Linked Life Data

17654585

Source:http://linkedlifedata.com/resource/pubmed/id/17654585

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
2
pubmed:dateCreated
2007-7-30
pubmed:abstractText
We examine the use of arterial spin labeling (ASL) in normal brains of rats and humans to measure perfusion (F) and capillary permeability surface area product (PS) using a previously described two-compartment model. We investigate the experimental limits on F and PS quantification using simulations and experimental verification in rat brain at 9.4T. A sensitivity analysis on the two-compartment model is presented to estimate optimal experimental inversion times (TIs) for F and PS quantification and indicate how sensitive the model would be to changes in F and PS. We present the expected error on flow-sensitive alternating inversion recovery (FAIR)-based F and PS measurements and quantify the precision with which these parameters could be estimated at various signal-to-noise ratios (SNRs). Perfusion was measured in four rat brains using FAIR ASL, and we conclude that perfusion could be quantified with an acceptable level of precision using this technique. However, we found that to measure PS with even a 100% coefficient of variation (CV) would require an SNR increase of approximately 2 orders of magnitude over our acquired data. We conclude that with current MR capabilities and with the experimental approach used in this study, acceptable levels of precision in the measurement of PS are not possible.
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:chemical
pubmed:status
MEDLINE
pubmed:month
Aug
pubmed:issn
0740-3194
pubmed:author
pubmed:issnType
Print
pubmed:volume
58
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
281-9
pubmed:meshHeading
pubmed:year
2007
pubmed:articleTitle
What levels of precision are achievable for quantification of perfusion and capillary permeability surface area product using ASL?
pubmed:affiliation
Imaging Science and Biomedical Engineering, University of Manchester, Manchester, UK.
pubmed:publicationType
Journal Article