Acceleration of three-dimensional diffusion magnetic resonance imaging using a kernel low-rank compressed sensing method.

ABSTRACT: Diffusion Magnetic Resonance Imaging (dMRI) has shown great potential in probing tissue microstructure and structural connectivity in the brain but is often limited by the lengthy scan time needed to sample the diffusion profile by acquiring multiple diffusion weighted images (DWIs). Although parallel imaging technique has improved the speed of dMRI acquisition, attaining high resolution three dimensional (3D) dMRI on preclinical MRI systems remained still time consuming. In this paper, kernel principal component analysis, a machine learning approach, was employed to estimate the correlation among DWIs. We demonstrated the feasibility of such correlation estimation from low-resolution training DWIs and used the correlation as a constraint to reconstruct high-resolution DWIs from highly und

SUBMITTER: Zhang C 

PROVIDER: S-EPMC7359413 | biostudies-literature | 2020 Apr

REPOSITORIES: biostudies-literature