用生成式神经物理模型实现快速高精度肌肉骨骼超声成像。
Ultrasound Tomography of Musculoskeletal Tissues with Generative Neural Physics
- 结合生成网络与物理约束的神经模拟，替代传统耗时的波场反演。
- 十分钟内完成3D组织参数重建，分辨率接近MRI，可检测微弱声学差异。
- 适合需无辐射、高分辨率定量成像的临床研究者及影像算法开发者。
超声断层扫描（UT）是一种无辐射、高分辨率的成像方式，但在肌肉骨骼成像中受限于强散射介质下全波形反演的高计算成本和不稳定性。本文提出一种生成式神经物理框架，将生成网络与物理信息神经模拟相结合，实现快速、高保真度的三维超声断层成像。通过从有限跨模态图像中学习超声波传播的紧凑代理模型，该方法融合了波动建模的准确性与深度学习的效率和稳定性。在乳房、手臂和腿部的合成数据及活体数据上，可在十分钟内重建组织参数的三维分布图，对肌肉骨骼组织中的声学变化敏感，分辨率与MRI相当。该方法克服了强散射环境下计算瓶颈，验证了定量超声断层扫描在肌肉骨骼成像中的可行性，并推动其向未来临床常规应用迈进。
原文摘要 · Abstract (English)
Ultrasound Tomography (UT) is a radiation-free, high-resolution modality, but remains limited for musculoskeletal imaging due to the high computational cost and instability of full-waveform inversion in strongly scattering media. We propose a generative neural physics framework that couples generative networks with physics-informed neural simulation for fast, high-fidelity 3D UT. By learning a compact surrogate of ultrasonic wave propagation from a limited set of cross-modality images, our method merges the accuracy of wave modeling with the efficiency and stability of deep learning. This enables accurate quantitative imaging of in vivo musculoskeletal tissues, producing spatial maps of acoustic properties beyond reflection-mode images. On synthetic and in vivo data of breasts, arms, and legs, we reconstruct 3D maps of tissue parameters in under ten minutes, with sensitivity to acoustic variations in musculoskeletal tissues and resolution comparable to MRI. By overcoming computational bottlenecks in strongly scattering regimes, this approach demonstrates the feasibility of quantitative UT for musculoskeletal imaging and advances its development toward future routine clinical use.
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