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A novel estimation method for physiological parameters in dynamic contrast-enhanced MRI: application of a distributed parameter model using Fourier-domain calculations
Umeå University, Faculty of Medicine, Department of Radiation Sciences, Radiation Physics.
Umeå University, Faculty of Medicine, Department of Radiation Sciences, Radiation Physics.
Umeå University, Faculty of Medicine, Department of Radiation Sciences, Radiation Physics.
2009 (English)In: IEEE Transactions on Medical Imaging, ISSN 0278-0062, E-ISSN 1558-254X, Vol. 28, no 9, 1375-1383 p.Article in journal (Refereed) Published
Abstract [en]

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.

Place, publisher, year, edition, pages
2009. Vol. 28, no 9, 1375-1383 p.
Keyword [en]
Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI), parameter estimation, tissue homogeneity models, tracer kinetics
National Category
Medical Image Processing
URN: urn:nbn:se:umu:diva-25803DOI: 10.1109/TMI.2009.2016212PubMedID: 19278930OAI: diva2:233923
Available from: 2009-09-03 Created: 2009-09-03 Last updated: 2011-11-18Bibliographically approved
In thesis
1. Contributions to quantitative dynamic contrast-enhanced MRI
Open this publication in new window or tab >>Contributions to quantitative dynamic contrast-enhanced MRI
2011 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

Background: Dynamic contrast-enhanced MRI (DCE-MRI) has the potential to produce images of physiological quantities such as blood flow, blood vessel volume fraction, and blood vessel permeability. Such information is highly valuable, e.g., in oncology. The focus of this work was to improve the quantitative aspects of DCE-MRI in terms of better understanding of error sources and their effect on estimated physiological quantities.

Methods: Firstly, a novel parameter estimation algorithm was developed to overcome a problem with sensitivity to the initial guess in parameter estimation with a specific pharmacokinetic model. Secondly, the accuracy of the arterial input function (AIF), i.e., the estimated arterial blood contrast agent concentration, was evaluated in a phantom environment for a standard magnitude-based AIF method commonly used in vivo. The accuracy was also evaluated in vivo for a phase-based method that has previously shown very promising results in phantoms and in animal studies. Finally, a method was developed for estimation of uncertainties in the estimated physiological quantities.

Results: The new parameter estimation algorithm enabled significantly faster parameter estimation, thus making it more feasible to obtain blood flow and permeability maps from a DCE-MRI study. The evaluation of the AIF measurements revealed that inflow effects and non-ideal radiofrequency spoiling seriously degrade magnitude-based AIFs and that proper slice placement and improved signal models can reduce this effect. It was also shown that phase-based AIFs can be a feasible alternative provided that the observed difficulties in quantifying low concentrations can be resolved. The uncertainty estimation method was able to accurately quantify how a variety of different errors propagate to uncertainty in the estimated physiological quantities.

Conclusion: This work contributes to a better understanding of parameter estimation and AIF quantification in DCE-MRI. The proposed uncertainty estimation method can be used to efficiently calculate uncertainties in the parametric maps obtained in DCE-MRI.

Place, publisher, year, edition, pages
Umeå: Umeå Universitet, 2011. 108 p.
Umeå University medical dissertations, ISSN 0346-6612 ; 1457
Dynamic contrast-enhanced MRI, quantitative imaging, parameter estimation, uncertainty estimation, arterial input function
National Category
Medical Image Processing
Research subject
urn:nbn:se:umu:diva-49773 (URN)978-91-7459-313-6 (ISBN)
Public defence
2011-12-10, Bergasalen, byggnad 27, Norrlands universitetssjukhus, Umeå, 10:00 (English)
Available from: 2011-11-18 Created: 2011-11-17 Last updated: 2011-11-22Bibliographically approved

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