Estimation-Denoising Integration Network Architecture With Updated Parameter for MRI Reconstruction

Abstract

In recent years, plug-and-play (PnP) approaches have emerged as an appealing strategy for recovering magnetic resonance imaging. Compared with traditional compressed sensing methods, these approaches can leverage innovative denoisers to exploit the richer structure of medical images. However, most state-of-the-art networks are not able to adaptively remove noise at each level. To solve this problem, we propose a joint denoising network based on PnP trained to evaluate the noise distribution, realizing efficient, flexible, and accurate reconstruction. The ability of the first subnetwork to estimate complex distributions is utilized to implicitly learn noisy features, effectively tackling the difficulty of precisely delineating the obscure noise law. The second subnetwork builds on the first network and can denoise and reconstruct the image after obtaining the noise distribution. Precisely, the hyperparameter is dynamically adjusted to regulate the denoising level throughout each iteration, ensuring the convergence of our model. This step can gradually remove the image noise and use previous knowledge extracted from the frequency domain to enhance spatial particulars simultaneously. The experimental results significantly improve quantitative metrics and visual performance on different datasets.