Highly accelerated Diffusion Spectrum Imaging (DSI) by leveraging low-rank Tensor Completion

Motivation: Diffusion Spectrum Imaging (DSI) provides detailed brain microstructure information, but is inherently slow. Goal(s): To highly accelerate DSI by undersampling the q-space and to enhance reconstruction quality of the undersampled q-space. Approach: DSI samples the q-space across various gradient directions and b-values, resulting in high redundancy along the same encoding directions. To take advantage of this redundancy, we propose utilizing a low-rank tensor completion framework, enabling highly accelerated DSI. Results: Our experiments demonstrated that high acceleration factors of R=5 are possible and that our proposed framework outperformed the state of the art Compressed Sensing approach by yielding sharper and clearer fiber orientations. Impact: The proposed reconstruction framework allows for higher acceleration factors to further enhance Diffusion Spectrum Imaging (DSI). Thanks to the reduced scan time, DSI could be frequently used in clinical protocols to obtain detailed anatomical information about fiber microstructure.