Medical physics · 2023
A probabilistic thermal dose model for the estimation of necrosis in MR-guided tumor ablations.
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Schröer Simon, Alpers Julian, Gutberlet Marcel, Brüsch Inga, Rumpel Regina, Wacker Frank, Hensen Bennet, Hansen Christian
Аннотация
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.
PURPOSE: 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.
CONCLUSION: 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.
PURPOSE: 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.
CONCLUSION: 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.