Ultrasound Image Reconstruction with Denoising Diffusion Restoration
  Models
Yuxin Zhang Clément Huneau Jérôme Idier Diana Mateus
Ultrasound image reconstruction can be approximately cast as a linear inverse
problem that has traditionally been solved with penalized optimization using
the $l_1$ or $l_2$ norm, or wavelet-based terms. However, such regularization
functions often struggle to balance the sparsity and the smoothness. A
promising alternative is using learned priors to make the prior knowledge
closer to reality. In this paper, we rely on learned priors under the framework
of Denoising Diffusion Restoration Models (DDRM), initially conceived for
restoration tasks with natural images. We propose and test two adaptions of
DDRM to ultrasound inverse problem models, DRUS and WDRUS. Our experiments on
synthetic and PICMUS data show that from a single plane wave our method can
achieve image quality comparable to or better than DAS and state-of-the-art
methods. The code is available at:
https://github.com/Yuxin-Zhang-Jasmine/DRUS-v1.