Estimating pharmacokinetic parameters from Dynamic Contrast-Enhanced T 1-weighted MRI using a three level hierarchical Bayesian model

Bouchebbah Kahina () and Zougab Nabil ()
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Bouchebbah Kahina: Bejaia University, Faculty of Exact Sciences, Department of Operational Research, Bejaia 06000, Algeria
Zougab Nabil: Bejaia University, Faculty of Technology, Department of Electrical Engineering; and Bejaia University, Faculty of Technology, LaMOS Laboratory, Bejaia 06000, Algeria

Monte Carlo Methods and Applications, 2024, vol. 30, issue 4, 437-448

Abstract: Nowadays, Dynamic Contrast Enhanced MRI (DCE-MRI) is becoming the most widely explored technique in clinical practice for tumor assessment. In acquiring DCE-MRI, a contrast agent (CA), also called tracer, is injected into the blood flow before or during the acquisition of a time series of T 1 {T_{1}} -weighted images with fast imaging techniques. When the CA goes through the tissue, MR signal intensity measurements in voxels of the region of interest (ROI) are registered and used to calculate the CA concentration in each voxel. The Tofts models have become standard for the analysis of DCE-MRI and which express tissue CA concentration C ⁢ ( t ) {C(t)} as function of time t. The analysis of quantitative parameters in DCE-MRI provides the quantitative criterion as a reference rather than relying only on the shape of the DCE-curve, as it is used for diagnosis of prostate cancer (PCa). This study aim to provide a new thinking in quantitative analysis which may therefore improve diagnostic accuracy for detection of prostate cancer and could be used in patient baseline prediction and guide management. A hierarchical Bayesian model was built to estimate the values of the four pharmacokinetic parameters ( K trans {K_{\mathrm{trans}}} , k ep {k_{\mathrm{ep}}} , υ p {\upsilon_{\mathrm{p}}} , υ e {\upsilon_{\mathrm{e}}} ) for both prostate healthy and lesion tissues in the peripheral zone. This estimation is important because it help to understand the behavior of the CA in the body and how this latter reacts to the CA in order to emphasize the expectation or the absence of prostate lesion during the diagnosis step.

Keywords: Magnetic resonance imaging (MRI); prostate cancer; pharmacokinetic models; hierarchical Bayesian model; MCMC algorithm (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1515/mcma-2024-2018

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