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Semi-parametric analysis of dynamic contrast enhanced MR images using Bayesian P-Splines

V.J. Schmid, B.J. Whitcher, Guang-Zhong Yang

Conference or Workshop Paper
Medical Image Computing and Computer-Assisted Intervention - MICCAI 2006, 9th International Conference, Copenhagen, Denmark
October, 2006
Lecture Notes in Computer Science
Volume 4190
Springer
DOI 10.1007/11866565
Abstract

Current approaches to quantitative analysis of DCE-MRI with non-linear models involve the convolution of an arterial input function (AIF) with the contrast agent concentration at a voxel or regional level. Full quantification provides meaningful biological parameters but is complicated by the issues related to convergence, (de-)convolution of the AIF, and goodness of fit. To overcome these problems, this paper presents a penalized spline smoothing approach to model the data in a semi-parametric way. With this method, the AIF is convolved with a set of B-splines to produce the design matrix, and modeling of the resulting deconvolved biological parameters is obtained in a way that is similar to the parametric models. Further kinetic parameters are obtained by fitting a non-linear model to the estimated response function and detailed validation of the method, both with simulated and in vivo data is provided.

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