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