# Ultrasound Elasticity Imaging Using Physics-based Models And Learning-based Plug-And-Play Priors

arXiv: https://arxiv.org/abs/2103.14096 · HTML: https://mlsanity.com/paper/2103.14096/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Mar 2021.

## Built on by (most cited first, 8 of 8)

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- [Lateral Strain Imaging using Self-supervised and Physically Inspired Constraints in Unsupervised Regularized Elastography](https://mlsanity.com/paper/2212.08740/) (2022)
- [Sparse Universum Quadratic Surface Support Vector Machine Models for Binary Classification](https://mlsanity.com/paper/2104.01331/) (2021)
- [Semantic Segmentation and Object Detection Towards Instance Segmentation: Breast Tumor Identification](https://mlsanity.com/paper/2108.03287/) (2021)
- [EYNet: Extended YOLO for Airport Detection in Remote Sensing Images](https://mlsanity.com/paper/2203.14007/) (2022)

## Abstract (arXiv)

Existing physical model-based imaging methods for ultrasound elasticity reconstruction utilize fixed variational regularizers that may not be appropriate for the application of interest or may not capture complex spatial prior information about the underlying tissues. On the other hand, end-to-end learning-based methods count solely on the training data, not taking advantage of the governing physical laws of the imaging system. Integrating learning-based priors with physical forward models for ultrasound elasticity imaging, we present a joint reconstruction framework which guarantees that learning driven reconstructions are consistent with the underlying physics. For solving the elasticity inverse problem as a regularized optimization problem, we propose a plug-and-play (PnP) reconstruction approach in which each iteration of the elasticity image estimation process involves separate updates incorporating data fidelity and learning-based regularization. In this methodology, the data fidelity term is developed using a statistical linear algebraic model of quasi-static equilibrium equation revealing the relationship of the observed displacement fields cmmntmeasured deformation data to the unobserved elastic modulus. The regularizer comprises a convolutional neural network (CNN) based denoiser that captures the learned prior structure of the underlying tissues. Preliminary simulation results demonstrate the robustness and effectiveness of the proposed approach with limited training datasets and noisy displacement measurements.