Generative neural physics enables quantitative volumetric ultrasound of tissue mechanics

• URL: http://arxiv.org/abs/2508.12226v2
• Date: Sun, 09 Nov 2025 17:56:00 GMT
• Title: Generative neural physics enables quantitative volumetric ultrasound of tissue mechanics
• Authors: Zhijun Zeng, Youjia Zheng, Chang Su, Qianhang Wu, Hao Hu, Zeyuan Dong, Shan Gao, Yang Lv, Rui Tang, Ligang Cui, Zhiyong Hou, Weijun Lin, Zuoqiang Shi, Yubing Li, He Sun,
• Abstract summary: We introduce a generative neural physics framework to produce rapid, high-fidelity 3D quantitative imaging of tissue mechanics.<n>A compact neural surrogate for full-wave propagation is trained on limited cross-modality data, preserving physical accuracy while enabling efficient inversion.<n>The resulting images reveal biomechanical features in bone, muscle, fat, and glandular tissues, maintaining structural resolution comparable to 3T MRI.
• Score: 19.276342580965217
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

Related papers

• LAYER: A Quantitative Explainable AI Framework for Decoding Tissue-Layer Drivers of Myofascial Low Back Pain [9.815323391482917]
  Myofascial pain (MP) is a leading cause of chronic low back pain.<n>Existing studies focus predominantly on muscle while neglecting fascia, fat, and other soft tissues that play integral biomechanical roles.
  arXiv  Detail & Related papers  (2025-11-25T15:47:43Z)
• Dynamic Reconstruction of Ultrasound-Derived Flow Fields With Physics-Informed Neural Fields [0.0]
  We present a physics-informed neural field model for estimating blood flow from sparse and noisy ultrasound data.<n>This model achieves consistently low mean squared error in denoising and inpainting both synthetic and real datasets.<n>We adapt methods that have proven effective in other imaging modalities to address the specific challenge of ultrasound-based flow reconstruction.
  arXiv  Detail & Related papers  (2025-11-03T18:05:11Z)
• Accelerating 3D Photoacoustic Computed Tomography with End-to-End Physics-Aware Neural Operators [74.65171736966131]
  Photoacoustic computed tomography (PACT) combines optical contrast with ultrasonic resolution, achieving deep-tissue imaging beyond the optical diffusion limit.<n>Current implementations require dense transducer arrays and prolonged acquisition times, limiting clinical translation.<n>We introduce Pano, an end-to-end physics-aware model that directly learns the inverse acoustic mapping from sensor measurements to volumetric reconstructions.
  arXiv  Detail & Related papers  (2025-09-11T23:12:55Z)
• WarpPINN-fibers: improved cardiac strain estimation from cine-MR with physics-informed neural networks [0.4349640169711269]
  We introduce WarpPINN-fibers, a physics-informed neural network framework to accurately obtain cardiac motion and strains enhanced by fiber information.<n>We build a loss function composed of three terms: a data-similarity loss between the reference and the warped template images, a regularizer enforcing near-incompressibility of cardiac tissue and a fiber-stretch penalization that controls strain in the direction of synthetically produced fibers.
  arXiv  Detail & Related papers  (2025-09-10T14:53:31Z)
• 3D Wavelet Latent Diffusion Model for Whole-Body MR-to-CT Modality Translation [13.252652406393205]
  Existing MR-to-CT methods for whole-body imaging often suffer from poor spatial alignment between the generated CT and input MR images.<n>We present a novel 3D Wavelet Latent Diffusion Model (3D-WLDM) that addresses these limitations.<n>By incorporating a Wavelet Residual Module into the encoder-decoder architecture, we enhance the capture and reconstruction of fine-scale features across image and latent spaces.
  arXiv  Detail & Related papers  (2025-07-14T06:17:05Z)
• Physics-informed neural network estimation of active material properties in time-dependent cardiac biomechanical models [2.8763745263714005]
  This work investigates the application of physics-informed neural networks (PINNs) for inferring active contractility parameters in time-dependent cardiac biomechanical models.<n>We are able to reconstruct in various settings active stress fields in the presence of noise and with a high spatial resolution.<n>This approach opens a new pathway to significantly improve the diagnosis, treatment planning, and management of heart conditions associated with cardiac fibrosis.
  arXiv  Detail & Related papers  (2025-05-06T10:01:16Z)
• ZECO: ZeroFusion Guided 3D MRI Conditional Generation [11.645873358288648]
  ZECO is a ZeroFusion guided 3D MRI conditional generation framework.<n>It extracts, compresses, and generates high-fidelity MRI images with corresponding 3D segmentation masks.<n>ZECO outperforms state-of-the-art models in both quantitative and qualitative evaluations on Brain MRI datasets.
  arXiv  Detail & Related papers  (2025-03-24T00:04:52Z)
• MRI Reconstruction with Regularized 3D Diffusion Model (R3DM) [2.842800539489865]
  We propose a 3D MRI reconstruction method that leverages a regularized 3D diffusion model combined with optimization method.<n>By incorporating diffusion based priors, our method improves image quality, reduces noise, and enhances the overall fidelity of 3D MRI reconstructions.
  arXiv  Detail & Related papers  (2024-12-25T00:55:05Z)
• TomoGRAF: A Robust and Generalizable Reconstruction Network for Single-View Computed Tomography [3.1209855614927275]
  Traditional analytical/iterative CT reconstruction algorithms require hundreds of angular data samplings. We develop a novel TomoGRAF framework incorporating the unique X-ray transportation physics to reconstruct high-quality 3D volumes.
  arXiv  Detail & Related papers  (2024-11-12T20:07:59Z)
• Denoising Diffusion Models for 3D Healthy Brain Tissue Inpainting [3.9347915104376168]
  Monitoring diseases that affect the brain's structural integrity requires automated analysis of magnetic resonance (MR) images. We modify state-of-the-art 2D, pseudo-3D, and 3D methods working in the image space, as well as 3D latent and 3D wavelet diffusion models, and train them to synthesize healthy brain tissue. Our evaluation shows that the pseudo-3D model performs best regarding the structural-similarity index, peak signal-to-noise ratio, and mean squared error.
  arXiv  Detail & Related papers  (2024-03-21T15:52:05Z)
• Real-Time Model-Based Quantitative Ultrasound and Radar [65.268245109828]
  We propose a neural network based on the physical model of wave propagation, which defines the relationship between the received signals and physical properties. Our network can reconstruct multiple physical properties in less than one second for complex and realistic scenarios.
  arXiv  Detail & Related papers  (2024-02-16T09:09:16Z)
• Style transfer between Microscopy and Magnetic Resonance Imaging via Generative Adversarial Network in small sample size settings [45.62331048595689]
  Cross-modal augmentation of Magnetic Resonance Imaging (MRI) and microscopic imaging based on the same tissue samples is promising.<n>We tested a method for generating microscopic histological images from MRI scans of the human corpus callosum using conditional generative adversarial network (cGAN) architecture.
  arXiv  Detail & Related papers  (2023-10-16T13:58:53Z)
• AiAReSeg: Catheter Detection and Segmentation in Interventional Ultrasound using Transformers [75.20925220246689]
  endovascular surgeries are performed using the golden standard of Fluoroscopy, which uses ionising radiation to visualise catheters and vasculature. This work proposes a solution using an adaptation of a state-of-the-art machine learning transformer architecture to detect and segment catheters in axial interventional Ultrasound image sequences.
  arXiv  Detail & Related papers  (2023-09-25T19:34:12Z)
• Deep learning network to correct axial and coronal eye motion in 3D OCT retinal imaging [65.47834983591957]
  We propose deep learning based neural networks to correct axial and coronal motion artifacts in OCT based on a single scan. The experimental result shows that the proposed method can effectively correct motion artifacts and achieve smaller error than other methods.
  arXiv  Detail & Related papers  (2023-05-27T03:55:19Z)
• Towards Autonomous Atlas-based Ultrasound Acquisitions in Presence of Articulated Motion [48.52403516006036]
  This paper proposes a vision-based approach allowing autonomous robotic US limb scanning. To this end, an atlas MRI template of a human arm with annotated vascular structures is used to generate trajectories. In all cases, the system can successfully acquire the planned vascular structure on volunteers' limbs.
  arXiv  Detail & Related papers  (2022-08-10T15:39:20Z)
• Learning Optimal K-space Acquisition and Reconstruction using Physics-Informed Neural Networks [46.751292014516025]
  Deep neural networks have been applied to reconstruct undersampled k-space data and have shown improved reconstruction performance. This work proposes a novel framework to learn k-space sampling trajectories by considering it as an Ordinary Differential Equation (ODE) problem. Experiments were conducted on different in-viv datasets (textite.g., brain and knee images) acquired with different sequences.
  arXiv  Detail & Related papers  (2022-04-05T20:28:42Z)
• 4D iterative reconstruction of brain fMRI in the moving fetus [1.8492120771993543]
  The accuracy of the proposed method was quantitatively evaluated on a group of real clinical fMRI fetuses. The results indicate improvements of reconstruction quality compared to the conventional 3D approach.
  arXiv  Detail & Related papers  (2021-11-22T18:12:21Z)
• Deep Learning for Ultrasound Beamforming [120.12255978513912]
  Beamforming, the process of mapping received ultrasound echoes to the spatial image domain, lies at the heart of the ultrasound image formation chain. Modern ultrasound imaging leans heavily on innovations in powerful digital receive channel processing. Deep learning methods can play a compelling role in the digital beamforming pipeline.
  arXiv  Detail & Related papers  (2021-09-23T15:15:21Z)
• ShuffleUNet: Super resolution of diffusion-weighted MRIs using deep learning [47.68307909984442]
  Single Image Super-Resolution (SISR) is a technique aimed to obtain high-resolution (HR) details from one single low-resolution input image. Deep learning extracts prior knowledge from big datasets and produces superior MRI images from the low-resolution counterparts.
  arXiv  Detail & Related papers  (2021-02-25T14:52:23Z)
• Microvascular Dynamics from 4D Microscopy Using Temporal Segmentation [81.30750944868142]
  We are able to track changes in cerebral blood volume over time and identify spontaneous arterial dilations that propagate towards the pial surface. This new imaging capability is a promising step towards characterizing the hemodynamic response function upon which functional magnetic resonance imaging (fMRI) is based.
  arXiv  Detail & Related papers  (2020-01-14T22:55:03Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.