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Title
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Model-based super-resolution reconstruction for pseudo-continuous Arterial Spin Labeling
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Author
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Abstract
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| 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. |
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Language
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English
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Source (journal)
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Neuroimage. - New York
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Publication
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New York
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2024
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ISSN
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1053-8119
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DOI
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10.1016/J.NEUROIMAGE.2024.120506
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Volume/pages
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286
(2024)
, p. 1-21
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Article Reference
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120506
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ISI
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001172406100001
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Pubmed ID
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38185186
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Full text (Publisher's DOI)
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Full text (open access)
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