Publication
Title
Model-based super-resolution reconstruction for pseudo-continuous Arterial Spin Labeling
Author
Abstract
Arterial spin labeling (ASL) is a promising, non-invasive perfusion magnetic resonance imaging technique for quantifying cerebral blood flow (CBF). Unfortunately, ASL suffers from an inherently low signal-to-noise ratio (SNR) and spatial resolution, undermining its potential. Increasing spatial resolution without significantly sacrificing SNR or scan time represents a critical challenge towards routine clinical use. In this work, we propose a model-based super-resolution reconstruction (SRR) method with joint motion estimation that breaks the traditional SNR/resolution/scan-time trade-off. From a set of differently oriented 2D multi-slice pseudo-continuous ASL images with a low through-plane resolution, 3D-isotropic, high resolution, quantitative CBF maps are estimated using a Bayesian approach. Experiments on both synthetic whole brain phantom data, and on in vivo brain data, show that the proposed SRR Bayesian estimation framework outperforms state-of-the-art ASL quantification.
Language
English
Source (journal)
Neuroimage. - New York
Publication
New York : 2024
ISSN
1053-8119
DOI
10.1016/J.NEUROIMAGE.2024.120506
Volume/pages
286 (2024) , p. 1-21
Article Reference
120506
ISI
001172406100001
Pubmed ID
38185186
Full text (Publisher's DOI)
Full text (open access)
UAntwerpen
Faculty/Department
Research group
Publication type
Subject
Affiliation
Publications with a UAntwerp address
External links
Record
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Creation 25.03.2024
Last edited 02.04.2024
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