Document Type
Article
Publication Date
11-23-2021
Abstract
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 Low Rank 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.
Recommended Citation
Baul, Abhijit, et al. "Self-Learned Kernel Low Rank Approach TO Accelerated High Resolution 3D Diffusion MRI." arXiv preprint arXiv:2110.08622 (2021).