Delay Correction for the Assessment of Blood-Brain Barrier Permeability Using First-Pass Dynamic Perfusion CT

T. Schneider; J. Hom; J. Bredno; J.W. Dankbaar; S.-C. Cheng; M. Wintermark

doi:10.3174/ajnr.A2152

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Delay Correction for the Assessment of Blood-Brain Barrier Permeability Using First-Pass Dynamic Perfusion CT

T. Schneider, J. Hom, J. Bredno, J.W. Dankbaar, S.-C. Cheng and M. Wintermark

American Journal of Neuroradiology August 2011, 32 (7) E134-E138; DOI: https://doi.org/10.3174/ajnr.A2152

T. Schneider

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J. Hom

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J. Bredno

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J.W. Dankbaar

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S.-C. Cheng

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M. Wintermark

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vol. 32 no. 7 E134-E138

DOI

https://doi.org/10.3174/ajnr.A2152

PubMed

20538824

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American Journal of Neuroradiology

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0195-6108

Online ISSN

1936-959X

History

Received December 26, 2009
Accepted after revision March 23, 2010
Published online August 12, 2011.

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Latest version (November 24, 2010 - 06:44).
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Author Information

T. Schneidera,
J. Homa,
J. Brednoc,
J.W. Dankbaara,d,
S.-C. Chengb and
M. Wintermarka

^aFrom the Department of Radiology, Neuroradiology Section (T.S., J.H., J.W.D., M.W.)
^bDepartment of Epidemiology and Biostatistics (S.-C.C.), University of California, San Francisco, San Francisco, California
^cClinical Sites Research Program (J.B.), CT Clinical Science Group and Philips Research North America, Philips Healthcare, Briarcliff Manor, New York
^dDepartment of Radiology (J.W.D.), University Medical Center, Utrecht, the Netherlands.

Please address correspondence to Max Wintermark, MD, MAS, Department of Radiology, Division of Neuroradiology, University of Virginia Health System, P.O. Box 800170, Charlottesville, VA, 22908-0170; e-mail: mw4v{at}virginia.edu

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A Fast Nonlinear Regression Method for Estimating Permeability in CT Perfusion Imaging

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Measurement of blood–brain barrier permeability in acute ischemic stroke using standard first-pass perfusion CT data

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Medical Physics 2015 42 8
Improving low-dose blood–brain barrier permeability quantification using sparse high-dose induced prior for Patlak model

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Medical Image Analysis 2014 18 6
Focal Low and Global High Permeability Predict the Possibility, Risk, and Location of Hemorrhagic Transformation following Intra-Arterial Thrombolysis Therapy in Acute Stroke

Y. Li, Y. Xia, H. Chen, N. Liu, A. Jackson, M. Wintermark, Y. Zhang, J. Hu, B. Wu, W. Zhang, J. Tu, Z. Su, G. Zhu

American Journal of Neuroradiology 2017 38 9
Quantification of blood-to-brain transfer rate in multiple sclerosis

Saeid Taheri, Gary A. Rosenberg, Corey Ford

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Determination of vascular permeability coefficients under slow luminal filling

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Microvascular Research 2013 90
Predicting Transformation to Type 2 Parenchymal Hematoma in Acute Ischemic Stroke by CT Permeability Imaging

K. Lin

American Journal of Neuroradiology 2011 32 6
Comparison of blind deconvolution- and Patlak analysis-based methods for determining vascular permeability

Joe Tien, Xuanyue Li, Raleigh M. Linville, Evan J. Feldman

Microvascular Research 2021 133

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T. Schneider, J. Hom, J. Bredno, J.W. Dankbaar, S.-C. Cheng, M. Wintermark

Delay Correction for the Assessment of Blood-Brain Barrier Permeability Using First-Pass Dynamic Perfusion CT

American Journal of Neuroradiology Aug 2011, 32 (7) E134-E138; DOI: 10.3174/ajnr.A2152

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Delay Correction for the Assessment of Blood-Brain Barrier Permeability Using First-Pass Dynamic Perfusion CT

T. Schneider, J. Hom, J. Bredno, J.W. Dankbaar, S.-C. Cheng, M. Wintermark

American Journal of Neuroradiology Aug 2011, 32 (7) E134-E138; DOI: 10.3174/ajnr.A2152

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Cited By...

Focal Low and Global High Permeability Predict the Possibility, Risk, and Location of Hemorrhagic Transformation following Intra-Arterial Thrombolysis Therapy in Acute Stroke

Crossref (9)
Google Scholar

This article has been cited by the following articles in journals that are participating in Crossref Cited-by Linking.

A Fast Nonlinear Regression Method for Estimating Permeability in CT Perfusion Imaging

Edwin Bennink, Alan J Riordan, Alexander D Horsch, Jan Willem Dankbaar, Birgitta K Velthuis, Hugo W de Jong

Journal of Cerebral Blood Flow & Metabolism 2013 33 11
Measurement of blood–brain barrier permeability in acute ischemic stroke using standard first-pass perfusion CT data

Giang Truong Nguyen, Alan Coulthard, Andrew Wong, Nabeel Sheikh, Robert Henderson, John D. O'Sullivan, David C. Reutens

NeuroImage: Clinical 2013 2
CT perfusion analysis by nonlinear regression for predicting hemorrhagic transformation in ischemic stroke

Edwin Bennink, Alexander D. Horsch, Jan Willem Dankbaar, Birgitta K. Velthuis, Max A. Viergever, Hugo W. A. M. de Jong

Medical Physics 2015 42 8
Improving low-dose blood–brain barrier permeability quantification using sparse high-dose induced prior for Patlak model

Ruogu Fang, Kolbeinn Karlsson, Tsuhan Chen, Pina C. Sanelli

Medical Image Analysis 2014 18 6
Focal Low and Global High Permeability Predict the Possibility, Risk, and Location of Hemorrhagic Transformation following Intra-Arterial Thrombolysis Therapy in Acute Stroke

Y. Li, Y. Xia, H. Chen, N. Liu, A. Jackson, M. Wintermark, Y. Zhang, J. Hu, B. Wu, W. Zhang, J. Tu, Z. Su, G. Zhu

American Journal of Neuroradiology 2017 38 9
Quantification of blood-to-brain transfer rate in multiple sclerosis

Saeid Taheri, Gary A. Rosenberg, Corey Ford

Multiple Sclerosis and Related Disorders 2013 2 2
Determination of vascular permeability coefficients under slow luminal filling

James G. Truslow, Joe Tien

Microvascular Research 2013 90
Predicting Transformation to Type 2 Parenchymal Hematoma in Acute Ischemic Stroke by CT Permeability Imaging

K. Lin

American Journal of Neuroradiology 2011 32 6
Comparison of blind deconvolution- and Patlak analysis-based methods for determining vascular permeability

Joe Tien, Xuanyue Li, Raleigh M. Linville, Evan J. Feldman

Microvascular Research 2021 133

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