Linked Life Data

17260375

Source:http://linkedlifedata.com/resource/pubmed/id/17260375

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
2
pubmed:dateCreated
2007-2-5
pubmed:abstractText
Quantitative MR metrics (e.g., T1, T2, diffusion coefficients, and magnetization transfer ratios (MTRs etc)) are often derived from two images collected with one acquisition parameter changed between them (the "two-point" method). Since a low signal-to-noise-ratio (SNR) adversely affects the precision of these metrics, averaging is frequently used, although improvement accrues slowly-in proportion to the square root of imaging time. Fortunately, the relationship between the images' SNRs and the metric's precision can be exploited to our advantage. Using error propagation rules, we show that for a given sequence, specifying the total imaging time uniquely determines the optimal acquisition protocol. Specifically, instead of changing only one acquisition parameter and repeating the imaging pair until all available time is spent, we propose to adjust all of the parameters and the number of averages at each point according to their contribution to the sought metric's precision. The tactic is shown to increase the precision of the well-known two-point T1, T2, and diffusion coefficients estimation by 13-90% for the same sample, sequence, hardware, and duration. It is also shown that under this general framework, precision accrues faster than the square root of time. Tables of optimal parameters are provided for various experimental scenarios.
pubmed:grant
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:status
MEDLINE
pubmed:month
Feb
pubmed:issn
0740-3194
pubmed:author
pubmed:copyrightInfo
Copyright (c) 2007 Wiley-Liss, Inc.
pubmed:issnType
Print
pubmed:volume
57
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
380-7
pubmed:dateRevised
2007-12-3
pubmed:meshHeading
pubmed:year
2007
pubmed:articleTitle
Optimizing the precision-per-unit-time of quantitative MR metrics: examples for T1, T2, and DTI.
pubmed:affiliation
Department of Radiology, New York University School of Medicine, New York, New York 10016, USA.
pubmed:publicationType
Journal Article, Research Support, N.I.H., Extramural