Linked Life Data

17975296

Source:http://linkedlifedata.com/resource/pubmed/id/17975296

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
22
pubmed:dateCreated
2007-11-2
pubmed:abstractText
Model-free measurement of perfusion from bolus-tracking data requires a discretization of the tracer kinetic model. In this study a classification is provided of existing approaches to discretization, and the accuracy of these methods is compared. Two methods are included which are delay invariant (circulant and time shift) and three methods which are not (volterra, singular and hybrid). Simulations of magnetic resonance imaging (MRI) in the brain are performed for two tissue types (plug flow and compartment) with variable delay and dispersion times, temporal resolution and signal to noise. Simulations were compared to measurements in a patient data set. Both delay-invariant methods are equally accurate, but the circulant method is sensitive to data truncation. Overall volterra produces highest estimates of perfusion, followed by hybrid, singular and delay-invariant methods. Volterra is most accurate except in plug-flow without delay or dispersion, which represents an unrealistic tissue type. Differences between methods vanish when delay or dispersion times increase above the temporal resolution. It is concluded that when negative delays cannot be avoided or when an accurate estimate of left-right perfusion ratios is required, the time shift is the method of choice. When delays are certain to be positive and absolute accuracy is the objective, the volterra method is to be preferred.
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:status
MEDLINE
pubmed:month
Nov
pubmed:issn
0031-9155
pubmed:author
pubmed:issnType
Print
pubmed:day
21
pubmed:volume
52
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
6761-78
pubmed:meshHeading
pubmed:year
2007
pubmed:articleTitle
Deconvolution of bolus-tracking data: a comparison of discretization methods.
pubmed:affiliation
Institute of Clinical Radiology, Ludwig Maximilian University Munich, Marchioninistrasse 15, 81377 Munich, Germany. Steven.Sourbron@med.uni-muenchen.de
pubmed:publicationType
Journal Article, Comparative Study