17 February 2012 Automated microwave ablation therapy planning with single and multiple entry points
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Abstract
Microwave ablation (MWA) has become a recommended treatment modality for interventional cancer treatment. Compared with radiofrequency ablation (RFA), MWA provides more rapid and larger-volume tissue heating. It allows simultaneous ablation from different entry points and allows users to change the ablation size by controlling the power/time parameters. Ablation planning systems have been proposed in the past, mainly addressing the needs for RFA procedures. Thus a planning system addressing MWA-specific parameters and workflows is highly desirable to help physicians achieve better microwave ablation results. In this paper, we design and implement an automated MWA planning system that provides precise probe locations for complete coverage of tumor and margin. We model the thermal ablation lesion as an ellipsoidal object with three known radii varying with the duration of the ablation and the power supplied to the probe. The search for the best ablation coverage can be seen as an iterative optimization problem. The ablation centers are steered toward the location which minimizes both un-ablated tumor tissue and the collateral damage caused to the healthy tissue. We assess the performance of our algorithm using simulated lesions with known "ground truth" optimal coverage. The Mean Localization Error (MLE) between the computed ablation center in 3D and the ground truth ablation center achieves 1.75mm (Standard deviation of the mean (STD): 0.69mm). The Mean Radial Error (MRE) which is estimated by comparing the computed ablation radii with the ground truth radii reaches 0.64mm (STD: 0.43mm). These preliminary results demonstrate the accuracy and robustness of the described planning algorithm.
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Sheena Xin Liu, Sandeep Dalal, Jochen Kruecker, "Automated microwave ablation therapy planning with single and multiple entry points", Proc. SPIE 8316, Medical Imaging 2012: Image-Guided Procedures, Robotic Interventions, and Modeling, 831635 (17 February 2012); doi: 10.1117/12.911316; https://doi.org/10.1117/12.911316
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