Percutaneous cryoablation has become a popular alternative to open surgery for the treatment of abdominal tumors. The preoperative planning of such interventions is an essential but complicated task. It consists in predicting the best placement for several cryoprobes to optimize the resulting iceball shape, that has to cover the whole tumor, while preserving healthy tissue and surrounding sensitive structures. In the past few years, methods have been proposed to simulate the propagation of cold within the tissue, in order to anticipate the final coverage. However, all the proposed models considered the source of cold as limited to the active tip of the cryoprobe, thus omitting a residual freezing along the probe’s body. The lack of precision of the resulting models can cause an underestimation of the predicted iceball leading to potential damages to healthy tissue or pain. In this paper, we describe the extension of an existing freezing simulation model to account for this effect. We detail the experimentation of our model on 5 retrospective cases, and demonstrate the improvement of the accuracy and realism of our simulation.
|Original language||American English|
|Title of host publication||Medical Image Computing and Computer Assisted Intervention – MICCAI 2019 - 22nd International Conference, Proceedings|
|Editors||Dinggang Shen, Pew-Thian Yap, Tianming Liu, Terry M. Peters, Ali Khan, Lawrence H. Staib, Caroline Essert, Sean Zhou|
|Publisher||Springer Science and Business Media Deutschland GmbH|
|Number of pages||9|
|State||Published - 2019|
|Event||22nd International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2019 - Shenzhen, China|
Duration: 13 Oct 2019 → 17 Oct 2019
|Name||Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)|
|Conference||22nd International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2019|
|Period||13/10/19 → 17/10/19|
Bibliographical noteFunding Information:
Acknowledgments. This work was partially supported by a grant from the Mai-monide France-Israel Research in Biomedical Robotics, funded jointly by the French Ministry of Higher Education, Research and Innovation, the French Ministry for the Economy and Finance, and Israel Ministry of Science, Technology and Space, 2016–18, and by Grant 53681 (METASEG) from the Israel Ministry of Science, Technology and Space, 2016–2019.
© 2019, Springer Nature Switzerland AG.