An extended vascular model for less biased estimation of permeability parameters in DCE-T1 images

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NMR in biomedicine


One of the key elements in Dynamic Contrast Enhanced (DCE) Image Analysis is the Arterial Input Function (AIF). Traditionally, in DCE studies a global AIF sampled from a major artery or vein is used for estimating the vascular permeability parameters; however, not addressing dispersion and delay of the AIF at the tissue level can lead to biased estimates of these parameters. To find less biased estimates of vascular permeability parameters, a vascular model of the cerebral vascular system is proposed that considers effects of dispersion of the Arterial Input Function (AIF) in the vessel branches, as well as extravasation of the contrast agent to the extravascular-extracellular space. Profiles of the contrast agent concentration were simulated for different branching levels of the vascular structure, combined with the effects of vascular leakage. To estimate the permeability parameters, the extended model was applied to these simulated signals and also to DCE-T1 (Dynamic Contrast Enhanced–T1) images of patients with Glioblastoma Multiforme tumors. The simulation study showed that compared to the case of solving the pharmacokinetic equation with a global AIF, using the local AIF that is corrected by the vascular model can give less biased estimates of the permeability parameters (Ktrans, vp and Kb). Applying the extended model to signals sampled from different areas of the DCE-T1 image showed that it is able to explain the contrast agent concentration profile both in the normal areas and the tumor area where effects of vascular leakage exist. Differences in the values of the permeability parameters estimated in these images using the local and global AIFs followed the same trend as the simulation study. These results demonstrate that the vascular model can be a useful tool for obtaining more accurate estimation of parameters in DCE studies.

Medical Subject Headings

Capillary Permeability; Computer Simulation; Contrast Media; Humans; Magnetic Resonance Imaging; Models, Biological

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