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Designing and comparing optimized pseudo-continuous Arterial Spin Labeling protocols for measurement of cerebral blood flow

Arterial Spin Labeling (ASL) is a non-invasive, non-contrast, perfusion imaging technique which is inherently SNR limited. It is, therefore, important to carefully design scan protocols to ensure accurate measurements. Many pseudo-continuous ASL (PCASL) protocol designs have been proposed for measur...

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Autores principales: Woods, Joseph G., Chappell, Michael A., Okell, Thomas W.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Academic Press 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7762814/
https://www.ncbi.nlm.nih.gov/pubmed/32853814
http://dx.doi.org/10.1016/j.neuroimage.2020.117246
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author Woods, Joseph G.
Chappell, Michael A.
Okell, Thomas W.
author_facet Woods, Joseph G.
Chappell, Michael A.
Okell, Thomas W.
author_sort Woods, Joseph G.
collection PubMed
description Arterial Spin Labeling (ASL) is a non-invasive, non-contrast, perfusion imaging technique which is inherently SNR limited. It is, therefore, important to carefully design scan protocols to ensure accurate measurements. Many pseudo-continuous ASL (PCASL) protocol designs have been proposed for measuring cerebral blood flow (CBF), but it has not yet been demonstrated which design offers the most accurate and repeatable CBF measurements. In this study, a wide range of literature PCASL protocols were first optimized for CBF accuracy and then compared using Monte Carlo simulations and in vivo experiments. The protocols included single-delay, sequential and time-encoded multi-timepoint protocols, and several novel protocol designs, which are hybrids of time-encoded and sequential multi-timepoint protocols. It was found that several multi-timepoint protocols produced more confident, accurate, and repeatable CBF estimates than the single-delay protocol, while also generating maps of arterial transit time. Of the literature protocols, the time-encoded protocol with T(1)-adjusted label durations gave the most confident and accurate CBF estimates in vivo (16% and 40% better than single-delay), while the sequential multi-timepoint protocol was the most repeatable (20% more repeatable than single-delay). One of the novel hybrid protocols, Hybrid(T1)(-adj), was found to produce the most confident, accurate and repeatable CBF estimates out of all the protocols tested in both simulations and in vivo (24%, 47%, and 28% more confident, accurate, and repeatable than single-delay in vivo). The Hybrid(T1)(-adj) protocol makes use of the best aspects of both time-encoded and sequential multi-timepoint protocols and should be a useful tool for accurately and efficiently measuring CBF.
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spelling pubmed-77628142020-12-28 Designing and comparing optimized pseudo-continuous Arterial Spin Labeling protocols for measurement of cerebral blood flow Woods, Joseph G. Chappell, Michael A. Okell, Thomas W. Neuroimage Article Arterial Spin Labeling (ASL) is a non-invasive, non-contrast, perfusion imaging technique which is inherently SNR limited. It is, therefore, important to carefully design scan protocols to ensure accurate measurements. Many pseudo-continuous ASL (PCASL) protocol designs have been proposed for measuring cerebral blood flow (CBF), but it has not yet been demonstrated which design offers the most accurate and repeatable CBF measurements. In this study, a wide range of literature PCASL protocols were first optimized for CBF accuracy and then compared using Monte Carlo simulations and in vivo experiments. The protocols included single-delay, sequential and time-encoded multi-timepoint protocols, and several novel protocol designs, which are hybrids of time-encoded and sequential multi-timepoint protocols. It was found that several multi-timepoint protocols produced more confident, accurate, and repeatable CBF estimates than the single-delay protocol, while also generating maps of arterial transit time. Of the literature protocols, the time-encoded protocol with T(1)-adjusted label durations gave the most confident and accurate CBF estimates in vivo (16% and 40% better than single-delay), while the sequential multi-timepoint protocol was the most repeatable (20% more repeatable than single-delay). One of the novel hybrid protocols, Hybrid(T1)(-adj), was found to produce the most confident, accurate and repeatable CBF estimates out of all the protocols tested in both simulations and in vivo (24%, 47%, and 28% more confident, accurate, and repeatable than single-delay in vivo). The Hybrid(T1)(-adj) protocol makes use of the best aspects of both time-encoded and sequential multi-timepoint protocols and should be a useful tool for accurately and efficiently measuring CBF. Academic Press 2020-12 /pmc/articles/PMC7762814/ /pubmed/32853814 http://dx.doi.org/10.1016/j.neuroimage.2020.117246 Text en © 2020 The Author(s) http://creativecommons.org/licenses/by/4.0/ This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Woods, Joseph G.
Chappell, Michael A.
Okell, Thomas W.
Designing and comparing optimized pseudo-continuous Arterial Spin Labeling protocols for measurement of cerebral blood flow
title Designing and comparing optimized pseudo-continuous Arterial Spin Labeling protocols for measurement of cerebral blood flow
title_full Designing and comparing optimized pseudo-continuous Arterial Spin Labeling protocols for measurement of cerebral blood flow
title_fullStr Designing and comparing optimized pseudo-continuous Arterial Spin Labeling protocols for measurement of cerebral blood flow
title_full_unstemmed Designing and comparing optimized pseudo-continuous Arterial Spin Labeling protocols for measurement of cerebral blood flow
title_short Designing and comparing optimized pseudo-continuous Arterial Spin Labeling protocols for measurement of cerebral blood flow
title_sort designing and comparing optimized pseudo-continuous arterial spin labeling protocols for measurement of cerebral blood flow
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7762814/
https://www.ncbi.nlm.nih.gov/pubmed/32853814
http://dx.doi.org/10.1016/j.neuroimage.2020.117246
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