Abstract：
:Dynamic contrast-enhanced magnetic resonance imaging (MRI) is a promising tool in the evaluation of tumor physiology. From rapidly acquired images and a model for contrast agent pharmacokinetics, physiological parameters are derived. One pharmacokinetic model, the tissue homogeneity model, enables estimation of both blood flow and vessel permeability together with parameters that describe blood volume and extracellular extravascular volume fraction. However, studies have shown that parameter estimation with this model is unstable. Therefore, several initial guesses are needed for accurate estimates, which makes the estimation slow. In this study a new estimation algorithm for the tissue homogeneity model, based on Fourier domain calculations, was derived and implemented as a Matlab program. The algorithm was tested with Monte-Carlo simulations and the results were compared to an existing method that uses the adiabatic approximation. The algorithm was also tested on data from a metastasis in the brain. The comparison showed that the new algorithm gave more accurate results on the 2.5th and 97.5th percentile levels, for instance the error in blood volume was reduced by 21%. In addition, the time needed for the computations was reduced with a factor 25. It was concluded that the new algorithm can be used to speed up parameter estimation while accuracy can be gained at the same time.
journal_name
IEEE Trans Med Imagingjournal_title
IEEE transactions on medical imagingauthors
Garpebring A,Ostlund N,Karlsson Mdoi
10.1109/TMI.2009.2016212subject
Has Abstractpub_date
2009-09-01 00:00:00pages
1375-83issue
9eissn
0278-0062issn
1558-254Xjournal_volume
28pub_type
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journal_title：IEEE transactions on medical imaging
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journal_title：IEEE transactions on medical imaging
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journal_title：IEEE transactions on medical imaging
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journal_title：IEEE transactions on medical imaging
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doi：10.1109/42.158942
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journal_title：IEEE transactions on medical imaging
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journal_title：IEEE transactions on medical imaging
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journal_title：IEEE transactions on medical imaging
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