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Diffusion Magnetic Resonance Imaging (dMRI) is a promising method to analyzethe subtle changes in the tissue structure. However, the lengthy acquisitiontime is a major limitation in the clinical application of dMRI. Different imageacquisition techniques such as parallel imaging, compressed sensing, hasshortened the prolonged acquisition time but creating high-resolution 3D dMRIslices still requires a significant amount of time. In this study, we haveshown that high-resolution 3D dMRI can be reconstructed from the highlyundersampled k-space and q-space data using a Kernel LowRank method. Ourproposed method has outperformed the conventional CS methods in terms of bothimage quality and diffusion maps constructed from the diffusion-weighted images

Self-Learned Kernel Low Rank Approach TO Accelerated High Resolution 3D Diffusion MRI