Ultrasound Image Reconstruction with Denoising Diffusion Restoration Models

Autor:	Zhang, Yuxin, Huneau, Clément, Idier, Jérôme, Mateus, Diana
Rok vydání:	2023
Předmět:	Computer Science - Computer Vision and Pattern Recognition Computer Science - Artificial Intelligence Electrical Engineering and Systems Science - Signal Processing
Druh dokumentu:	Working Paper
Popis:	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. Comment: accepted for DGM4MICCAI 2023
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2307.15990 Zobrazit plný text záznamu View this record from Arxiv