Journal
MAGNETIC RESONANCE IMAGING
Volume 23, Issue 4, Pages 519-529Publisher
ELSEVIER SCIENCE INC
DOI: 10.1016/j.mri.2005.02.013
Keywords
DCE-MRI; arterial input function; pharmacokinetics; reference region model
Funding
- NCI NIH HHS [T32 CA009592, R25 CA092043] Funding Source: Medline
- NIBIB NIH HHS [R01 EB000461, 5R01 EB00461] Funding Source: Medline
- NCPDCID CDC HHS [NCI 1R25 CA92043] Funding Source: Medline
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Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) can assess tumor perfusion, microvascular vessel wall permeability and extravascular-extracellular volume fraction. Analysis of DCE-MRI data is usually based on indicator dilution theory that requires knowledge of the concentration of the contrast agent in the blood plasma, the arterial input function (AIF). A method is presented that compares the tissues of interest (TOI) curve shape to that of a reference region (RR), thereby eliminating the need for direct AIF measurement. By assigning literature values for K-trans (the blood perfusion-vessel permeability product) and v(c) (extravascular extracellular volume fraction) in a reference tissue, it is possible to extract the K-trans and v(c) values for a TOI without knowledge of the AIF. The operational RR equation for DCE-MRI analysis is derived, and its sensitivity to noise and incorrect assignment of the RR parameters is tested via simulations. The method is robust at noise levels of 10%, returning accurate (+/- 20% in the worst case) and precise (+/- 15% in the worst case) values. Errors in the TOI K-trans and v(e) values scale approximately linearly with the errors in the assigned RR K-trans and v(e) values. The methodology is then applied to a Lewis Lung Carcinoma mouse tumor model. A slowly enhancing TOI yielded K-trans = 0.039 +/- 0.002 min(-1) and v(e) = 0.46 +/- 0.01, while a rapidly enhancing region yielded K-trans=0.35 +/- 0.05 min(-1) and v(e)=0.31 +/- 0.01. Parametric K-trans and v(e) mappings manifested a tumor periphery with elevated K-trans (> 0.30 min(-1)) and v(e) (> 0.30) values. The main advantage of the RR approach is that it allows for quantitative assessment of tissue properties without having to obtain high temporal resolution images to characterize an AIF. This allows for acquiring images with higher spatial resolution and/or SNR, and therefore, increased ability to probe tissue heterogeneity. (c) 2005 Elsevier Inc. All rights reserved.
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