Back to articles
Article
Volume: 35 | Article ID: COIMG-171
Image
Ultrasound elasticity reconstruction with inaccurate forward model using integrated data-driven correction of data-fidelity gradient
  DOI :  10.2352/EI.2023.35.14.COIMG-171  Published OnlineJanuary 2023
Abstract
Abstract

Ultrasound elasticity images, which enable the visualization of quantitative maps of tissue stiffness, can be reconstructed by solving an inverse problem. Classical model-based approaches for ultrasound elastography use deterministic finite element methods (FEMs) to incorporate the governing physical laws leading to poor performance in low SNR conditions. Moreover, these approaches utilize approximate linear forward models discretized by FEMs to describe the underlying physics governed by partial differential equations (PDEs). To achieve highly accurate stiffness images, it is essential to compensate the error induced by noisy measurements and inaccurate forward models. In this regard, we propose a joint model-based and learning-based framework for estimating the elasticity distribution by solving a regularized optimization problem. To address noise, we introduce a statistical representation of the imaging system, which incorporates the noise statistics as a signal-dependent correlated noise model. Moreover, in order to compensate for the model errors, we introduce an explicit data-driven correction model, which can be integrated with any regularization term. This constrained optimization problem is solved using fixed-point gradient descent where the analytical gradient of the inaccurate data-fidelity term is corrected using a neural network, while regularization is achieved by data-driven unrolled regularization by denoising (RED). Both networks are jointly trained in an end-to-end manner.

Subject Areas :
Views 95
Downloads 45
 articleview.views 95
 articleview.downloads 45
  Cite this article 

Narges Mohammadi, Marvin M. Doyley, Mujdat Cetin, "Ultrasound elasticity reconstruction with inaccurate forward model using integrated data-driven correction of data-fidelity gradientin Electronic Imaging,  2023,  pp 171--1 - 171-6,  https://doi.org/10.2352/EI.2023.35.14.COIMG-171

 Copy citation
  Copyright statement 
Copyright © 2023, Society for Imaging Science and Technology 2023
ei
Electronic Imaging
2470-1173
2470-1173
Society for Imaging Science and Technology
IS&T 7003 Kilworth Lane, Springfield, VA 22151 USA