Radiofrequency ablation (RFA) is a widely used minimally invasive technique for ablating solid tumors. Achieving precise personalized treatment necessitates feedback information on in situ thermal effects induced by the RFA procedure. While computer simulation facilitates the prediction of electrical and thermal phenomena associated with RFA, its practical implementation in clinical settings is hindered by high computational demands. In this paper, we propose a physics-guided neural network model, named PhysRFANet, to enable real-time prediction of thermal effect during RFA treatment. The networks, designed for predicting temperature distribution and the corresponding ablation lesion, were trained using biophysical computational models that integrated electrostatics, bio-heat transfer, and cell necrosis, alongside magnetic resonance (MR) images of breast cancer patients. Validation of the computational model was performed through experiments on ex vivo bovine liver tissue. Our model demonstrated a 96% Dice score in predicting the lesion volume and an RMSE of 0.4854 for temperature distribution when tested with foreseen tumor images. Notably, even with unforeseen images, it achieved a 93% Dice score for the ablation lesion and an RMSE of 0.6783 for temperature distribution. All networks were capable of inferring results within 10 ms. The presented technique, applied to optimize the placement of the electrode for a specific target region, holds significant promise in enhancing the safety and efficacy of RFA treatments.

翻译：射频消融（RFA）是一种广泛用于实体肿瘤消融的微创技术。实现精确的个性化治疗需要RFA过程中原位热效应的反馈信息。虽然计算机模拟有助于预测与RFA相关的电热现象，但其在临床实践中的实际应用受到高计算需求的限制。本文提出一种名为PhysRFANet的物理引导神经网络模型，以实现RFA治疗过程中热效应的实时预测。该网络设计用于预测温度分布及相应的消融病灶，训练数据源自整合静电学、生物热传递和细胞坏死模型的生物物理计算模型，并结合乳腺癌患者的磁共振（MR）图像。计算模型通过离体牛肝组织实验进行了验证。在已知肿瘤图像测试中，模型预测病灶体积的Dice得分为96%，温度分布的均方根误差（RMSE）为0.4854。值得注意的是，即使在未知图像输入下，其消融病灶预测Dice得分仍达93%，温度分布RMSE为0.6783。所有网络均能在10毫秒内完成结果推理。本文提出的技术应用于优化特定靶区的电极布放，在提升RFA治疗安全性与有效性方面具有重要潜力。