Bayesian methods for pharmacokinetic models in dynamic contrast-enhanced magnetic resonance imaging.
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.
Schmid
Volker J
VJ
Institute of Biomedical Engineering, Imperial College, London SW7 2AZ, UK.
Whitcher
Brandon
B
Padhani
Anwar R
AR
Taylor
N Jane
NJ
Yang
Guang-Zhong
GZ
D000465:Algorithms; D001499:Bayes Theorem; D001943:Breast Neoplasms / Q000175:diagnosis* / Q000378:metabolism*; D003198:Computer Simulation; D003287:Contrast Media / Q000493:pharmacokinetics*; D005260:Female; D006801:Humans; D007089:Image Enhancement / Q000379:methods*; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D007201:Indicator Dilution Techniques; D008279:Magnetic Resonance Imaging / Q000379:methods*; D008657:Metabolic Clearance Rate; D008954:Models, Biological; D015233:Models, Statistical; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity
D003287:Contrast Media