SuperDTI: Ultrafast diffusion tensor imaging and fiber tractography with deep learning

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) (reduced in q-space and k-space) 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 artifacts in DWIs. The network is trained and tested using datasets from Human Connectome Project and patients with ischemic stroke. The noise robustness of the network and the lesion detectability of the reconstructed maps are evaluated. SuperDTI is compared against the state-of-the-art methods for diffusion map reconstruction and fiber tracking. Results: The proposed technique is able to generate fractional anisotropy and mean diffusivity maps, as well as fiber tractography, from as few as six undersampled raw DWIs. SuperDTI achieves a quantification error of less than 5% in all regions of interest in white matter and gray matter structures. In addition, we demonstrate that the trained neural network is robust to additional noise 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 corrupted DWIs (up to 30-fold). Such a significant reduction in scan time will allow the inclusion of DTI into clinical routine for many potential applications.