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Statistical analysis of pharmacokinetic models in dynamic contrast-enhanced magnetic resonance imaging

V.J. Schmid, B.J. Whitcher, Guang-Zhong Yang, N.J. Taylor, A.R. Padhani

Conference or Workshop Paper
Medical Image Computing and Computer Assisted Intervention (MICCAI05)
Volume 8
Issue 2

This paper assesses the estimation of kinetic parameters from dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI). Asymptotic results from likelihood-based nonlinear regression are compared with results derived from the posterior distribution using Bayesian estimation, along with the output from an established software package (MRIW). By using the estimated error from kinetic parameters, it is possible to produce more accurate clinical statistics, such as tumor size, for patients with breast tumors. Further analysis has also shown that Bayesian methods are more accurate and do not suffer from convergence problems, but at a higher computational cost.

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