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Uncertainty estimation in dynamic contrast-enhanced MRI
Umeå University, Faculty of Medicine, Department of Radiation Sciences, Radiation Physics.
Umeå University, Faculty of Medicine, Department of Radiation Sciences, Radiation Physics.
Sveriges lantbruksuniversitet, Centre of Biostochastiscs.
Lunds universitet, Medicinsk strålningsfysik.
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2013 (English)In: Magnetic Resonance in Medicine, ISSN 0740-3194, E-ISSN 1522-2594, Vol. 69, no 4, 992-1002 p.Article in journal (Refereed) Published
Abstract [en]

Using dynamic contrast-enhanced MRI (DCE-MRI), it is possible to estimate pharmacokinetic (PK) parameters that convey information about physiological properties, e.g., in tumors. In DCE-MRI, errors propagate in a nontrivial way to the PK parameters. We propose a method based on multivariate linear error propagation to calculate uncertainty maps for the PK parameters. Uncertainties in the PK parameters were investigated for the modified Kety model. The method was evaluated with Monte Carlo simulations and exemplified with in vivo brain tumor data. PK parameter uncertainties due to noise in dynamic data were accurately estimated. Noise with standard deviation up to 15% in the baseline signal and the baseline T1 map gave estimated uncertainties in good agreement with the Monte Carlo simulations. Good agreement was also found for up to 15% errors in the arterial input function amplitude. The method was less accurate for errors in the bolus arrival time with disagreements of 23%, 32%, and 29% for Ktrans, ve, and vp, respectively, when the standard deviation of the bolus arrival time error was 5.3 s. In conclusion, the proposed method provides efficient means for calculation of uncertainty maps, and it was applicable to a wide range of sources of uncertainty.

Place, publisher, year, edition, pages
Wiley-Blackwell, 2013. Vol. 69, no 4, 992-1002 p.
Keyword [en]
Uncertainty estimation, dynamic contrast-enhanced-MRI, precision analysis, accuracy
National Category
Medical Image Processing Probability Theory and Statistics
Research subject
URN: urn:nbn:se:umu:diva-49758DOI: 10.1002/mrm.24328ISI: 000316629300013OAI: diva2:457242
Available from: 2011-11-17 Created: 2011-11-17 Last updated: 2013-10-01Bibliographically 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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