Semi-parametric Analysis of Dynamic Contrast-Enhanced MRI Using Bayesian P-Splines

  • Volker J. Schmid
  • Brandon Whitcher
  • Guang-Zhong Yang
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4190)


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.


Response Function Arterial Input Function Contrast Agent Concentration Concentration Time Series Adaptive Smoothing 


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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Volker J. Schmid
    • 1
  • Brandon Whitcher
    • 2
  • Guang-Zhong Yang
    • 1
  1. 1.Institute for Biomedical EngineeringImperial CollegeSouth Kensington, LondonUnited Kingdom
  2. 2.Translational Medicine & Genetics, GlaxoSmithKlineGreenford, MiddlesexUnited Kingdom

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