DocumentCode :
1396772
Title :
Consolidation of common parameters from multiple fits in dynamic PET data analysis
Author :
Huesman, Ronald H. ; Coxson, Pamela G.
Author_Institution :
California Univ., Berkeley, CA, USA
Volume :
16
Issue :
5
fYear :
1997
Firstpage :
675
Lastpage :
683
Abstract :
In dynamic positron emission tomography (PET) data analysis, regions of interest (ROIs) are analyzed by fitting a parametric model to the time-activity curve acquired after a radio-labeled tracer has been introduced into the patient´s bloodstream. This procedure can be carried out for multiple ROIs and/or multiple injections of the same or a different radiopharmaceutical. The approach presented here takes advantage of prior knowledge that some of the parameters of those multiple fits are the same. Reduction of the total number of parameters to be estimated results in smaller statistical uncertainty for all parameter estimates, especially those common to multiple fits.
Keywords :
maximum likelihood estimation; medical image processing; parameter estimation; positron emission tomography; common parameters consolidation; dynamic PET data analysis; medical diagnostic imaging; multiple fits; multiple injections; nuclear medicine; parametric model fitting; patient´s bloodstream; radiolabeled tracer; radiopharmaceutical; regions of interest; statistical uncertainty; time-activity curve; Approximation methods; Biomedical imaging; Blood; Curve fitting; Data analysis; Maximum likelihood estimation; Parameter estimation; Parametric statistics; Positron emission tomography; Uncertainty; Algorithms; Animals; Artifacts; Brain; Glucose; Humans; Image Processing, Computer-Assisted; Likelihood Functions; Models, Biological; Models, Statistical; Radionuclide Ventriculography; Radiopharmaceuticals; Time Factors; Tomography, Emission-Computed;
fLanguage :
English
Journal_Title :
Medical Imaging, IEEE Transactions on
Publisher :
ieee
ISSN :
0278-0062
Type :
jour
DOI :
10.1109/42.640758
Filename :
640758
Link To Document :
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