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Proceedings Paper

Computational modeling of radiofrequency ablation: evaluation on ex vivo data using ultrasound monitoring
Author(s): Chloé Audigier; Younsu Kim; Austin Dillow; Emad M. Boctor
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Paper Abstract

Radiofrequency ablation (RFA) is the most widely used minimally invasive ablative therapy for liver cancer, but it is challenged by a lack of patient-specific monitoring. Inter-patient tissue variability and the presence of blood vessels make the prediction of the RFA difficult. A monitoring tool which can be personalized for a given patient during the intervention would be helpful to achieve a complete tumor ablation. However, the clinicians do not have access to such a tool, which results in incomplete treatment and a large number of recurrences. Computational models can simulate the phenomena and mechanisms governing this therapy. The temperature evolution as well as the resulted ablation can be modeled. When combined together with intraoperative measurements, computational modeling becomes an accurate and powerful tool to gain quantitative understanding and to enable improvements in the ongoing clinical settings. This paper shows how computational models of RFA can be evaluated using intra-operative measurements. First, simulations are used to demonstrate the feasibility of the method, which is then evaluated on two ex vivo datasets. RFA is simulated on a simplified geometry to generate realistic longitudinal temperature maps and the resulted necrosis. Computed temperatures are compared with the temperature evolution recorded using thermometers, and with temperatures monitored by ultrasound (US) in a 2D plane containing the ablation tip. Two ablations are performed on two cadaveric bovine livers, and we achieve error of 2.2 °C on average between the computed and the thermistors temperature and 1.4 °C and 2.7 °C on average between the temperature computed and monitored by US during the ablation at two different time points (t = 240 s and t = 900 s).

Paper Details

Date Published: 3 March 2017
PDF: 10 pages
Proc. SPIE 10135, Medical Imaging 2017: Image-Guided Procedures, Robotic Interventions, and Modeling, 1013525 (3 March 2017); doi: 10.1117/12.2254986
Show Author Affiliations
Chloé Audigier, Johns Hopkins Univ. (United States)
Younsu Kim, Johns Hopkins Univ. (United States)
Austin Dillow, Johns Hopkins Univ. (United States)
Emad M. Boctor, Johns Hopkins Univ. (United States)

Published in SPIE Proceedings Vol. 10135:
Medical Imaging 2017: Image-Guided Procedures, Robotic Interventions, and Modeling
Robert J. Webster III; Baowei Fei, Editor(s)

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