Bayesian Methods for Pharmacokinetic Models in Dynamic Contrast-Enhanced Magnetic Resonance Imaging
- Resource Type
- Periodical
- Authors
- Schmid, V. J.; Whitcher, B.; Padhani, A. R.; Taylor, N. J.; Yang, G.-Z.
- Source
- IEEE Transactions on Medical Imaging IEEE Trans. Med. Imaging Medical Imaging, IEEE Transactions on. 25(12):1627-1636 Dec, 2006
- Subject
- Bioengineering
Computing and Processing
Bayesian methods
Magnetic resonance imaging
Kinetic theory
Parameter estimation
Markov random fields
Breast neoplasms
Breast tumors
Convergence
Statistical analysis
Statistical distributions
Adaptive smoothing
Bayesian hierarchical modeling
dynamic contrast-enhanced magnetic resonance imaging
Gaussian Markov random fields
oncology
pharmacokinetic models
- Language
- ISSN
- 0278-0062
1558-254X
This paper proposes a new method for estimating kinetic parameters of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) based on adaptive Gaussian Markov random fields. Kinetic parameter estimates using neighboring voxels reduce the observed variability in local tumor regions while preserving sharp transitions between heterogeneous tissue boundaries. Asymptotic results for standard errors from likelihood- based nonlinear regression are compared with those derived from the posterior distribution using Bayesian estimation with and without neighborhood information. Application of the method to the analysis of breast tumors based on kinetic parameters has shown that the use of Bayesian analysis combined with adaptive Gaussian Markov random fields provides improved convergence behavior and more consistent morphological and functional statistics .