A Semi-parametric Technique for the Quantitative Analysis of Dynamic Contrast-enhanced MR Images Based on Bayesian P-splines

TL;DR

This paper introduces a semi-parametric Bayesian P-spline method for analyzing DCE-MRI data, improving the robustness and accuracy of kinetic parameter estimation by addressing computational challenges in traditional nonlinear models.

Contribution

The paper presents a novel Bayesian P-spline approach for deconvolving DCE-MRI data, enhancing stability and flexibility over existing nonlinear pharmacokinetic models.

Findings

Method effectively estimates kinetic parameters from simulated data.

Approach successfully applied to in vivo DCE-MRI data.

Improves robustness of model fitting in dynamic contrast analysis.

Abstract

Dynamic Contrast-enhanced Magnetic Resonance Imaging (DCE-MRI) is an important tool for detecting subtle kinetic changes in cancerous tissue. Quantitative analysis of DCE-MRI typically involves the convolution of an arterial input function (AIF) with a nonlinear pharmacokinetic model of the contrast agent concentration. Parameters of the kinetic model are biologically meaningful, but the optimization of the non-linear model has significant computational issues. In practice, convergence of the optimization algorithm is not guaranteed and the accuracy of the model fitting may be compromised. To overcome this problems, this paper proposes a semi-parametric penalized spline smoothing approach, with which the AIF is convolved with a set of B-splines to produce a design matrix using locally adaptive smoothing parameters based on Bayesian penalized spline models (P-splines). It has been shown that kinetic parameter estimation can be obtained from the resulting deconvolved response function, which also includes the onset of contrast enhancement. Detailed validation of the method, both with simulated and in vivo data, is provided.