Chen Chun-Cheng R, Miga Michael I, Galloway Robert L
Department of Biomedical Engineering, Vanderbilt University, 5824 Stevenson Center Nashville, Tennessee 37235, USA.
Med Phys. 2007 Oct;34(10):4030-40. doi: 10.1118/1.2761978.
In radiofrequency ablation (RFA), successful therapy requires accurate, image-guided placement of the ablation device in a location selected by a predictive treatment plan. Current planning methods rely on geometric models of ablations that are not sensitive to underlying physical processes in RFA. Implementing plans based on computational models of RFA with image-guided techniques, however, has not been well characterized. To study the use of computational models of RFA in planning needle placement, this work compared ablations performed with an optically tracked RFA device with corresponding models of the ablations. The calibration of the tracked device allowed the positions of distal features of the device, particularly the tips of the needle electrodes, to be determined to within 1.4 +/- 0.6 mm of uncertainty. Ablations were then performed using the tracked device in a phantom system based on an agarose-albumin mixture. Images of the sliced phantom obtained from the ablation experiments were then compared with the predictions of a bioheat transfer model of RFA, which used the positional data of the tracked device obtained during ablation. The model was demonstrated to predict 90% of imaged pixels classified as being ablated. The discrepancies between model predictions and observations were analyzed and attributed to needle tracking inaccuracy as well as to uncertainties in model parameters. The results suggest the feasibility of using finite element modeling to plan ablations with predictable outcomes when implemented using tracked RFA.
在射频消融（RFA）中，成功的治疗需要在预测性治疗计划所选定的位置，通过图像引导精确放置消融设备。当前的规划方法依赖于消融的几何模型，而这些模型对RFA中的潜在物理过程并不敏感。然而，将基于RFA计算模型的计划与图像引导技术相结合的做法，尚未得到充分的描述。为了研究RFA计算模型在规划针放置中的应用，本研究将使用光学跟踪RFA设备进行的消融与相应的消融模型进行了比较。跟踪设备的校准使得能够确定设备远端特征的位置，特别是针电极尖端的位置，其不确定性在1.4 +/- 0.6毫米以内。然后，在基于琼脂糖-白蛋白混合物的体模系统中，使用跟踪设备进行消融。从消融实验获得的切片体模图像随后与RFA生物热传递模型的预测结果进行比较，该模型使用了消融过程中获得的跟踪设备的位置数据。结果表明，该模型能够预测90%被分类为已消融的成像像素。对模型预测与观察结果之间的差异进行了分析，并归因于针跟踪不准确以及模型参数的不确定性。结果表明，当使用跟踪RFA实施时，使用有限元建模来规划具有可预测结果的消融是可行的。
