Vera C Keil1, Burkhard Mädler2, Jürgen Gieseke3, Rolf Fimmers4, Elke Hattingen5, Hans H Schild6, Dariusch R Hadizadeh7. 1. Department of Radiology, University Hospital Bonn, Sigmund-Freud-Strasse 25, 53127 Bonn, Germany. Electronic address: vera.keil@ukbonn.de. 2. Philips Healthcare, Röntgenstrasse 22, 22335 Hamburg, Germany. Electronic address: burkhard.maedler@philips.com. 3. Department of Radiology, University Hospital Bonn, Sigmund-Freud-Strasse 25, 53127 Bonn, Germany; Philips Healthcare, Röntgenstrasse 22, 22335 Hamburg, Germany. Electronic address: juergen.gieseke@philips.com. 4. IMBIE (Statistics Department), University Hospital Bonn, Sigmund-Freud-Strasse 25, 53127 Bonn, Germany. Electronic address: rolf.fimmers@imbie.uni-bonn.de. 5. Department of Radiology, University Hospital Bonn, Sigmund-Freud-Strasse 25, 53127 Bonn, Germany. Electronic address: elke.hattingen@ukbonn.de. 6. Department of Radiology, University Hospital Bonn, Sigmund-Freud-Strasse 25, 53127 Bonn, Germany. Electronic address: hans.schild@ukbonn.de. 7. Department of Radiology, University Hospital Bonn, Sigmund-Freud-Strasse 25, 53127 Bonn, Germany. Electronic address: dariusch.hadizadeh@ukbonn.de.
Abstract
PURPOSE: Kinetic parameters derived from dynamic contrast-enhanced MRI (DCE-MRI) were suggested as a possible instrument for multi-parametric lesion characterization, but have not found their way into clinical practice yet due to inconsistent results. The quantification is heavily influenced by the definition of an appropriate arterial input functions (AIF). Regarding brain tumor DCE-MRI, there are currently several co-existing methods to determine the AIF frequently including different brain vessels as sources. This study quantitatively and qualitatively analyzes the impact of AIF source selection on kinetic parameters derived from commonly selected AIF source vessels compared to a population-based AIF model. MATERIAL AND METHODS: 74 patients with brain lesions underwent 3D DCE-MRI. Kinetic parameters [transfer constants of contrast agent efflux and reflux Ktrans and kep and, their ratio, ve, that is used to measure extravascular-extracellular volume fraction and plasma volume fraction vp] were determined using extended Tofts model in 821 ROI from 4 AIF sources [the internal carotid artery (ICA), the closest artery to the lesion, the superior sagittal sinus (SSS), the population-based Parker model]. The effect of AIF source alteration on kinetic parameters was evaluated by tissue type selective intra-class correlation (ICC) and capacity to differentiate gliomas by WHO grade [area under the curve analysis (AUC)]. RESULTS: Arterial AIF more often led to implausible ve >100% values (p<0.0001). AIF source alteration rendered different absolute kinetic parameters (p<0.0001), except for kep. ICC between kinetic parameters of different AIF sources and tissues were variable (0.08-0.87) and only consistent >0.5 between arterial AIF derived kinetic parameters. Differentiation between WHO III and II glioma was exclusively possible with vp derived from an AIF in the SSS (p=0.03; AUC 0.74). CONCLUSION: The AIF source has a significant impact on absolute kinetic parameters in DCE-MRI, which limits the comparability of kinetic parameters derived from different AIF sources. The effect is also tissue-dependent. The SSS appears to be the best choice for AIF source vessel selection in brain tumor DCE-MRI as it exclusively allowed for WHO grades II/III and III/IV glioma distinction (by vp) and showed the least number of implausible ve values.
PURPOSE: Kinetic parameters derived from dynamic contrast-enhanced MRI (DCE-MRI) were suggested as a possible instrument for multi-parametric lesion characterization, but have not found their way into clinical practice yet due to inconsistent results. The quantification is heavily influenced by the definition of an appropriate arterial input functions (AIF). Regarding brain tumorDCE-MRI, there are currently several co-existing methods to determine the AIF frequently including different brain vessels as sources. This study quantitatively and qualitatively analyzes the impact of AIF source selection on kinetic parameters derived from commonly selected AIF source vessels compared to a population-based AIF model. MATERIAL AND METHODS: 74 patients with brain lesions underwent 3D DCE-MRI. Kinetic parameters [transfer constants of contrast agent efflux and reflux Ktrans and kep and, their ratio, ve, that is used to measure extravascular-extracellular volume fraction and plasma volume fraction vp] were determined using extended Tofts model in 821 ROI from 4 AIF sources [the internal carotid artery (ICA), the closest artery to the lesion, the superior sagittal sinus (SSS), the population-based Parker model]. The effect of AIF source alteration on kinetic parameters was evaluated by tissue type selective intra-class correlation (ICC) and capacity to differentiate gliomas by WHO grade [area under the curve analysis (AUC)]. RESULTS: Arterial AIF more often led to implausible ve >100% values (p<0.0001). AIF source alteration rendered different absolute kinetic parameters (p<0.0001), except for kep. ICC between kinetic parameters of different AIF sources and tissues were variable (0.08-0.87) and only consistent >0.5 between arterial AIF derived kinetic parameters. Differentiation between WHO III and II glioma was exclusively possible with vp derived from an AIF in the SSS (p=0.03; AUC 0.74). CONCLUSION: The AIF source has a significant impact on absolute kinetic parameters in DCE-MRI, which limits the comparability of kinetic parameters derived from different AIF sources. The effect is also tissue-dependent. The SSS appears to be the best choice for AIF source vessel selection in brain tumorDCE-MRI as it exclusively allowed for WHO grades II/III and III/IV glioma distinction (by vp) and showed the least number of implausible ve values.
Authors: Soudabeh Kargar; Eric A Borisch; Adam T Froemming; Akira Kawashima; Lance A Mynderse; Eric G Stinson; Joshua D Trzasko; Stephen J Riederer Journal: Magn Reson Imaging Date: 2017-12-24 Impact factor: 2.546
Authors: Bruna V Jardim-Perassi; Suning Huang; William Dominguez-Viqueira; Jan Poleszczuk; Mikalai M Budzevich; Mahmoud A Abdalah; Smitha R Pillai; Epifanio Ruiz; Marilyn M Bui; Debora A P C Zuccari; Robert J Gillies; Gary V Martinez Journal: Cancer Res Date: 2019-06-11 Impact factor: 12.701
Authors: Qihao Zhang; Pascal Spincemaille; Michele Drotman; Christine Chen; Sarah Eskreis-Winkler; Weiyuan Huang; Liangdong Zhou; John Morgan; Thanh D Nguyen; Martin R Prince; Yi Wang Journal: Magn Reson Imaging Date: 2021-11-06 Impact factor: 2.546
Authors: Wei Huang; Yiyi Chen; Andriy Fedorov; Xia Li; Guido H Jajamovich; Dariya I Malyarenko; Madhava P Aryal; Peter S LaViolette; Matthew J Oborski; Finbarr O'Sullivan; Richard G Abramson; Kourosh Jafari-Khouzani; Aneela Afzal; Alina Tudorica; Brendan Moloney; Sandeep N Gupta; Cecilia Besa; Jayashree Kalpathy-Cramer; James M Mountz; Charles M Laymon; Mark Muzi; Paul E Kinahan; Kathleen Schmainda; Yue Cao; Thomas L Chenevert; Bachir Taouli; Thomas E Yankeelov; Fiona Fennessy; Xin Li Journal: Tomography Date: 2019-03
Authors: Bart R J van Dijken; Peter Jan van Laar; Marion Smits; Jan Willem Dankbaar; Roelien H Enting; Anouk van der Hoorn Journal: J Magn Reson Imaging Date: 2019-01 Impact factor: 4.813
Authors: Liangdong Zhou; Qihao Zhang; Pascal Spincemaille; Thanh D Nguyen; John Morgan; Weiying Dai; Yi Li; Ajay Gupta; Martin R Prince; Yi Wang Journal: Magn Reson Med Date: 2020-11-18 Impact factor: 4.668