2023
DOI: 10.1002/mrm.29783
|View full text |Cite
|
Sign up to set email alerts
|

Brain perfusion imaging by multi‐delay arterial spin labeling: Impact of modeling dispersion and interaction with denoising strategies and pathology

Abstract: PurposeArterial spin labeling (ASL) acquisitions at multiple post‐labeling delays may provide more accurate quantification of cerebral blood flow (CBF), by fitting appropriate kinetic models and simultaneously estimating relevant parameters such as the arterial transit time (ATT) and arterial cerebral blood volume (aCBV). We evaluate the effects of denoising strategies on model fitting and parameter estimation when accounting for the dispersion of the label bolus through the vasculature in cerebrovascular dise… Show more

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
1

Citation Types

0
1
0

Year Published

2024
2024
2024
2024

Publication Types

Select...
2
2

Relationship

1
3

Authors

Journals

citations
Cited by 4 publications
(1 citation statement)
references
References 48 publications
0
1
0
Order By: Relevance
“…This limitation could be overcome by implementing a multi delay strategy that accounts for differences in arterial transit times (ATT). 39,40 This would also allow for the estimation of other perfusion related parameters (including the ATT) that may be altered in pathologies such as the ischemic stroke. In principle, higher resolution imaging may be possible this way using novel denoising approaches that make use of the information sparsity in such measurements.…”
Section: Discussionmentioning
confidence: 99%
“…This limitation could be overcome by implementing a multi delay strategy that accounts for differences in arterial transit times (ATT). 39,40 This would also allow for the estimation of other perfusion related parameters (including the ATT) that may be altered in pathologies such as the ischemic stroke. In principle, higher resolution imaging may be possible this way using novel denoising approaches that make use of the information sparsity in such measurements.…”
Section: Discussionmentioning
confidence: 99%