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
@article{arxiv.2110.08622,
title = {Self-Learned Kernel Low Rank Approach TO Accelerated High Resolution 3D Diffusion MRI},
author = {Abhijit Baul and Nian Wang and Choyi Zhang and Leslie Ying and Yuchou Chang and Ukash Nakarmi},
journal= {arXiv preprint arXiv:2110.08622},
year = {2024}
}
Comments
This work has been submitted to the IEEE for possible publication