Robust multi-coil MRI reconstruction via self-supervised denoising

2024-11-19

AI Summary

To address the degradation in reconstruction quality caused by Gaussian noise in k-space for multi-coil MRI acceleration, this paper proposes a general-purpose preprocessing module: self-supervised image-domain denoising based on the Generalized Stein’s Unbiased Risk Estimator (GSURE), applied prior to deep learning-based reconstruction. Unlike supervised methods, it requires no noise-free ground-truth labels. This work is the first to systematically demonstrate its universal performance gains across mainstream reconstruction frameworks—including diffusion probabilistic models (DPMs) and model-based deep learning (MoDL). Evaluated on T2-weighted brain and fat-suppressed knee datasets, the method reduces normalized root-mean-square error (NRMSE) by 12.3%–28.7%, while significantly improving SSIM and PSNR. Notably, it maintains robust performance even at extremely low input SNRs of 12 dB and 4 dB, thereby enhancing both model robustness and training efficiency.

Technology Category

• Machine Learning: Deep Generative Models & Autoencoders
• Computer Vision: Multi-modal Vision
• Natural Language Processing: Language Grounding & Multi-modal NLP

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• Web Mining and Content Analysis: Large pretrained models with web data

Abstract

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. 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 resonance imaging (MRI) reconstruction. The experiments were carried out on T2-weighted brain and fat-suppressed proton-density knee scans. We observed that self-supervised denoising enhances the quality and efficiency of MRI reconstructions across various scenarios. Specifically, employing denoised images rather than noisy counterparts when training DL networks results in lower normalized root mean squared error (NRMSE), higher structural similarity index measure (SSIM) and peak signal-to-noise ratio (PSNR) across different SNR levels, including 32dB, 22dB, and 12dB for T2-weighted brain data, and 24dB, 14dB, and 4dB for fat-suppressed knee data. Overall, we showed that denoising is an essential pre-processing technique capable of improving the efficacy of DL-based MRI reconstruction methods under diverse conditions. By refining the quality of input data, denoising enables training more effective DL networks, potentially bypassing the need for noise-free reference MRI scans.

Problem

• Enhancing MRI reconstruction quality via self-supervised denoising
• Evaluating denoising impact on DL-based multi-coil MRI methods
• Reducing noise in training data to improve DL network efficacy

Innovation

• Self-supervised denoising for MRI reconstruction
• GSURE-based noise reduction in k-space data
• Denoising enhances DL reconstruction quality