Tissue electroporation is the basis of several therapies. Electroporation is performed by briefly exposing tissues to high electric fields. It is generally accepted that electroporation is effective where an electric field magnitude threshold is overreached. However, it is difficult to preoperatively estimate the field distribution because it is highly dependent on anatomy and treatment parameters. OBJECTIVE: We developed PIRET, a platform to predict the treatment volume in electroporation-based therapies. METHODS: The platform seamlessly integrates tools to build patient-specific models where the electric field is simulated to predict the treatment volume. Patient anatomy is segmented from medical images and 3D reconstruction aids in placing the electrodes and setting up treatment parameters. RESULTS: Four canine patients that had been treated with high-frequency irreversible electroporation were retrospectively planned with PIRET and with a workflow commonly used in previous studies, which uses different general-purpose segmentation (3D Slicer) and modeling software (3Matic and COMSOL Multiphysics). PIRET outperformed the other workflow by 65 minutes (Ã 1.7 faster), thanks to the improved user experience during treatment setup and model building. Both approaches computed similarly accurate electric field distributions, with average Dice scores higher than 0.93. CONCLUSION: A platform which integrates all the required tools for electroporation treatment planning is presented. Treatment plan can be performed rapidly with minimal user interaction in a stand-alone platform. SIGNIFICANCE: This platform is, to the best of our knowledge, the most complete software for treatment planning of irreversible electroporation. It can potentially be used for other electroporation applications.
PIRET-A Platform for Treatment Planning in Electroporation-Based Therapies.
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作者:Perera-Bel Enric, Aycock Kenneth N, Salameh Zaid S, Gomez-Barea Mario, Davalos Rafael V, Ivorra Antoni, Ballester Miguel A Gonzalez
| 期刊: | IEEE Transactions on Biomedical Engineering | 影响因子: | 4.500 |
| 时间: | 2023 | 起止号: | 2023 Jun;70(6):1902-1910 |
| doi: | 10.1109/TBME.2022.3232038 | ||
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