AI 中文总结
本文提出结合2D U-Net初至分割与混合全波形反演的学习辅助流程，解决初至拾取不可靠问题，在多数据集上验证其能提升初至质量并稳定FWI重建。
AI 中文摘要
全波形反演（FWI）是定量肌肉骨骼超声计算机层析成像（USCT）的有前景策略，但骨相关散射、衰减和信号衰减使其对初始声学属性分布的准确性高度敏感，且易出现周期跳跃。初至走时为初始模型构建提供重要运动学信息，但传统逐道拾取在初至信号弱、空间异质或被系统噪声掩盖时不可靠。本文提出一种学习辅助重建流程，将基于分割的初至提取与混合全波形反演（HFWI）相结合，该流程在反演早期阶段结合基于Rytov近似的走时信息与波形拟合。轻量级2D U-Net将接收通道间的初至轨迹视为初至分割目标，利用其空间连续性而非逐道处理。为解决人工标注有限及模拟到真实的 gap，网络在经真实系统噪声记录增强的任务特定模拟上预训练，随后通过逐步增加信号衰减的分阶段训练，以及使用有限弱标记实验数据的仅解码器微调完成训练。该方法在体外体模、离体牛肢体和活体人大腿数据集上评估，与传统STA/LTA拾取相比，所提网络产生更具空间一致性的初至轨迹、更低的平均提取误差，且能在数秒内处理全矩阵采集数据集。当集成到HFWI中时，提取的初至改善了初始模型构建，并实现稳定的后续FWI重建，包括估计局部初至信噪比低于3 dB的具有挑战性案例。
英文摘要
Full-waveform inversion (FWI) is a promising strategy for quantitative musculoskeletal ultrasound computed tomography (USCT), but bone-related scattering, attenuation, and signal degradation make it highly sensitive to the accuracy of the initial acoustic-property distributions and prone to cycle skipping. First-arrival traveltimes provide important kinematic information for initial-model construction, yet conventional trace-wise picking is unreliable when arrivals are weak, spatially heterogeneous, or buried in system noise. We propose a learning-assisted reconstruction pipeline that combines segmentation-based first-arrival extraction with hybrid full-waveform inversion (HFWI), which incorporates Rytov-approximation-based traveltime information together with waveform fitting during the early inversion stage. A lightweight 2D U-Net treats the first-arrival trajectory across receiver channels as a first-break segmentation target and exploits its spatial continuity rather than processing each trace independently. To address both limited manual annotations and the simulation-to-real gap, the network is pretrained on task-specific simulations augmented with real system-noise recordings, followed by stage-wise training with progressively increased signal degradation and decoder-only fine-tuning using limited weakly labeled experimental data. The method is evaluated on in vitro phantom, ex vivo bovine-limb, and in vivo human-thigh datasets. Compared with conventional STA/LTA picking, the proposed network yields more spatially coherent first-arrival trajectories, lower mean extraction errors, and processes a full-matrix-capture dataset within seconds. When integrated into HFWI, the extracted arrivals improve initial-model construction and lead to stable subsequent FWI reconstructions, including challenging cases with estimated local first-arrival SNRs below 3 dB.
