Semi-parametric Analysis of Dynamic Contrast-Enhanced MRI Using Bayesian P-Splines
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.
KeywordsResponse Function Arterial Input Function Contrast Agent Concentration Concentration Time Series Adaptive Smoothing
- 9.Tofts, P., Kermode, A.: Measurement of the blood-brain barrier permeability and leakage space using dynamic MR imaging–1. Fundamental concepts. Magnetic Resonance in Medicine 17, 357–367 (1991)Google Scholar
- 13.Ah-See, M.L.W., et al.: Does vascular imaging with MRI predict response to neoadjuvant chemotherapy in primary breast cancer? Journal of Clinical Oncology (Meeting Abstracts) 22, 582 (2004)Google Scholar