利用波聚焦质量作为引导，从反射矩阵中重建复杂介质的波速分布。
Physics-Based Learning of the Wave Speed Landscape in Complex Media
- 将波传播建模为可训练的多层网络，结合优化与深度学习反演波速分布。
- 在组织模拟体和人乳腺组织上验证，可有效检测和表征肿瘤。
- 适用于任意可测反射矩阵的波与介质，突破传统反射成像局限。
波速是成像复杂介质的关键参数，但临床测量通常仅限于反射几何，只能获取短尺度非均质性产生的背向散射波，导致传统反射成像无法恢复大尺度波速变化。本文提出矩阵成像方法，利用波聚焦质量作为内在导星，克服该限制。我们将波传播建模为可训练的多层网络，借助优化与深度学习工具推断波速分布。通过超声实验在组织模拟体及人乳腺组织上验证，结果表明该方法具备肿瘤检测与表征潜力。本方法适用于任何可测量反射矩阵的波与介质。
原文摘要 · Abstract (English)
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.
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