  Recent developments in low-field (LF) magnetic resonance imaging (MRI)
systems present remarkable opportunities for affordable and widespread MRI
access. A robust denoising method to overcome the intrinsic low
signal-noise-ratio (SNR) barrier is critical to the success of LF MRI. However,
current data-driven MRI denoising methods predominantly handle magnitude images
and rely on customized models with constrained data diversity and quantity,
which exhibit limited generalizability in clinical applications across diverse
MRI systems, pulse sequences, and organs. In this study, we present ImT-MRD: a
complex-valued imaging transformer trained on a vast number of clinical MRI
scans aiming at universal MR denoising at LF systems. Compared with averaging
multiple-repeated scans for higher image SNR, the model obtains better image
quality from fewer repetitions, demonstrating its capability for accelerating
scans under various clinical settings. Moreover, with its complex-valued image
input, the model can denoise intermediate results before advanced
post-processing and prepare high-quality data for further MRI research. By
delivering universal and accurate denoising across clinical and research tasks,
our model holds great promise to expedite the evolution of LF MRI for
accessible and equal biomedical applications.
