Self-supervised Deep Unrolled Reconstruction Using Regularization by Denoising

TL;DR

This paper introduces DURED-Net, a self-supervised MRI reconstruction method that combines denoising and physics-based priors, reducing training data needs while maintaining high image quality.

Contribution

It presents a novel self-supervised MRI reconstruction approach integrating RED and Noise2Noise, enhancing performance with less training data.

Findings

Requires less training data for high-quality reconstruction

Outperforms existing Noise2Noise-based methods

Demonstrates robustness in MRI reconstruction tasks

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.