Abstract：
BACKGROUND: Monitoring minimally invasive thermo ablation procedures using magnetic resonance (MR) thermometry allows therapy of tumors even close to critical anatomical structures. Unfortunately, intraoperative monitoring remains challenging due to the necessary accuracy and real-time capability. One reason for this is the statistical error introduced by MR measurement, which causes the prediction of ablation zones to become inaccurate.
OBJECTIVE: In this work, we derive a probabilistic model for the prediction of ablation zones during thermal ablation procedures based on the thermal damage model CEM43 . By integrating the statistical error caused by MR measurement into the conventional prediction, we hope to reduce the amount of falsely classified voxels.
METHODS: The probabilistic CEM43 model is empirically evaluated using a polyacrilamide gel phantom and three in-vivo pig livers.
RESULTS: The results show a higher accuracy in three out of four data sets, with a relative difference in Sørensen-Dice coefficient from - 3.04 % $-3.04\\%$ to 3.97% compared to the conventional model. Furthermore, the ablation zones predicted by the probabilistic model show a false positive rate with a relative decrease of 11.89%-30.04% compared to the conventional model.
CONCLUSIONS: The presented probabilistic thermal dose model might help to prevent false classification of voxels within ablation zones. This could potentially result in an increased success rate for MR-guided thermal ablation procedures. Future work may address additional error sources and a follow-up study in a more realistic clinical context.
OBJECTIVE: In this work, we derive a probabilistic model for the prediction of ablation zones during thermal ablation procedures based on the thermal damage model CEM43 . By integrating the statistical error caused by MR measurement into the conventional prediction, we hope to reduce the amount of falsely classified voxels.
METHODS: The probabilistic CEM43 model is empirically evaluated using a polyacrilamide gel phantom and three in-vivo pig livers.
RESULTS: The results show a higher accuracy in three out of four data sets, with a relative difference in Sørensen-Dice coefficient from - 3.04 % $-3.04\\%$ to 3.97% compared to the conventional model. Furthermore, the ablation zones predicted by the probabilistic model show a false positive rate with a relative decrease of 11.89%-30.04% compared to the conventional model.
CONCLUSIONS: The presented probabilistic thermal dose model might help to prevent false classification of voxels within ablation zones. This could potentially result in an increased success rate for MR-guided thermal ablation procedures. Future work may address additional error sources and a follow-up study in a more realistic clinical context.
摘要：
背景：使用磁共振（MR）测温法监测微创热消融程序可以治疗甚至接近关键解剖结构的肿瘤。不幸的是,由于必要的准确性和实时性,术中监测仍然具有挑战性.其中一个原因是MR测量引入的统计误差，这导致消融区的预测变得不准确。
目的：在这项工作中，我们基于热损伤模型CEM43推导了热消融过程中消融区预测的概率模型。通过将MR测量引起的统计误差集成到常规预测中，我们希望减少错误分类的体素的数量。
方法：使用聚丙烯酰胺凝胶模型和三个体内猪肝对概率CEM43模型进行了经验评估。
结果：结果显示，四个数据集中的三个数据具有更高的准确性，与常规模型相比，Sørensen-Dice系数的相对差异从-3.04%$-3.04\\%$到3.97%。此外，与传统模型相比,概率模型预测的消融区假阳性率相对降低11.89%-30.04%.
结论：所提出的概率热剂量模型可能有助于防止消融区内体素的错误分类。这可能潜在地导致MR引导的热消融程序的成功率增加。未来的工作可能会在更现实的临床背景下解决其他错误源和后续研究。
目的：在这项工作中，我们基于热损伤模型CEM43推导了热消融过程中消融区预测的概率模型。通过将MR测量引起的统计误差集成到常规预测中，我们希望减少错误分类的体素的数量。
方法：使用聚丙烯酰胺凝胶模型和三个体内猪肝对概率CEM43模型进行了经验评估。
结果：结果显示，四个数据集中的三个数据具有更高的准确性，与常规模型相比，Sørensen-Dice系数的相对差异从-3.04%$-3.04\\%$到3.97%。此外，与传统模型相比,概率模型预测的消融区假阳性率相对降低11.89%-30.04%.
结论：所提出的概率热剂量模型可能有助于防止消融区内体素的错误分类。这可能潜在地导致MR引导的热消融程序的成功率增加。未来的工作可能会在更现实的临床背景下解决其他错误源和后续研究。
