View Item 

Show simple item record

An extended vascular model for less biased estimation of permeability parameters in DCEâ T1 images
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.accessioned2017-06-16T20:10:01Z
dc.date.available2018-08-07T15:51:22Zen
dc.date.issued2017-06
dc.identifier.citationNejad‐davarani, Siamak P. ; Bagher‐ebadian, Hassan ; Ewing, James R.; Noll, Douglas C.; Mikkelsen, Tom; Chopp, Michael; Jiang, Quan (2017). "An extended vascular model for less biased estimation of permeability parameters in DCEâ T1 images." NMR in Biomedicine 30(6): n/a-n/a.
dc.identifier.issn0952-3480
dc.identifier.issn1099-1492
dc.identifier.urihttps://hdl.handle.net/2027.42/137317
dc.publisherErlbaum
dc.publisherWiley Periodicals, Inc.
dc.subject.othervascular permeability
dc.subject.otherarterial input function
dc.subject.otherDCEâ MRI
dc.subject.otherdynamic contrast enhanced imaging
dc.subject.othervascular modeling
dc.subject.othercerebral tumors
dc.titleAn extended vascular model for less biased estimation of permeability parameters in DCEâ T1 images
dc.typeArticleen_US
dc.rights.robotsIndexNoFollow
dc.subject.hlbsecondlevelElectrical Engineering
dc.subject.hlbsecondlevelPhysics
dc.subject.hlbtoplevelEngineering
dc.subject.hlbtoplevelScience
dc.description.peerreviewedPeer Reviewed
dc.description.bitstreamurlhttps://deepblue.lib.umich.edu/bitstream/2027.42/137317/1/nbm3698_am.pdf
dc.description.bitstreamurlhttps://deepblue.lib.umich.edu/bitstream/2027.42/137317/2/nbm3698.pdf
dc.identifier.doi10.1002/nbm.3698
dc.identifier.sourceNMR in Biomedicine
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.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.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.citedreferenceGall P, Kiselev V. On the form of the residue function for brain tissue. Proc Int Soc Magn Reson Med. 2010; 18: 1795.
dc.identifier.citedreferenceSourbron S, Ingrisch M, Siefert A, Reiser M, Herrmann K. Quantification of cerebral blood flow, cerebral blood volume, and bloodâ brainâ barrier leakage with DCEâ MRI. Magn Reson Med. 2009; 62 ( 1 ): 205 â 217.
dc.identifier.citedreferenceGuzmanâ deâ Villoria JA, Fernandezâ Garcia P, Mateosâ Perez JM, Desco M. Estudio de la perfusion cerebral mediante tecnicas de susceptibilidad magnetica: tecnica y aplicaciones [Studying cerebral perfusion using magnetic susceptibility techniques: technique and applications]. Radiologia. 2012; 54 ( 3 ): 208 â 220.
dc.identifier.citedreferenceChan AA, Nelson SJ. Simplified gammaâ variate fitting of perfusion curves. In: IEEE International Symposium on Biomedical Imaging (ISBI), Arlington, VA, USA. 2004; 1067 â 1070.
dc.identifier.citedreferenceNestorov I. Wholeâ body physiologically based pharmacokinetic models. Expert Opin Drug Metab Toxicol. 2007; 3 ( 2 ): 235 â 249.
dc.identifier.citedreferenceBagherâ Ebadian H, Jain R, Nejadâ Davarani SP, et al. Model selection for DCEâ T1 studies in glioblastoma. Magn Reson Med. 2012; 68 ( 1 ): 241 â 251.
dc.identifier.citedreferenceTofts PS, Brix G, Buckley DL, et al. Estimating kinetic parameters from dynamic contrastâ enhanced T 1 â weighted MRI of a diffusable tracer: standardized quantities and symbols. J Magn Reson Imaging. 1999; 10 ( 3 ): 223 â 232.
dc.identifier.citedreferenceCalamante F. Arterial input function in perfusion MRI: a comprehensive review. Prog Nucl Magn Reson Spectrosc. 2013; 74: 1 â 32.
dc.identifier.citedreferenceSourbron SP, Buckley DL. Classic models for dynamic contrastâ enhanced MRI. NMR Biomed. 2013; 26 ( 8 ): 1004 â 1027.
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.citedreferenceLee JJ, Bretthorst GL, Derdeyn CP, et al. Dynamic susceptibility contrast MRI with localized arterial input functions. Magn Reson Med. 2010 May; 63 ( 5 ): 1305 â 1314.
dc.identifier.citedreferenceFluckiger JU, Schabel MC, DiBella EV. Toward local arterial input functions in dynamic contrastâ enhanced MRI. J Magn Reson Imaging. 2010; 32 ( 4 ): 924 â 934.
dc.identifier.citedreferenceYankeelov TE, Luci JJ, Lepage M, et al. Quantitative pharmacokinetic analysis of DCEâ MRI data without an arterial input function: a reference region model. Magn Reson Imaging. 2005; 23 ( 4 ): 519 â 529.
dc.identifier.citedreferenceNejad Davarani SP, Bagherâ Ebadian H, Ewing JR, Chopp M, Noll D, Jiang Q, eds. Analytical model of the brain vascular system for estimation of the arterial input function (AIF) at the tissue level. Presented at: WorldComp’12; July 16â 19, 2012; Las Vegas, NV.
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: 36.
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:(Spec No. 2): S112 â S120.
dc.identifier.citedreferenceEwing JR, Bagherâ Ebadian H. Model selection in measures of vascular parameters using dynamic contrastâ enhanced MRI: experimental and clinical applications. NMR Biomed. 2013; 26 ( 8 ): 1028 â 1041.
dc.identifier.citedreferenceDeoni SC, Peters TM, Rutt BK. Highâ resolution T 1 and T 2 mapping of the brain in a clinically acceptable time with DESPOT1 and DESPOT2. Magn Reson Med. 2005; 53 ( 1 ): 237 â 241.
dc.identifier.citedreferenceStrich G, Hagan PL, Gerber KH, Slutsky RA. Tissue distribution and magnetic resonance spin lattice relaxation effects of gadoliniumâ DTPA. Radiology. 1985; 154 ( 3 ): 723 â 726.
dc.identifier.citedreferenceBagherâ Ebadian H, Paudyal R, Nagaraja TN, Croxen RL, Fenstermacher JD, Ewing JR. MRI estimation of gadolinium and albumin effects on water proton. Neuroimage. 2011; 54 ( Suppl. 1 ): S176 â S179.
dc.identifier.citedreferencePeladeauâ Pigeon M, Coolens C. Computational fluid dynamics modelling of perfusion measurements in dynamic contrastâ enhanced computed tomography: development, validation and clinical applications. Phys Med Biol. 2013; 58 ( 17 ): 6111 â 6131.
dc.identifier.citedreferenceSourbron SP, Buckley DL. On the scope and interpretation of the Tofts models for DCEâ MRI. Magn Reson Med. 2011; 66 ( 3 ): 735 â 745.
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.citedreferenceLomax RG. An Introduction to Statistical Concepts. 2nd ed. Mahwah, NJ: Erlbaum; 2007.
dc.identifier.citedreferenceNejadâ Davarani SP, Bagherâ Ebadian JR, et al. A parametric model of the brain vascular system for estimation of the arterial input function (AIF) at the tissue level. NMR in Biomedicine. 2017; e3695. https://doi.org/10.1002/nbm.3695
dc.owningcollnameInterdisciplinary and Peer-Reviewed


Files in this item

Name:
nbm3698_am.pdf
Size:
2.144MB
Format:
PDF
View/Open
Name:
nbm3698.pdf
Size:
1.317MB
Format:
PDF
View/Open

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.