  The ability to recover MRI signal from noise is key to achieve fast
acquisition, accurate quantification, and high image quality. Past work has
shown convolutional neural networks can be used with abundant and paired low
and high-SNR images for training. However, for applications where high-SNR data
is difficult to produce at scale (e.g. with aggressive acceleration, high
resolution, or low field strength), training a new denoising network using a
large quantity of high-SNR images can be infeasible.
  In this study, we overcome this limitation by improving the generalization of
denoising models, enabling application to many settings beyond what appears in
the training data. Specifically, we a) develop a training scheme that uses
complex MRIs reconstructed in the SNR units (i.e., the images have a fixed
noise level, SNR unit training) and augments images with realistic noise based
on coil g-factor, and b) develop a novel imaging transformer (imformer) to
handle 2D, 2D+T, and 3D MRIs in one model architecture. Through empirical
evaluation, we show this combination improves performance compared to CNN
models and improves generalization, enabling a denoising model to be used
across field-strengths, image contrasts, and anatomy.
