发表机构
School of Mathematics and Computing (Computational Science and Engineering), Yonsei University; Innovative & Intelligent Computational Science Institute (IN2CSI)（延世大学数学与计算科学学院（计算科学与工程）; 创新与智能计算科学研究所）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该研究提出首个基于算子的tFUS声场预测模型tFUSOperator，其定位声焦点精度高、速度远快于数值模拟，为患者特异性tFUS治疗数字孪生提供了无辐射的快速方案。
AI 中文摘要
经颅聚焦超声（tFUS）需要准确估算颅内声场，但颅骨会引发像差导致声场畸变。数值求解器精度高，但对于数字孪生而言计算成本高昂，因为当治疗条件改变时必须反复重新估算声场。现有深度学习替代模型速度快，但通常采用固定网格上的体素到体素回归，无法反映声能穿过颅骨的传播机制。我们将tFUS模拟转化为算子学习问题，提出tFUSOperator，一种坐标感知神经算子，它将自由场压力、颅骨解剖结构和治疗参数映射到共享物理坐标系内的颅内声场。据我们所知，这是首个基于算子的tFUS声场预测方案。在已见过和未见过的颅骨上，该模型均能准确定位声焦点，Dice系数分别达到约90%和72%；从磁共振（MR）输入获取的结果与从计算机断层扫描（CT）输入获取的结果性能几乎相当，且运行速度比数值模拟快5.6×10⁴倍。这些结果为安全实用的患者特异性tFUS治疗数字孪生提供了快速、无辐射的途径。代码可在指定网址获取。
英文摘要
Transcranial focused ultrasound (tFUS) requires accurate estimation of the intracranial acoustic field, which is distorted by skull-induced aberrations. Numerical solvers are accurate but computationally expensive for digital twins, where the field must be re-estimated repeatedly as treatment conditions change. Existing deep-learning surrogates are fast but typically use voxel-to-voxel regression on a fixed grid, with no mechanism reflecting how acoustic energy propagates through the skull. We instead cast tFUS simulation as an operator learning problem and propose tFUSOperator, a coordinate-aware neural operator that maps the free-field pressure, skull anatomy, and treatment parameters to the intracranial field within a shared physical coordinate frame. To our knowledge, this is the first operator-based formulation of tFUS field prediction. On both seen and unseen skulls, the model localizes the acoustic focus accurately-reaching about 90% and 72% Dice, respectively-and it performs nearly as well from magnetic resonance (MR) as from computed tomography (CT) input while running $5.6 \times 10^4$ times faster than numerical simulation. These results suggest a fast, radiation-free route to safe and practical digital twins for patient-specific tFUS treatment. The code is available at: https://github.com/CMME-Lab/tFUSOperator.git.
CommentsPublished at Digital Twin for Healthcare (MICCAI 2026 Workshop)
