Generative neural physics enables quantitative volumetric ultrasound of tissue mechanics
This work addresses the bottleneck of full-wave scattering models in ultrasound tomography for in-vivo biomechanical assessment, enabling rapid quantitative imaging of tissue mechanics in diseases like breast cancer or musculoskeletal disorders, though it appears incremental as it builds on existing physics-informed and generative methods.
The authors tackled the problem of efficiently and accurately quantifying tissue mechanical properties using ultrasound tomography by introducing a generative neural physics framework that fuses generative models with physics-informed PDE solvers. This resulted in accurate and efficient 3D quantitative imaging of in vivo human breast and musculoskeletal tissues in under ten minutes, providing spatial maps of tissue mechanical properties with structural resolution comparable to 3T MRI and greater sensitivity to disease-related mechanics.
Tissue mechanics--stiffness, density and impedance contrast--are broadly informative biomarkers across diseases, yet routine CT, MRI, and B-mode ultrasound rarely quantify them directly. While ultrasound tomography (UT) is intrinsically suited to in-vivo biomechanical assessment by capturing transmitted and reflected wavefields, efficient and accurate full-wave scattering models remain a bottleneck. Here, we introduce a generative neural physics framework that fuses generative models with physics-informed partial differential equation (PDE) solvers to produce rapid, high-fidelity 3D quantitative imaging of tissue mechanics. A compact neural surrogate for full-wave propagation is trained on limited cross-modality data, preserving physical accuracy while enabling efficient inversion. This enables, for the first time, accurate and efficient quantitative volumetric imaging of in vivo human breast and musculoskeletal tissues in under ten minutes, providing spatial maps of tissue mechanical properties not available from conventional reflection-mode or standard UT reconstructions. The resulting images reveal biomechanical features in bone, muscle, fat, and glandular tissues, maintaining structural resolution comparable to 3T MRI while providing substantially greater sensitivity to disease-related tissue mechanics.