Show simple item record

A parametric model of the brain vascular system for estimation of the arterial input function (AIF) at the tissue level

dc.contributor.authorNejad‐davarani, Siamak P.
dc.contributor.authorBagher‐ebadian, Hassan
dc.contributor.authorEwing, James R.
dc.contributor.authorNoll, Douglas C.
dc.contributor.authorMikkelsen, Tom
dc.contributor.authorChopp, Michael
dc.contributor.authorJiang, Quan
dc.date.accessioned2017-04-14T15:10:59Z
dc.date.available2018-07-09T17:42:24Zen
dc.date.issued2017-05
dc.identifier.citationNejad‐davarani, Siamak P. ; Bagher‐ebadian, Hassan ; Ewing, James R.; Noll, Douglas C.; Mikkelsen, Tom; Chopp, Michael; Jiang, Quan (2017). "A parametric model of the brain vascular system for estimation of the arterial input function (AIF) at the tissue level." NMR in Biomedicine 30(5): n/a-n/a.
dc.identifier.issn0952-3480
dc.identifier.issn1099-1492
dc.identifier.urihttps://hdl.handle.net/2027.42/136452
dc.publisherUpper Saddle River, NJ
dc.publisherWiley Periodicals, Inc.
dc.subject.othervascular permeability
dc.subject.otherarterial input function
dc.subject.otherlaminar flow
dc.subject.otherperfusion
dc.subject.othervascular modeling
dc.subject.otherdynamic contrast enhanced imaging
dc.titleA parametric model of the brain vascular system for estimation of the arterial input function (AIF) at the tissue level
dc.typeArticleen_US
dc.rights.robotsIndexNoFollow
dc.subject.hlbsecondlevelElectrical Engineering
dc.subject.hlbsecondlevelPhysics
dc.subject.hlbtoplevelScience
dc.subject.hlbtoplevelEngineering
dc.description.peerreviewedPeer Reviewed
dc.description.bitstreamurlhttps://deepblue.lib.umich.edu/bitstream/2027.42/136452/1/nbm3695.pdf
dc.description.bitstreamurlhttps://deepblue.lib.umich.edu/bitstream/2027.42/136452/2/nbm3695_am.pdf
dc.identifier.doi10.1002/nbm.3695
dc.identifier.sourceNMR in Biomedicine
dc.identifier.citedreferenceEsposito A. A simplified method for analyzing circuits by analogy. Mach Des. 1969; 173 â 177.
dc.identifier.citedreferenceHrabe J, Lewis DP. Two analytical solutions for a model of pulsed arterial spin labeling with randomized blood arrival times. J Magn Reson. 2004; 167 ( 1 ): 49 â 55.
dc.identifier.citedreferenceOzyurt O, Dincer A, Ozturk C. A modified version of Hrabeâ Lewis model to account dispersion of labeled bolus in arterial spin labeling. Proc Int Soc Magn Reson Med. 2010; 18: 4065.
dc.identifier.citedreferenceHernandezâ Garcia L, Lee GR, Vazquez AL, Yip CY, Noll DC. Quantification of perfusion fMRI using a numerical model of arterial spin labeling that accounts for dynamic transit time effects. Magn Reson Med. 2005; 54 ( 4 ): 955 â 964.
dc.identifier.citedreferenceKazan SM, Chappell MA, Payne SJ. Modeling the effects of flow dispersion in arterial spin labeling. IEEE Trans Biomed Eng. 2009; 56 ( 6 ): 1635 â 1643.
dc.identifier.citedreferenceGallichan D, Jezzard P. Modeling the effects of dispersion and pulsatility of blood flow in pulsed arterial spin labeling. Magn Reson Med. 2008; 60 ( 1 ): 53 â 63.
dc.identifier.citedreferenceChappell MA, Woolrich MW, Kazan S, Jezzard P, Payne SJ, MacIntosh BJ. Modeling dispersion in arterial spin labeling: validation using dynamic angiographic measurements. Magn Reson Med. 2013; 69 ( 2 ): 563 â 570.
dc.identifier.citedreferenceGall P, Guether M, Kiselev V. Model of blood transport couples delay and dispersion and predicts ASL bolus measurements. Proc Int Soc Magn Reson Med. 2010; 18: 1736.
dc.identifier.citedreferenceKellner E, Gall P, Gunther M, et al. Blood tracer kinetics in the arterial tree. PLoS One. 2014; 9 ( 10 ): e109230:
dc.identifier.citedreferenceLee SE, Lee SW, Fischer PF, Bassiouny HS, Loth F. Direct numerical simulation of transitional flow in a stenosed carotid bifurcation. J Biomechanics. 2008; 41 ( 11 ): 2551 â 2561.
dc.identifier.citedreferenceTruskey GA, Yuan F, Katz DF. Transport Phenomena in Biological Systems. 2nd ed. Pearson Prentice Hall: Upper Saddle River, NJ; 2009.
dc.identifier.citedreferenceGall P, Petersen ET, Golay X, Kiselev V. Delay and dispersion in DSC perfusion derived from a vascular tree model predicts ASL measurements. Proc Int Soc Magn Reson Med. 2008; 16: 627.
dc.identifier.citedreferenceMurray CD. The physiological principle of minimum work: I. The vascular system and the cost of blood volume. Proc Natl Acad Sci U S A. 1926; 12 ( 3 ): 207 â 214.
dc.identifier.citedreferenceSherman TF. On connecting large vessels to small. The meaning of Murray’s law. J Gen Physiol. 1981; 78 ( 4 ): 431 â 453.
dc.identifier.citedreferenceTurner R. How much cortex can a vein drain? Downstream dilution of activationâ related cerebral blood oxygenation changes. Neuroimage. 2002; 16 ( 4 ): 1062 â 1067.
dc.identifier.citedreferenceFerrara LA, Mancini M, Iannuzzi R, et al. Carotid diameter and blood flow velocities in cerebral circulation in hypertensive patients. Stroke. 1995; 26 ( 3 ): 418 â 421.
dc.identifier.citedreferenceUflacker R. Atlas of Vascular Anatomy: an Angiographic Approach. 2nd ed. Philadelphia, PA: Lippincott Williams & Wilkins; 2007.
dc.identifier.citedreferenceWright SN, Kochunov P, Mut F, et al. Digital reconstruction and morphometric analysis of human brain arterial vasculature from magnetic resonance angiography. Neuroimage. 2013; 82C: 170 â 181.
dc.identifier.citedreferenceLagarias JC, Reeds JA, Wright MH, Wright PE. Convergence properties of the Nelderâ Mead simplex method in low dimensions. SIAM J Optim. 1998; 9 ( 1 ): 36.
dc.identifier.citedreferencePosada D, Buckley TR. Model selection and model averaging in phylogenetics: advantages of Akaike information criterion and Bayesian approaches over likelihood ratio tests. Syst Biol. 2004; 53 ( 5 ): 793 â 808.
dc.identifier.citedreferenceBurnham KP, Anderson DR. Model Selection and Inference: a Practical Informationâ Theoretic Approach. New York: Springer; 1998.
dc.identifier.citedreferenceBrix G, Zwick S, Kiessling F, Griebel J. Pharmacokinetic analysis of tissue microcirculation using nested models: multimodel inference and parameter identifiability. Med Phys. 2009; 36 ( 7 ): 2923 â 2933.
dc.identifier.citedreferenceOgura A, Miyai A, Maeda F, Hongoh T, Kikumoto R. Comparison of contrast resolution between dynamic MRI and dynamic CT in liver scanning. Nihon Hoshasen Gijutsu Gakkai zasshi. 2002; 58 ( 2 ): 286 â 291.
dc.identifier.citedreferenceKudo K, Terae S, Katoh C, et al. Quantitative cerebral blood flow measurement with dynamic perfusion CT using the vascularâ pixel elimination method: comparison with H 2 15 O positron emission tomography. Am J Neuroradiol. 2003; 24 ( 3 ): 419 â 426.
dc.identifier.citedreferenceO’Connor JP, Tofts PS, Miles KA, Parkes LM, Thompson G, Jackson A. Dynamic contrastâ enhanced imaging techniques: CT and MRI. Br J Radiol. 2011; 84 ( 2 ): S112 â S120.
dc.identifier.citedreferenceIbaraki M, Ito H, Shimosegawa E, et al. Cerebral vascular mean transit time in healthy humans: a comparative study with PET and dynamic susceptibility contrastâ enhanced MRI. J Cereb Blood Flow Metab. 2007; 27 ( 2 ): 404 â 413.
dc.identifier.citedreferenceNejadâ Davarani SP, Bagherâ Ebadian H, Ewing JR, et al. An extended vascular model for less biased estimation of permeability parameters in DCEâ T1 images. NMR in Biomedicine. 2017;e3698. https://doi.org/10.1002/nbm.3698.
dc.identifier.citedreferenceIto H, Kanno I, Iida H, et al. Arterial fraction of cerebral blood volume in humans measured by positron emission tomography. Ann Nucl Med. 2001; 15 ( 2 ): 111 â 116.
dc.identifier.citedreferenceChan AA, Nelson SJ. Simplified gammaâ variate fitting of perfusion curves. In: 2th IEEE international symposium on biomedical imaging (ISBI), Arlington, VA, USA. 2004; 1067 â 1070.
dc.identifier.citedreferenceNestorov I. Wholeâ body physiologically based pharmacokinetic models. Exp Opin Drug Metab Toxicol. 2007; 3 ( 2 ): 235 â 249.
dc.identifier.citedreferenceSherwin SJ, Franke V, Peiró J, Parker KH. Oneâ dimensional modelling of a vascular network in spaceâ time variables. J Eng Math. 2003; 47: 217 â 250.
dc.identifier.citedreferenceBlanco PJ, Pivello MR, Urquiza SA, Feijoo RA. On the potentialities of 3Dâ 1D coupled models in hemodynamics simulations. J Biomechanics. 2009; 42 ( 7 ): 919 â 930.
dc.identifier.citedreferenceBagherâ Ebadian H, Jafariâ Khouzani K, Soltanianâ Zadeh H, Ewing JR. A blood circulatory model to estimate the arterial input function in MR brain perfusion studies. Proc Int Soc Magn Reson Med. 2008; 16: 3260.
dc.identifier.citedreferenceNoorizadeh A, Bagherâ Ebadian H, Faghihi R, Narang J, Jain R, Ewing JR. Input function detection in MR brain perfusion using a blood circulatory model based algorithm. Proc Int Soc Magn Reson Med. 2010; 18: 5102.
dc.identifier.citedreferenceCalamante F, Morup M, Hansen LK. Defining a local arterial input function for perfusion MRI using independent component analysis. Magn Reson Med. 2004 Oct; 52 ( 4 ): 789 â 797.
dc.identifier.citedreferenceMouridsen K, Friston K, Hjort N, Gyldensted L, Ostergaard L, Kiebel S. Bayesian estimation of cerebral perfusion using a physiological model of microvasculature. Neuroimage. 2006; 33 ( 2 ): 570 â 579.
dc.identifier.citedreferenceCalamante F, Gadian DG, Connelly A. Quantification of bolusâ tracking MRI: improved characterization of the tissue residue function using Tikhonov regularization. Magn Reson Med. 2003; 50 ( 6 ): 1237 â 1247.
dc.identifier.citedreferenceCalamante F, Gadian DG, Connelly A. Delay and dispersion effects in dynamic susceptibility contrast MRI: simulations using singular value decomposition. Magn Reson Med. 2000; 44 ( 3 ): 466 â 473.
dc.identifier.citedreferenceCebral JR, Yim PJ, Lohner R, Soto O, Choyke PL. Blood flow modeling in carotid arteries with computational fluid dynamics and MR imaging. Acad Radiol. 2002; 9 ( 11 ): 1286 â 1299.
dc.owningcollnameInterdisciplinary and Peer-Reviewed


Files in this item

Show simple item record

Remediation of Harmful Language

The University of Michigan Library aims to describe library materials in a way that respects the people and communities who create, use, and are represented in our collections. Report harmful or offensive language in catalog records, finding aids, or elsewhere in our collections anonymously through our metadata feedback form. More information at Remediation of Harmful Language.

Accessibility

If you are unable to use this file in its current format, please select the Contact Us link and we can modify it to make it more accessible to you.