Acquisition- and modeling-independent resolution enhancement of brain diffusion-weighted magnetic resonance imaging volumes

Diffusion-weighted magnetic resonance imaging (dwMRI) provides unique capabilities for non-invasive imaging of neural fiber pathways in the brain. dwMRI is an increasingly popular imaging method and has promising diagnostic and surgical applications for Alzheimer's disease, brain tumors, and epilepsy, to name a few. However, one limitation of dwMRI (specifically, the more common diffusion tensor imaging scheme, DTI) is that it suffers from a relatively low resolution. This often leads to ambiguity in determining location and orientation of neural fibers, and therefore reduces the reliability of information gained from dwMRI. Several approaches have been suggested to address this issue. One approach is to have a finer sampling grid, as in diffusion spectrum imaging (DSI) and high-angular resolution imaging (HARDI). While this did result in a resolution improvement, it has the side effects of lowering the quality of image signal-to-noise ratio (SNR) or prolonging imaging time, which hinders its use in routine clinical practice. Subsequently, an alternative approach has been proposed based on super-resolution methods, where multiple low resolution images are fused into a higher resolution one. While this managed to improve resolution without reducing SNR, the multiple acquisitions required still resulted in a prolonged imaging time. In this thesis, we propose a processing pipeline that uses a super resolution approach based on dictionary learning for alleviating the dwMRI low resolution problem. Unlike the majority of existing dwMRI resolution enhancement approaches, our proposed framework does not require modifying the dwMRI acquisition. This makes it applicable to legacy data. Moreover, this approach does not require using a specific diffusion model. Motivated by how functional connectivity (FC) reflects the underlying structural connectivity (SC), we use the Human Connectome Project and Kirby multimodal dataset to quantitatively validate our results by investigating the consistency between SC and FC before and after super-resolving the data. Based on this scheme, we show that our method outperforms interpolation and the only existing single image super-resolution method for dMRI that is not dependent on a specific diffusion model. Qualitatively, we illustrate the improved resolution in diffusion images and illustrate the revealed details beyond what is achievable with the original data.Applied Science, Faculty ofGraduat