用于经颅聚焦超声中相位-振幅像差校正的少样本深度学习
Few-shot Deep Learning for Phase-Amplitude Aberration Correction in Transcranial Focused Ultrasound
- School of Mathematics and Computing (Computational Science and Engineering), Yonsei University（延世大学数学与计算科学学院（计算科学与工程方向））
- Innovative & Intelligent Computational Science Institute (IN2CSI)（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总结：
该研究针对经颅聚焦超声像差校正的实时需求，提出少样本深度代理框架，通过预训练加少量微调实现快速适配，校正精度高且速度较传统方法提升显著。
AI中文摘要：
经颅聚焦超声（transcranial focused ultrasound, tFUS）是一种非侵入性技术，可通过颅骨传递聚焦声能以实现神经调节与治疗应用。但颅骨的异质结构会产生复杂、患者特异性的相位和振幅像差，导致声聚焦偏离目标，损害治疗效果与安全性。传统时间反转（time-reversal, TR）模拟可校正这些像差，但依赖计算成本高昂的全波求解器，无法用于实时应用与迭代治疗规划。本文提出一种少样本深度代理框架，可从患者CT图像预测96元3D相控阵换能器的各元相位与振幅校正量。该框架采用几何感知编码器提取颅骨路径特征，供专用的相位分类与振幅回归分支共享，其中相位周期性通过循环期望解码处理。框架在多样颅骨几何结构上预训练，仅用10个目标点微调，无需针对患者的全模拟即可快速适配新患者。通过12个颅骨的留一交叉验证评估，其平均相位循环平均绝对误差（CMAE）为0.155 rad，振幅相对平均绝对误差（rMAE）为9.089%，焦点形心误差为0.467 mm，Dice分数为94.422%，峰值压力比为92.332%，速度约为TR模拟的2535倍。代码可在该网址获取。
英文摘要：
Transcranial focused ultrasound (tFUS) is a non-invasive technique that delivers focused acoustic energy through the skull for neuromodulation and therapeutic applications. However, the heterogeneous structure of the skull induces complex, patient-specific phase and amplitude aberrations that distort the acoustic focus and deviate it from the intended target, compromising therapeutic efficacy and safety. Conventional time-reversal (TR) simulations can correct these aberrations but rely on computationally expensive full-wave solvers, making them impractical for real-time use and iterative treatment planning. We propose a few-shot deep surrogate framework that predicts per-element phase and amplitude corrections for a 96-element 3D phased-array transducer from patient CT images. A geometry-aware encoder extracts skull-path features shared across dedicated phase classification and amplitude regression branches, where phase periodicity is handled via circular expectation decoding. The framework is pretrained on diverse skull geometries and fine-tuned with only ten target points, enabling rapid adaptation to unseen patients without full patient-specific simulation. Evaluated via leave-one-out cross-validation across 12 skulls, it achieves a mean phase CMAE of 0.155 rad and amplitude rMAE of 9.089%, a focal centroid error of 0.467 mm, Dice score of 94.422%, and peak pressure ratio of 92.332%, with an approximately 2,535 times speedup over TR simulation. The code is available at https://github.com/Minju-Seol/fewshot-tfus-correction.
