title: A parametric model of the brain vascular system for estimation of the arterial input function (AIF) at the tissue level.
abstract: In this paper, we introduce a novel model of the brain vascular system, which is developed based on laws of fluid dynamics and vascular morphology. This model is used to address dispersion and delay of the arterial input function (AIF) at different levels of the vascular structure and to estimate the local AIF in DCE images. We developed a method based on the simplex algorithm and Akaike information criterion to estimate the likelihood of the contrast agent concentration signal sampled in DCE images belonging to different layers of the vascular tree or being a combination of different signal levels from different nodes of this structure. To evaluate this method, we tested the method on simulated local AIF signals at different levels of this structure. Even down to a signal to noise ratio of 5.5 our method was able to accurately detect the branching level of the simulated signals. When two signals with the same power level were combined, our method was able to separate the base signals of the composite AIF at the 50% threshold. We applied this method to dynamic contrast enhanced computed tomography (DCE-CT) data, and using the parameters estimated by our method we created an arrival time map of the brain. Our model corrected AIF can be used for solving the pharmacokinetic equations for more accurate estimation of vascular permeability parameters in DCE imaging studies.
authors.affiliation: Department of Radiation Oncology, Henry Ford Health System, Detroit, Michigan, USA.|Department of Biomedical Engineering, University of Michigan, Ann Arbor, Michigan, USA.|Department of Neurology, Henry Ford Health System, Detroit, Michigan, USA.
authors.forename: Siamak P
authors.identifier: http://orcid.org/0000-0002-2415-6689
authors.initials: SP
authors.lastname: Nejad-Davarani
authors.affiliation: Department of Radiation Oncology, Henry Ford Health System, Detroit, Michigan, USA.|Department of Physics, Oakland University, Rochester, Michigan, USA.
authors.forename: Hassan
authors.initials: H
authors.lastname: Bagher-Ebadian
authors.affiliation: Department of Neurology, Henry Ford Health System, Detroit, Michigan, USA.|Department of Physics, Oakland University, Rochester, Michigan, USA.
authors.forename: James R
authors.initials: JR
authors.lastname: Ewing
authors.affiliation: Department of Biomedical Engineering, University of Michigan, Ann Arbor, Michigan, USA.
authors.forename: Douglas C
authors.initials: DC
authors.lastname: Noll
authors.affiliation: Department of Neurosurgery, Henry Ford Hospital, Detroit, Michigan, USA.
authors.forename: Tom
authors.initials: T
authors.lastname: Mikkelsen
authors.affiliation: Department of Neurology, Henry Ford Health System, Detroit, Michigan, USA.|Department of Physics, Oakland University, Rochester, Michigan, USA.
authors.forename: Michael
authors.initials: M
authors.lastname: Chopp
authors.affiliation: Department of Neurology, Henry Ford Health System, Detroit, Michigan, USA.|Department of Physics, Oakland University, Rochester, Michigan, USA.
authors.forename: Quan
authors.initials: Q
authors.lastname: Jiang
mesh_terms: D001783:Blood Flow Velocity / Q000502:physiology*; D002536:Cerebral Arteries / Q000502:physiology*; D002560:Cerebrovascular Circulation / Q000502:physiology*; D003198:Computer Simulation; D003287:Contrast Media / Q000493:pharmacokinetics; D006801:Humans; D018810:Magnetic Resonance Angiography / Q000379:methods*; D008955:Models, Cardiovascular; D008959:Models, Neurological; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity
chemical_list: D003287:Contrast Media
keywords: arterial input function; dynamic contrast enhanced imaging; laminar flow; perfusion; vascular modeling; vascular permeability