  Diffusion MRI is a non-invasive, in-vivo biomedical imaging method for
mapping tissue microstructure. Applications include structural connectivity
imaging of the human brain and detecting microstructural neural changes.
However, acquiring high signal-to-noise ratio dMRI datasets with high angular
and spatial resolution requires prohibitively long scan times, limiting usage
in many important clinical settings, especially for children, the elderly, and
in acute neurological disorders that may require conscious sedation or general
anesthesia. We employ a Swin UNEt Transformers model, trained on augmented
Human Connectome Project data and conditioned on registered T1 scans, to
perform generalized denoising of dMRI. We also qualitatively demonstrate
super-resolution with artificially downsampled HCP data in normal adult
volunteers. Remarkably, Swin UNETR can be fine-tuned for an out-of-domain
dataset with a single example scan, as we demonstrate on dMRI of children with
neurodevelopmental disorders and of adults with acute evolving traumatic brain
injury, each cohort scanned on different models of scanners with different
imaging protocols at different sites. We exceed current state-of-the-art
denoising methods in accuracy and test-retest reliability of rapid diffusion
tensor imaging requiring only 90 seconds of scan time. Applied to tissue
microstructural modeling of dMRI, Swin UNETR denoising achieves dramatic
improvements over the state-of-the-art for test-retest reliability of
intracellular volume fraction and free water fraction measurements and can
remove heavy-tail noise, improving biophysical modeling fidelity. Swin UNeTR
enables rapid diffusion MRI with unprecedented accuracy and reliability,
especially for probing biological tissues for scientific and clinical
applications. The code and model are publicly available at
https://github.com/ucsfncl/dmri-swin.
