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
