Pharmacokinetic Model and Arterial Input Function Selection in Dynamic Contrast Enhanced-MRI in Head and Neck Cancers

AUTHORS

Sanam Assili 1 , Anahita Fathi Kazerooni 2 , Hamidreza Saligheh Rad 2 , *

1 Tabriz University of Medical Sciences, Tabriz, IR Iran

2 Tehran University of Medical Sciences, Tehran, IR Iran

How to Cite: Assili S, Fathi Kazerooni A, Saligheh Rad H. Pharmacokinetic Model and Arterial Input Function Selection in Dynamic Contrast Enhanced-MRI in Head and Neck Cancers, Iran J Radiol. Online ahead of Print ; 11(30th Iranian Congress of Radiology):e21291. doi: 10.5812/iranjradiol.21291.

ARTICLE INFORMATION

Iranian Journal of Radiology: 11 (30th Iranian Congress of Radiology); e21291
Published Online: February 28, 2014
Article Type: Research Article
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Abstract

Introduction: Head and Neck cancers constitute 6% of all cancers. Dynamic contrast enhanced magnetic resonance imaging (DCE-MRI) has the potential to measure the physiological properties of the tissue, like vascularization, vascular leakage, blood vessel volume fraction and blood flow, all of which play important roles in oncology. DCE-MRI is an effective MR imaging technique suitable for characterizing tumor angiogenesis, which is a critical process in tumor growth and metastasis. DCE-MRI technique is performed by acquiring MR images during the passage of an exogenous contrast agent (CA) in the tissue of interest. When the acquisition time is short, it allows to get valuable information about perfusion, tracer uptake and wash-out rate (WOR). Quantitative measures of tumor vascularity could be computed by investigating the signal intensity change during CA passage with respect to the signal intensity before CA injection in the tissue of interest and a reference artery, representing the arterial input function (AIF). Accurate definition of the behavior of the tissue during CA passage highly depends on selected tracer kinetic parameters, which can be modeled using pharmacokinetic (PK) modeling approach, and the AIF selection strategy. Here, we aim to introduce the proper PK model, describing hemodynamic characteristics of head and neck cancer, and AIF selection approach.

Materials and Methods: Various PK models have been introduced in literature to be used in combination with DCE-MR imaging, each exploiting different subsets of kinetic parameters. In most tissues, CA leaks rapidly into intracellular intravascular space (EES) at a rate determined by the permeability of the micro vessels (KTrans), their surface area (PS), and blood flow (F).Two other parameters include Ve , which shows the EES volume per unit volume of tissue and Vp , that indicates the blood plasma volume per unit volume of tissue. In order to achieve the optimal tissue-specific model, it is essential to select suitable descriptive parameters of the tissue of interest. Accurate quantification of kinetic parameters highly rely on accurate characterization of AIF, which could itself be affected by many technical factors such as spatial and temporal resolution accuracy of the T1 measurements, in-flow effects, B1-inhomogenity and also many patient-related factors such as heart output rate, vascular tone, hematocrit and tracer distribution in the body and kidney. Recently, it has been proposed that measuring the contrast agent concentration in veins could be preferable in brain perfusion studies, as it could reduce the in-flow effects.

Conclusion: Results show that there are two models, namely Tofts and Brix, which can be used in head and neck cancers. One of the problems of PK modeling would be AIF selection. In order to increase the validation of AIF selection it is possible to use the vein CTCs for Tofts model to measure the reliable pharmacokinetic parameters.

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© 2014, Author(s). This is an open-access article distributed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/) which permits copy and redistribute the material just in noncommercial usages, provided the original work is properly cited.

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