NDLI: Bayesian Estimation of Smooth Parameter Maps for Dynamic Contrast-Enhanced MR Images with Block-ICM

Content Provider	IEEE Xplore Digital Library
Author	Kelm, B.M. Mueller, N. Menze, B.H. Hamprecht, F.A.
Copyright Year	2006
Description	Author affiliation: University of Heidelberg, Germany (Kelm, B.M.)
Abstract	In cancer, pathological tissue often exhibits abnormal perfusion and vascular permeability. These can be estimated by monitoring the abundance of an injected contrast medium over time, using Dynamic Contrast-Enhanced (DCE) MR Imaging. The resulting spatially resolved time curves are usually interpreted in terms of a pharmacokinetic model which is fitted by maximum likelihood. However, the resulting nonlinear least squares (NLLS) problem may exhibit spurious local optima leading to false parameter estimates at individual voxels in the generated parameter map. We propose the application of a spatial prior model in form of a generalized Gaussian Markov random field. By using information from parameter estimates at neighboring voxels and computing a maximum a posteriori solution for the whole parameter map at once, false local optima at individual voxels can be avoided. Since the number of variables gets very big for common image resolutions, standard NLLS solvers cannot be employed anymore. We therefore propose a generalized iterated conditional modes (ICM) approach operating on blocks instead of sites. Results on DCE-MR images of the prostate show less speckle noise in the resulting parameter maps. Furthermore, the mean square error (MSE) in the affected voxels is significantly smaller, thus reflecting a better fit.
Starting Page	96
Ending Page	96
File Size	327577
Page Count	1
File Format	PDF
ISBN	0769526462
DOI	10.1109/CVPRW.2006.41
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2006-06-17
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Pathology Maximum likelihood estimation Parameter estimation Bayesian methods Permeability Spatial resolution Least squares approximation Monitoring Cancer Markov random fields
Content Type	Text
Resource Type	Article

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