Paper Info
Title
Using dynamic contrast-enhanced magnetic resonance imaging data to constrain a positron emission tomography kinetic model: theory and simulations
Abstract
We show how dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) data can constrain a compartmental model for analyzing dynamic positron emission tomography (PET) data. We first develop the theory that enables the use of DCE-MRI data to separate whole tissue time activity curves (TACs) available fromdynamic PET data into individual TACs associated with the blood space, the extravascular-extracellular space (EES), and the extravascular-intracellular space (EIS). Then we simulate whole tissue TACs over a range of physiologically relevant kinetic parameter values and show that using appropriate DCE-MRI data can separate the PET TAC into the three components with accuracy that is noise dependent. The simulations show that accurate blood, EES, and EIS TACs can be obtained as evidenced by concordance correlation coefficients 0.9 between the true and estimated TACs. Additionally, provided that the estimated DCE-MRI parameters are within 10% of their true values, the errors in the PET kinetic parameters are within approximately 20% of their true values. The parameters returned by this approach may provide new information on the transport of a tracer in a variety of dynamic PET studies.
Year
DOI
Venue
2013
10.1155/2013/576470
Int. J. Biomedical Imaging
Keywords
Field
DocType
biomedical research,bioinformatics,text mining
Computer vision,Data mining,TRACER,Contrast-enhanced Magnetic Resonance Imaging,Biological system,Computer science,Kinetic model,Artificial intelligence,Positron emission tomography,Magnetic resonance imaging
Journal
Volume
ISSN
Citations 
2013,
1687-4188
0
PageRank 
References 
Authors
0.34
0
6
Name
Order
Citations
PageRank
Jacob U. Fluckiger100.68
Xia Li2163.19
Jennifer G. Whisenant300.34
Todd E. Peterson43610.02
John C Gore561641.36
Thomas E. Yankeelov6207.14