Logo
ScienceStack
Table of Contents
Fetching ...
Sign In

DenOiS: Dual-Domain Denoising of Observation and Solution in Ultrasound Image Reconstruction

Can Deniz Bezek, Orcun Goksel

Abstract

Medical imaging aims to recover underlying tissue properties, using inexact (simplified/linearized) imaging models and often from inaccurate and incomplete measurements. Analytical reconstruction methods rely on hand-crafted regularization, sensitive to noise assumptions and parameter tuning. Among deep learning alternatives, plug-and-play (PnP) approaches learn regularization while incorporating imaging physics during inference, outperforming purely data-driven methods. The performance of all these approaches, however, still strongly depends on measurement quality and imaging model accuracy. In this work, we propose DenOiS, a framework that denoises both input observations and resulting solution in their respective domains. It consists of an observation refinement strategy that corrects degraded measurements while compensating for imaging model simplifications, and a diffusion-based PnP reconstruction approach that remains robust under missing measurements. DenOiS enables generalization to real data from training only in simulations, resulting in high-fidelity image reconstruction with noisy observations and inexact imaging models. We demonstrate this for speed-of-sound imaging as a challenging setting of quantitative ultrasound image reconstruction.

DenOiS: Dual-Domain Denoising of Observation and Solution in Ultrasound Image Reconstruction

Abstract

Medical imaging aims to recover underlying tissue properties, using inexact (simplified/linearized) imaging models and often from inaccurate and incomplete measurements. Analytical reconstruction methods rely on hand-crafted regularization, sensitive to noise assumptions and parameter tuning. Among deep learning alternatives, plug-and-play (PnP) approaches learn regularization while incorporating imaging physics during inference, outperforming purely data-driven methods. The performance of all these approaches, however, still strongly depends on measurement quality and imaging model accuracy. In this work, we propose DenOiS, a framework that denoises both input observations and resulting solution in their respective domains. It consists of an observation refinement strategy that corrects degraded measurements while compensating for imaging model simplifications, and a diffusion-based PnP reconstruction approach that remains robust under missing measurements. DenOiS enables generalization to real data from training only in simulations, resulting in high-fidelity image reconstruction with noisy observations and inexact imaging models. We demonstrate this for speed-of-sound imaging as a challenging setting of quantitative ultrasound image reconstruction.

Paper Structure

This paper contains 9 sections, 5 equations, 2 figures, 2 tables.

Table of Contents

  1. Introduction
  2. Methods
  3. Model-based Image Reconstruction and Denoising Diffusion
  4. Measurement Refinement
  5. DenOiS: Dual-Domain Denoising of Observations and Solution
  6. Application to Quantitative US: Speed-of-Sound Imaging
  7. Implementation
  8. Results
  9. Discussion and Conclusion

Figures (2)

  • Figure 1: Deep learning approaches in image reconstruction (a) without using the imaging model, (b,c) using it disjointly, and (d) jointly with data consistency in a plug-and-play (PnP) framework. (e) Our proposed iterative dual-domain denoising framework (DenOiS), alternating between observation and solution domain refinement. (f) Illustration on an ultrasound application for speed-of-sound imaging from incomplete and noisy observations with an inexact model.
  • Figure 2: (a) Sample reconstructions for C$_2$ and phantom data, with RMSE (ACE) values reported. (b) An in-vivo breast cancer example reporting $\mathrm\Delta \mathrm{c}$ / gCNR. (c) Corrections of two acquired observations with different $\mathcal{M}_\text{D}$ variants.