  Purpose: To propose a deep learning-based reconstruction framework for
ultrafast and robust diffusion tensor imaging and fiber tractography. Methods:
We propose SuperDTI to learn the nonlinear relationship between
diffusion-weighted images (DWIs) and the corresponding tensor-derived
quantitative maps as well as the fiber tractography. Super DTI bypasses the
tensor fitting procedure, which is well known to be highly susceptible to noise
and motion in DWIs. The network is trained and tested using datasets from Human
Connectome Project and patients with ischemic stroke. SuperDTI is compared
against the state-of-the-art methods for diffusion map reconstruction and fiber
tracking. Results: Using training and testing data both from the same protocol
and scanner, SuperDTI is shown to generate fractional anisotropy and mean
diffusivity maps, as well as fiber tractography, from as few as six raw DWIs.
The method achieves a quantification error of less than 5% in all regions of
interest in white matter and gray matter structures. We also demonstrate that
the trained neural network is robust to noise and motion in the testing data,
and the network trained using healthy volunteer data can be directly applied to
stroke patient data without compromising the lesion detectability. Conclusion:
This paper demonstrates the feasibility of superfast diffusion tensor imaging
and fiber tractography using deep learning with as few as six DWIs directly,
bypassing tensor fitting. Such a significant reduction in scan time may allow
the inclusion of DTI into the clinical routine for many potential applications.
