Semi-parametric analysis of dynamic contrast-enhanced MRI using Bayesian P-splines

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.

Publication
n Larsen, R., Nielsen, M., Sporring, J., eds.: Medical Image Computing and Computer-Assisted Intervention – MICCAI 2006. Number 4190 in Lecture Notes in Computer Science, Berlin: Springer, pp. 679–686
Date