title: 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
authors.affiliation: Institute for Biomedical Engineering, Imperial College, South Kensington, London SW7 2AZ, United Kingdom. v.schmid@imperial.ac.uk
authors.forename: Volker J
authors.initials: VJ
authors.lastname: Schmid
authors.forename: Brandon
authors.initials: B
authors.lastname: Whitcher
authors.forename: Guang-Zhong
authors.initials: GZ
authors.lastname: Yang
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D001499:Bayes Theorem; D001943:Breast Neoplasms / Q000473:pathology*; D003287:Contrast Media; D019786:Gadolinium DTPA; D006801:Humans; D007089:Image Enhancement / Q000379:methods*; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D008279:Magnetic Resonance Imaging / Q000379:methods*; D008954:Models, Biological; D015233:Models, Statistical; D009716:Numerical Analysis, Computer-Assisted; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity
chemical_list: D003287:Contrast Media; D019786:Gadolinium DTPA