基于物理的学习复杂介质中的波速景观
Physics-Based Learning of the Wave Speed Landscape in Complex Media
AI总结：
本文提出基于物理的矩阵成像方法，利用波聚焦质量推断复杂介质中的波速分布，用于肿瘤检测与表征。
摘要：
波速是成像复杂介质的关键参数，但在体内测量通常仅限于反射几何构型，其中只能获取来自短尺度异质性的背向散射波。因此，传统反射成像无法恢复波速景观的大尺度变化。本文展示矩阵成像通过利用波聚焦质量作为内在导星来克服这一限制。我们将波传播建模为可训练的多层网络，利用优化和深度学习工具来推断波速分布。我们通过超声实验在组织模拟假体和人类乳腺组织上验证了这种方法，展示了其在肿瘤检测和表征中的潜力。我们的方法广泛适用于任何种类的波和介质，只要可以测量反射矩阵。
英文摘要：
Wave velocity is a key parameter for imaging complex media, but in vivo measurements are typically limited to reflection geometries, where only backscattered waves from short-scale heterogeneities are accessible. As a result, conventional reflection imaging fails to recover large-scale variations of the wave velocity landscape. Here we show that matrix imaging overcomes this limitation by exploiting the quality of wave focusing as an intrinsic guide star. We model wave propagation as a trainable multi-layer network that leverages optimization and deep learning tools to infer the wave velocity distribution. We validate this approach through ultrasound experiments on tissue-mimicking phantoms and human breast tissues, demonstrating its potential for tumour detection and characterization. Our method is broadly applicable to any kind of waves and media for which a reflection matrix can be measured.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
