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Self-Supervised Noise Adaptive MRI Denoising via Repetition to Repetition (Rep2Rep) Learning.

Nikola Janjušević^1,2, Jingjia Chen^1,2, Luke Ginocchio^1,2

¹Bernard and Irene Schwartz Center for Biomedical Imaging, Department of Radiology, New York University Grossman School of Medicine, New York, New York, USA.

Magnetic Resonance in Medicine

|November 9, 2025

Summary

This summary is machine-generated.

Repetition to Repetition (Rep2Rep) learning is a new self-supervised method for denoising low-field MRI scans. This noise-adaptive framework improves image quality without needing ground truth data, making low-field MRI more practical.

Keywords:

MRI MRI acceleration Rep2Rep deep learning denoising self‐supervised learning

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Area of Science:

Medical Imaging
Artificial Intelligence
Biophysics

Background:

Low-field MRI offers advantages but suffers from lower signal-to-noise ratios (SNR).
Image denoising is crucial for low-field MRI to enhance diagnostic accuracy.
Existing denoising methods often require ground-truth data or struggle with varying noise levels.

Purpose of the Study:

To introduce Repetition to Repetition (Rep2Rep) learning, a novel self-supervised denoising framework for low-field MRI.
To develop a noise-adaptive approach that generalizes across different noise levels.
To improve image quality and scan efficiency in low-field MRI applications.

Main Methods:

Rep2Rep learning utilizes two repeated MRI acquisitions, using one as input and the other as target, extending the Noise2Noise framework.
A neural network is trained in a self-supervised manner, eliminating the need for ground-truth data.
Noise-adaptive training allows generalization across varying noise levels and flexible inference.

Main Results:

Rep2Rep learning outperformed MC-SURE on synthetic and 0.55T MRI datasets.
Denoising quality on synthetic Brain MRI was comparable to supervised learning, surpassing MC-SURE in detail preservation.
Reader studies showed Rep2Rep-denoised 2-average 0.55T Prostate MRI images were superior to 8-average noisy images.
The method demonstrated robustness to noise-level discrepancies between training and inference.

Conclusions:

Rep2Rep learning provides effective self-supervised denoising for low-field MRI using multi-repetition data.
The noise-adaptive nature enables generalization to different SNR regimes without clean reference images.
Rep2Rep learning is a promising tool for enhancing image quality and scan efficiency in low-field MRI.