Self-supervised Deep Unrolled Reconstruction Using Regularization by Denoising
This addresses the challenge of data scarcity in MRI applications, offering an incremental improvement over existing Noise2Noise methods.
The paper tackles the problem of high-quality MRI reconstruction with limited labeled data by proposing DURED-Net, a method combining self-supervised denoising and plug-and-play techniques, which reduces the required training data while achieving competitive reconstruction quality.
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.