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Archive (2016–2006)

Maximum Entropy Approach in Dynamic Contrast-Enhanced Magnetic Resonance Imaging

Journal: Methods of Information in Medicine
Subtitle: A journal stressing, for more than 50 years, the methodology and scientific fundamentals of organizing, representing and analyzing data, information and knowledge in biomedicine and health care
ISSN: 0026-1270
DOI: https://doi.org/10.3414/ME17-01-0027
Issue: 2017 (Vol. 56): Issue 6 2017
Pages: 461-468

Maximum Entropy Approach in Dynamic Contrast-Enhanced Magnetic Resonance Imaging

Z. Amini Farsani (1, 2), V. J. Schmid (1)

(1) Bioimaging Group, Department of Statistics, Ludwig-Maximilians-University of Munich, Munich, Germany; (2) Department of Statistics, Lorestan University, Khorramabad, Iran

Keywords

Kullback-Leibler Divergence, Image processing, Bayesian statistics, kinetic parameter, maximum entropy method, maximum a posterior probability

Summary

Background: In the estimation of physiological kinetic parameters from Dynamic Contrast-Enhanced Magnetic Resonance Imaging (DCE-MRI) data, the determination of the arterial input function (AIF) plays a key role. Objectives: This paper proposes a Bayesian method to estimate the physiological parameters of DCE-MRI along with the AIF in situations, where no measurement of the AIF is available. Methods: In the proposed algorithm, the maximum entropy method (MEM) is combined with the maximum a posterior approach (MAP). To this end, MEM is used to specify a prior probability distribution of the unknown AIF. The ability of this method to estimate the AIF is validated using the Kullback-Leibler divergence. Subsequently, the kinetic parameters can be estimated with MAP. The proposed algorithm is evaluated with a data set from a breast cancer MRI study. Results: The application shows that the AIF can reliably be determined from the DCE-MRI data using MEM. Kinetic parameters can be estimated subsequently. Conclusions: The maximum entropy method is a powerful tool to reconstructing images from many types of data. This method is useful for generating the probability distribution based on given information. The proposed method gives an alternative way to assess the input function from the existing data. The proposed method allows a good fit of the data and therefore a better estimation of the kinetic parameters. In the end, this allows for a more reliable use of DCE-MRI.

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