Publication
Title
Super-resolution reconstruction of diffusion parameters from diffusion-weighted images with different slice orientations
Author
Abstract
Purpose Diffusion MRI is hampered by long acquisition times, low spatial resolution, and a low signal-to-noise ratio. Recently, methods have been proposed to improve the trade-off between spatial resolution, signal-to-noise ratio, and acquisition time of diffusion-weighted images via super-resolution reconstruction (SRR) techniques. However, during the reconstruction, these SRR methods neglect the q-space relation between the different diffusion-weighted images. Method An SRR method that includes a diffusion model and directly reconstructs high resolution diffusion parameters from a set of low resolution diffusion-weighted images was proposed. Our method allows an arbitrary combination of diffusion gradient directions and slice orientations for the low resolution diffusion-weighted images, optimally samples the q- and k-space, and performs motion correction with b-matrix rotation. Results Experiments with synthetic data and in vivo human brain data show an increase of spatial resolution of the diffusion parameters, while preserving a high signal-to-noise ratio and low scan time. Moreover, the proposed SRR method outperforms the previous methods in terms of the root-mean-square error. Conclusion The proposed SRR method substantially increases the spatial resolution of MRI that can be obtained in a clinically feasible scan time.
Language
English
Source (journal)
Magnetic resonance in medicine. - Orlando, Fla
Publication
Orlando, Fla : 2016
ISSN
0740-3194
Volume/pages
75:1(2016), p. 181-195
ISI
000367739200018
Full text (Publisher's DOI)
Full text (open access)
Full text (publisher's version - intranet only)
UAntwerpen
Faculty/Department
Research group
[E?say:metaLocaldata.cgzprojectinf]
Integrated cerebral networks for perception, cognition and action in human and non-human primates (CEREBNET).
Publication type
Subject
Affiliation
Publications with a UAntwerp address
External links
Web of Science
Record
Identification
Creation 16.04.2015
Last edited 20.11.2017
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