Robust multi-coil MRI reconstruction via self-supervised denoising.

ABSTRACT:

Purpose

To examine the effect of incorporating self-supervised denoising as a pre-processing step for training deep learning (DL) based reconstruction methods on data corrupted by Gaussian noise. K-space data employed for training are typically multi-coil and inherently noisy. Although DL-based reconstruction methods trained on fully sampled data can enable high reconstruction quality, obtaining large, noise-free datasets is impractical.

Methods

We leverage Generalized Stein's Unbiased Risk Estimate (GSURE) for denoising. We evaluate two DL-based reconstruction methods: Diffusion Probabilistic Models (DPMs) and Model-Based Deep Learning (MoDL). We evaluate the impact of denoising on the performance of these DL-based methods in solving accelerated multi-coil magnetic resonanc

SUBMITTER: Aali A 

PROVIDER: S-EPMC12354090 | biostudies-literature | 2025 Nov

REPOSITORIES: biostudies-literature