model-predicted and experimentally measured tissue temperatures, as well as comparable predicted and observed lesion characteristics and geometry. These results suggest that the proposed technique is capable of providing reasonably accurate and sufficiently fast representations of the created RF ablation lesions, to generate lesion maps in near real time. These maps can be used to guide the placement of successive lesions to ensure continuous and enduring suppression of the arrhythmic pathway.
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Cristian A. Linte, Cristian A. Linte, Jon J. Camp, Jon J. Camp, Maryam E. Rettmann, Maryam E. Rettmann, David R. Holmes, David R. Holmes, Richard A. Robb, Richard A. Robb, } "Image-based modeling and characterization of RF ablation lesions in cardiac arrhythmia therapy", Proc. SPIE 8671, Medical Imaging 2013: Image-Guided Procedures, Robotic Interventions, and Modeling, 86710E (8 March 2013); doi: 10.1117/12.2008529; https://doi.org/10.1117/12.2008529
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