Literature DB >> 19449435

Quantification of cerebral blood flow, cerebral blood volume, and blood-brain-barrier leakage with DCE-MRI.

Steven Sourbron1, Michael Ingrisch, Axel Siefert, Maximilian Reiser, Karin Herrmann.   

Abstract

Dynamic susceptibility contrast MRI (DSC-MRI) is the current standard for the measurement of Cerebral Blood Flow (CBF) and Cerebral Blood Volume (CBV), but it is not suitable for the measurement of Extraction Flow (EF) and may not allow for absolute quantification. The objective of this study was to develop and evaluate a methodology to measure CBF, CBV, and EF from T1-weighted dynamic contrast-enhanced MRI (DCE-MRI). A two-compartment modeling approach was developed, which applies both to tissues with an intact and with a broken Blood-Brain-Barrier (BBB). The approach was evaluated using measurements in normal grey matter (GM) and white matter (WM) and in tumors of 15 patients. Accuracy and precision were estimated with simulations of normal brain tissue. All tumor and normal tissue curves were accurately fitted by the model. CBF (mL/100 mL/min) was 82 +/- 21 in GM and 23 +/- 14 in WM, CBV (mL/100 mL) was 2.6 +/- 0.8 in GM and 1.3 +/- 0.4 in WM. EF (mL/100 mL/min) was close to zero in GM (-0.009 +/- 0.05) and WM (-0.03 +/- 0.08). Simulations show an overlap between CBF values of WM and GM, which is eliminated when Contrast-to-Noise (CNR) is improved. The model provides a consistent description of tracer kinetics in all brain tissues, and an accurate assessment of perfusion and permeability in reference tissues. The measurement sequence requires optimization to improve CNR and the precision in the perfusion parameters. With this approach, DCE-MRI presents a promising alternative to DSC-MRI for quantitative bolus-tracking in the brain. (c) 2009 Wiley-Liss, Inc.

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Year:  2009        PMID: 19449435     DOI: 10.1002/mrm.22005

Source DB:  PubMed          Journal:  Magn Reson Med        ISSN: 0740-3194            Impact factor:   4.668


  84 in total

1.  Temporal dynamics and spatial specificity of arterial and venous blood volume changes during visual stimulation: implication for BOLD quantification.

Authors:  Tae Kim; Seong-Gi Kim
Journal:  J Cereb Blood Flow Metab       Date:  2010-12-22       Impact factor: 6.200

2.  Dynamic contrast enhanced T1 MRI perfusion differentiates pseudoprogression from recurrent glioblastoma.

Authors:  Alissa A Thomas; Julio Arevalo-Perez; Thomas Kaley; John Lyo; Kyung K Peck; Weiji Shi; Zhigang Zhang; Robert J Young
Journal:  J Neurooncol       Date:  2015-08-15       Impact factor: 4.130

3.  [Contrast-enhanced ultrasound in animal models].

Authors:  P M Paprottka; P Zengel; M Ingrisch; C C Cyran; M Eichhorn; M F Reiser; K Nikolaou; D-A Clevert
Journal:  Radiologe       Date:  2011-06       Impact factor: 0.635

4.  Human cerebral blood volume measurements using dynamic contrast enhancement in comparison to dynamic susceptibility contrast MRI.

Authors:  Moran Artzi; Gilad Liberman; Guy Nadav; Faina Vitinshtein; Deborah T Blumenthal; Felix Bokstein; Orna Aizenstein; Dafna Ben Bashat
Journal:  Neuroradiology       Date:  2015-04-07       Impact factor: 2.804

Review 5.  Blood-brain barrier imaging in human neuropathologies.

Authors:  Ronel Veksler; Ilan Shelef; Alon Friedman
Journal:  Arch Med Res       Date:  2014-11-29       Impact factor: 2.235

Review 6.  Review of treatment assessment using DCE-MRI in breast cancer radiation therapy.

Authors:  Chun-Hao Wang; Fang-Fang Yin; Janet Horton; Zheng Chang
Journal:  World J Methodol       Date:  2014-06-26

7.  Permeability measurement using dynamic susceptibility contrast magnetic resonance imaging enhances differential diagnosis of primary central nervous system lymphoma from glioblastoma.

Authors:  Ji Ye Lee; Atle Bjørnerud; Ji Eun Park; Bo Eun Lee; Joo Hyun Kim; Ho Sung Kim
Journal:  Eur Radiol       Date:  2019-03-15       Impact factor: 5.315

8.  A model-constrained Monte Carlo method for blind arterial input function estimation in dynamic contrast-enhanced MRI: I. Simulations.

Authors:  Matthias C Schabel; Jacob U Fluckiger; Edward V R DiBella
Journal:  Phys Med Biol       Date:  2010-08-03       Impact factor: 3.609

9.  Diagnostic accuracy of dynamic contrast-enhanced MR imaging using a phase-derived vascular input function in the preoperative grading of gliomas.

Authors:  T B Nguyen; G O Cron; J F Mercier; C Foottit; C H Torres; S Chakraborty; J Woulfe; G H Jansen; J M Caudrelier; J Sinclair; M J Hogan; R E Thornhill; I G Cameron
Journal:  AJNR Am J Neuroradiol       Date:  2012-03-22       Impact factor: 3.825

10.  UMMPerfusion: an open source software tool towards quantitative MRI perfusion analysis in clinical routine.

Authors:  Frank G Zöllner; Gerald Weisser; Marcel Reich; Sven Kaiser; Stefan O Schoenberg; Steven P Sourbron; Lothar R Schad
Journal:  J Digit Imaging       Date:  2013-04       Impact factor: 4.056

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