PDF herunterladen
Methods Inf Med 2017; 56(06): 461-468
DOI: 10.3414/ME17-01-0027
Original Articles
Schattauer GmbH

Maximum Entropy Approach in Dynamic Contrast-Enhanced Magnetic Resonance Imaging

Authors

  • Zahra Amini Farsani

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

  • Volker J. Schmid

    1   Bioimaging Group, Department of Statistics, Ludwig-Maximilians-University of Munich, Munich, Germany
Weitere Informationen

Publikationsverlauf

received: 16. März 2017

accepted: 26. September 2017

Publikationsdatum:
10. Februar 2018 (online)

   Lizenzen und Reprints
Preview

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.

Keywords

Bayesian statistics - image processing - kinetic parameter - maximum entropy method - maximum a posterior probability - Kullback-Leibler divergence