Self-supervised Deep Unrolled Reconstruction Using Regularization by
Denoising
- URL: http://arxiv.org/abs/2205.03519v3
- Date: Thu, 5 Oct 2023 00:15:00 GMT
- Title: Self-supervised Deep Unrolled Reconstruction Using Regularization by
Denoising
- Authors: Peizhou Huang, Chaoyi Zhang, Xiaoliang Zhang, Xiaojuan Li, Liang Dong,
Leslie Ying
- Abstract summary: We propose a novel reconstruction method, named DURED-Net, that enables interpretable self-supervised learning for MR image reconstruction.
We aim to boost the reconstruction performance of Noise2Noise in MR reconstruction by adding an explicit prior that utilizes imaging physics.
Experiment results demonstrate that the proposed method requires a reduced amount of training data to achieve high reconstruction quality.
- Score: 9.489726334567171
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Deep learning methods have been successfully used in various computer vision
tasks. Inspired by that success, deep learning has been explored in magnetic
resonance imaging (MRI) reconstruction. In particular, integrating deep
learning and model-based optimization methods has shown considerable
advantages. However, a large amount of labeled training data is typically
needed for high reconstruction quality, which is challenging for some MRI
applications. In this paper, we propose a novel reconstruction method, named
DURED-Net, that enables interpretable self-supervised learning for MR image
reconstruction by combining a self-supervised denoising network and a
plug-and-play method. We aim to boost the reconstruction performance of
Noise2Noise in MR reconstruction by adding an explicit prior that utilizes
imaging physics. Specifically, the leverage of a denoising network for MRI
reconstruction is achieved using Regularization by Denoising (RED). Experiment
results demonstrate that the proposed method requires a reduced amount of
training data to achieve high reconstruction quality among the state-of-art of
MR reconstruction utilizing the Noise2Noise method.
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