Communication Dans Un Congrès Année : 2025

A deep learning-assisted hybrid model for electric dosimetry in electroporation therapies

Résumé

Accurate electric dosimetry is essential for predicting treatment outcomes in Irreversible Electroporation (IRE), a promising non-thermal tumor ablation technique. However, real-time 3D electric field computation remains computationally expensive, limiting its practical use in clinical settings. In this study, we propose a hybrid approach combining deep learning (DL)-based initialization with an iterative numerical solver to accelerate dose map calculation. Specifically, we employ a convolutional neural network (CNN) to predict basis functions, which are subsequently refined using a Biconjugate Gradient Stabilized (Bi-CGSTAB) solver to maintain physical accuracy. We evaluate our method on data from 10 patients undergoing IRE liver ablation under real-time clinical conditions. Our results demonstrate a 10× speedup compared to conventional solvers while maintaining comparable accuracy. This hybrid method offers a promising pathway toward fast and reliable electric dosimetry for on-line IRE procedures.

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Dates et versions

hal-05060101 , version 1 (07-05-2025)

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  • HAL Id : hal-05060101 , version 1

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Kylian Desier, Luc Lafitte, Laurent Facq, Olivier Sutter, Clair Poignard, et al.. A deep learning-assisted hybrid model for electric dosimetry in electroporation therapies. CBMS 2025 - International Symposium on Computer-Based Medical Systems, IEEE, Jun 2025, Madrid, Spain. pp.1-4. ⟨hal-05060101⟩
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