A systematic study of the sensitivity of partial volume correction methods for the quantification of perfusion from pseudo-continuous arterial spin labeling MRI

Arterial spin labeling (ASL) MRI is a non-invasive technique for the quantification of cerebral perfusion, and pseudo-continuous arterial spin labeling (PCASL) has been recommended as the standard implementation by a recent consensus of the community. Due to the low spatial resolution of ASL images,...

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Main Authors: Zhao, M, Mezue, M, Segerdahl, A, Okell, T, Tracey, I, Xiao, Y, Chappell, M
Format: Journal article
Published: Elsevier 2017
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author Zhao, M
Mezue, M
Segerdahl, A
Okell, T
Tracey, I
Xiao, Y
Chappell, M
author_facet Zhao, M
Mezue, M
Segerdahl, A
Okell, T
Tracey, I
Xiao, Y
Chappell, M
author_sort Zhao, M
collection OXFORD
description Arterial spin labeling (ASL) MRI is a non-invasive technique for the quantification of cerebral perfusion, and pseudo-continuous arterial spin labeling (PCASL) has been recommended as the standard implementation by a recent consensus of the community. Due to the low spatial resolution of ASL images, perfusion quantification is biased by partial volume effects. Consequently, several partial volume correction (PVEc) methods have been developed to reduce the bias in gray matter (GM) perfusion quantification. The efficacy of these methods relies on both the quality of the ASL data and the accuracy of partial volume estimates. Here we systematically investigate the sensitivity of different PVEc methods to variability in both the ASL data and partial volume estimates using simulated PCASL data and in vivo PCASL data from a reproducibility study. We examined the PVEc methods in two ways: the ability to preserve spatial details and the accuracy of GM perfusion estimation. Judging by the root-mean-square error (RMSE) between simulated and estimated GM CBF, the spatially regularized method was superior in preserving spatial details compared to the linear regression method (RMSE of 1.2 vs 5.1 in simulation of GM CBF with short scale spatial variations). The linear regression method was generally less sensitive than the spatially regularized method to noise in data and errors in the partial volume estimates (RMSE 6.3 vs 23.4 for SNR=5 simulated data), but this could be attributed to the greater smoothing introduced by the method. Analysis of a healthy cohort dataset indicates that PVEc, using either method, improves the repeatability of perfusion quantification (within-subject coefficient of variation reduced by 5% after PVEc).
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spelling oxford-uuid:6df0f8bb-401b-4b93-8df9-664e5c0b4edf2022-03-26T19:21:03ZA systematic study of the sensitivity of partial volume correction methods for the quantification of perfusion from pseudo-continuous arterial spin labeling MRIJournal articlehttp://purl.org/coar/resource_type/c_dcae04bcuuid:6df0f8bb-401b-4b93-8df9-664e5c0b4edfSymplectic Elements at OxfordElsevier2017Zhao, MMezue, MSegerdahl, AOkell, TTracey, IXiao, YChappell, MArterial spin labeling (ASL) MRI is a non-invasive technique for the quantification of cerebral perfusion, and pseudo-continuous arterial spin labeling (PCASL) has been recommended as the standard implementation by a recent consensus of the community. Due to the low spatial resolution of ASL images, perfusion quantification is biased by partial volume effects. Consequently, several partial volume correction (PVEc) methods have been developed to reduce the bias in gray matter (GM) perfusion quantification. The efficacy of these methods relies on both the quality of the ASL data and the accuracy of partial volume estimates. Here we systematically investigate the sensitivity of different PVEc methods to variability in both the ASL data and partial volume estimates using simulated PCASL data and in vivo PCASL data from a reproducibility study. We examined the PVEc methods in two ways: the ability to preserve spatial details and the accuracy of GM perfusion estimation. Judging by the root-mean-square error (RMSE) between simulated and estimated GM CBF, the spatially regularized method was superior in preserving spatial details compared to the linear regression method (RMSE of 1.2 vs 5.1 in simulation of GM CBF with short scale spatial variations). The linear regression method was generally less sensitive than the spatially regularized method to noise in data and errors in the partial volume estimates (RMSE 6.3 vs 23.4 for SNR=5 simulated data), but this could be attributed to the greater smoothing introduced by the method. Analysis of a healthy cohort dataset indicates that PVEc, using either method, improves the repeatability of perfusion quantification (within-subject coefficient of variation reduced by 5% after PVEc).
spellingShingle Zhao, M
Mezue, M
Segerdahl, A
Okell, T
Tracey, I
Xiao, Y
Chappell, M
A systematic study of the sensitivity of partial volume correction methods for the quantification of perfusion from pseudo-continuous arterial spin labeling MRI
title A systematic study of the sensitivity of partial volume correction methods for the quantification of perfusion from pseudo-continuous arterial spin labeling MRI
title_full A systematic study of the sensitivity of partial volume correction methods for the quantification of perfusion from pseudo-continuous arterial spin labeling MRI
title_fullStr A systematic study of the sensitivity of partial volume correction methods for the quantification of perfusion from pseudo-continuous arterial spin labeling MRI
title_full_unstemmed A systematic study of the sensitivity of partial volume correction methods for the quantification of perfusion from pseudo-continuous arterial spin labeling MRI
title_short A systematic study of the sensitivity of partial volume correction methods for the quantification of perfusion from pseudo-continuous arterial spin labeling MRI
title_sort systematic study of the sensitivity of partial volume correction methods for the quantification of perfusion from pseudo continuous arterial spin labeling mri
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