用于声速估计的脉冲回波超声方法：综述
Pulse-Echo Ultrasound Methods for Speed-of-Sound Estimation: A Review
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中文总结 AI 辅助
本文综述了脉冲回波超声声速估计方法，按信息来源等分类，涵盖多种技术，总结其在乳腺等领域的诊断应用并探讨挑战与可能性，以解决传统方法临床可用性局限。
中文摘要 AI 辅助
声速（SoS）是超声波束形成的基础，也是用于组织表征的极具潜力的定量生物标志物。尽管基于透射和反射体的方法已证实SoS的诊断价值，但它们需要专用硬件或受限的采集几何结构，限制了临床可用性。脉冲回波SoS成像通过使用常规超声探头和数据采集范式估计SoS，解决了这些局限。本综述对脉冲回波SoS估计方法进行结构化概述，按所用信息来源、估计的SoS表示及重建策略分类。我们还按局部性（如全局、分层和空间分辨预测）及预测策略（包括无梯度搜索、基于优化的解决方案、解析闭式近似和基于深度学习的方法）对这些方法进行综述。最后，我们总结了其在乳腺、肝脏和肌肉中的新兴诊断应用，同时探讨了SoS成像面临的挑战与可能性。
英文摘要
Speed of sound (SoS) is fundamental to ultrasound beamforming and a promising quantitative biomarker for tissue characterization. Although transmission- and reflector-based methods have demonstrated the diagnostic value of SoS, their need for dedicated hardware or constrained acquisition geometries limits clinical usability. Pulse-echo SoS imaging addresses these limitations by estimating SoS with conventional ultrasound probes and data acquisition paradigms. This review provides a structured overview of pulse-echo SoS estimation methods, categorized by the source of information used, the estimated SoS representation, and the reconstruction strategy. We review methods by their locality, e.g., global, layer-wise, and spatially-resolved predictions, as well as in terms of prediction strategies including gradient-free search, optimization-based solutions, analytical closed-form approximations, and deep learning-based methods. Finally, we summarize emerging diagnostic applications in the breast, liver, and muscle, while discussing challenges and possibilities with SoS imaging.
