  Diffusion Magnetic Resonance Imaging (dMRI) is a promising method to analyze
the subtle changes in the tissue structure. However, the lengthy acquisition
time is a major limitation in the clinical application of dMRI. Different image
acquisition techniques such as parallel imaging, compressed sensing, has
shortened the prolonged acquisition time but creating high-resolution 3D dMRI
slices still requires a significant amount of time. In this study, we have
shown that high-resolution 3D dMRI can be reconstructed from the highly
undersampled k-space and q-space data using a Kernel LowRank method. Our
proposed method has outperformed the conventional CS methods in terms of both
image quality and diffusion maps constructed from the diffusion-weighted images
