Lesion modeling, characterization, and visualization for image-guided cardiac ablation therapy monitoring

Cristian A. Linte; Jon J. Camp; Maryam E. Rettmann; Dieter Haemmerich; Mehmet K. Aktas; David T. Huang; Douglas L. Packer; David R. Holmes III

doi:10.1117/1.JMI.5.2.021218

1 March 2018 Lesion modeling, characterization, and visualization for image-guided cardiac ablation therapy monitoring

Cristian A. Linte, Jon J. Camp, Maryam E. Rettmann, Dieter Haemmerich, Mehmet K. Aktas, David T. Huang, Douglas L. Packer, David R. Holmes III

Author Affiliations +

Journal of Medical Imaging, Vol. 5, Issue 2, 021218 (March 2018). https://doi.org/10.1117/1.JMI.5.2.021218

Abstract

In spite of significant efforts to improve image-guided ablation therapy, a large number of patients undergoing ablation therapy to treat cardiac arrhythmic conditions require repeat procedures. The delivery of insufficient thermal dose is a significant contributor to incomplete tissue ablation, in turn leading to the arrhythmia recurrence. Ongoing research efforts aim to better characterize and visualize RF delivery to monitor the induced tissue damage during therapy. Here, we propose a method that entails modeling and visualization of the lesions in real-time. The described image-based ablation model relies on classical heat transfer principles to estimate tissue temperature in response to the ablation parameters, tissue properties, and duration. The ablation lesion quality, geometry, and overall progression are quantified on a voxel-by-voxel basis according to each voxel’s cumulative temperature and time exposure. The model was evaluated both numerically under different parameter conditions, as well as experimentally, using ex vivo bovine tissue samples undergoing ex vivo clinically relevant ablation protocols. The studies demonstrated less than 5°C difference between the model-predicted and experimentally measured end-ablation temperatures. The model predicted lesion patterns were within 0.5 to 1 mm from the observed lesion patterns, suggesting sufficiently accurate modeling of the ablation lesions. Lastly, our proposed method enables therapy delivery feedback with no significant workflow latency. This study suggests that the proposed technique provides reasonably accurate and sufficiently fast visualizations of the delivered ablation lesions.

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Cristian A. Linte, Jon J. Camp, Maryam E. Rettmann, Dieter Haemmerich, Mehmet K. Aktas, David T. Huang, Douglas L. Packer, and David R. Holmes III "Lesion modeling, characterization, and visualization for image-guided cardiac ablation therapy monitoring," Journal of Medical Imaging 5(2), 021218 (1 March 2018). https://doi.org/10.1117/1.JMI.5.2.021218

Received: 19 September 2017; Accepted: 2 February 2018; Published: 1 March 2018

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