High-resolution ultra-low-field MRI with SNRAware denoising

TL;DR

This study demonstrates that deep learning-based denoising can substantially improve the signal-to-noise ratio in ultra-low-field MRI, enabling higher quality images comparable to standard clinical protocols.

Contribution

The paper presents a systematic evaluation of a SNRAware deep learning denoising model applied to ultra-low-field MRI, showing its effectiveness and limitations across various conditions.

Findings

DL denoising increases effective SNR in ULF MRI.

Images achieve spatial resolution comparable to 3 T clinical protocols.

Denoising mainly removes stochastic noise while preserving signal structure.

Abstract

Ultra-low-field (ULF, <0.1 T) magnetic resonance imaging (MRI) systems offer advantages in cost, portability, and accessibility, but their current utility is still limited by low signal-to-noise ratio (SNR). Deep learning (DL)-based denoising has emerged as a potential strategy to mitigate this limitation. In this work, we present a systematic evaluation of a high-performance DL denoising model trained using the SNRAware framework and applied to 88 mT and 72 mT data. Using a series of controlled experiments, we assessed model performance as a function of spatial resolution, coil impedance matching, readout bandwidth, input noise level, k-space undersampling, anatomy, image contrast, and scanner platform, and compared against analytical denoising algorithms. The model consistently increased the effective SNR of ULF acquisitions, enabling images with nominal spatial resolutions comparable to those commonly used in clinical 3 T protocols. Residual analyses indicated that the model predominantly removed stochastic noise while preserving underlying signal structure. At the same time, the results highlight some constraints: denoising performance remains dependent on the starting SNR of the acquisition, and training-domain mismatch influences behavior under certain artifact conditions. These findings suggest that DL-based denoising can significantly expand the practical capabilities of ULF MRI, while emphasizing potential benefits from hardware-software co-optimization and the need for rigorous clinical validation to determine the diagnostic value of denoised images.