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This is a Quantitative Study study.

September 17, 2025

Zero-Shot Unsupervised Denoiser Learning for Plug-and-Play MRI Reconstruction

Key Points

Experiments reveal that the zero-shot denoiser improves MRI reconstruction, particularly when training data is limited.
The method utilizes k-space splitting to generate training data without requiring extensive datasets, enhancing flexibility.
Employing Noise2Noise training enables the denoiser to effectively reconstruct images in a plug-and-play framework.
The proposed approach shows potential benefits beyond MRI, applicable to various imaging tasks requiring minimal training data.

Abstract

Motivation: Deep learning-based MRI reconstruction often requires extensive training data or fully sampled ground truths, potentially limiting flexibility and applicability. Goal(s): To develop a novel zero-shot learning method for undersampled MRI reconstruction without the need for training data. Approach: The method trains a denoiser by randomly splitting acquired undersampled k-space into two complementary subsets, applying unbiased reconstruction to each subset to create training data and target labels, respectively, and performing Noise2Noise training. The trained denoiser is then integrated into the plug-and-play framework for final reconstruction. Results: Experiments demonstrate advantages over existing techniques, offering improved flexibility and adaptability, particularly in scenarios with limited training data. Impact: The proposed zero-shot method offers improved MRI reconstruction without requiring any training data. This framework not only provides enhanced image quality and adaptability across diverse MR applications but also is able to extend to other imaging tasks beyond MRI.

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