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Title: GSURE Denoising enables training of higher quality generative priors for accelerated Multi-Coil MRI Reconstruction
Motivation: Publicly available k-space data used for training are inherently noisy with no available ground truth. Goal(s): To denoise k-space data in an unsupervised manner for downstream applications. Approach: We use Generalized Stein’s Unbiased Risk Estimate (GSURE) applied to multi-coil MRI to denoise images without access to ground truth. Subsequently, we train a generative model to show improved accelerated MRI reconstruction. Results: We demonstrate: (1) GSURE can successfully remove noise from k-space; (2) generative priors learned on GSURE-denoised samples produce realistic synthetic samples; and (3) reconstruction performance on subsampled MRI improves using priors trained on denoised images in comparison to training on noisy samples. Impact: This abstract shows that we can denoise multi-coil data without ground truth and train deep generative models directly on noisy k-space in an unsupervised manner, for improved accelerated reconstruction.  more » « less
Award ID(s):
2239687
PAR ID:
10525854
Author(s) / Creator(s):
; ; ; ;
Publisher / Repository:
International Society for Magnetic Resonance in Medicine
Date Published:
Subject(s) / Keyword(s):
AI/ML Image Reconstruction, Image Reconstruction, Deep Generative Models, Inverse Problems, Unsupervised Learning, Denoising
Format(s):
Medium: X
Location:
ISMRM 2024, Singapore
Sponsoring Org:
National Science Foundation
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