Diffusion MRI is a non-invasive, in-vivo medical imaging method able to map
tissue microstructure and structural connectivity of the human brain, as well
as detect changes, such as brain development and injury, not visible by other
clinical neuroimaging techniques. However, acquiring high signal-to-noise ratio
(SNR) datasets with high angular and spatial sampling requires prohibitively
long scan times, limiting usage in many important clinical settings, especially
children, the elderly, and emergency patients with acute neurological disorders
who might not be able to cooperate with the MRI scan without conscious sedation
or general anesthesia. Here, we propose to use a Swin UNEt TRansformers (Swin
UNETR) model, trained on augmented Human Connectome Project (HCP) data and
conditioned on registered T1 scans, to perform generalized denoising and
super-resolution of diffusion MRI invariant to acquisition parameters, patient
populations, scanners, and sites. We qualitatively demonstrate super-resolution
with artificially downsampled HCP data in normal adult volunteers. Our
experiments on two other unrelated datasets, one of children with
neurodevelopmental disorders and one of traumatic brain injury patients, show
that our method demonstrates superior denoising despite wide data distribution
shifts. Further improvement can be achieved via finetuning with just one
additional subject. We apply our model to diffusion tensor (2nd order spherical
harmonic) and higher-order spherical harmonic coefficient estimation and show
results superior to current state-of-the-art methods. Our method can be used
out-of-the-box or minimally finetuned to denoise and super-resolve a wide
variety of diffusion MRI datasets. The code and model are publicly available at
https://github.com/ucsfncl/dmri-swin