R H M van Hoof1, E Hermeling1, M T B Truijman2, R J van Oostenbrugge3, J W H Daemen4, R J van der Geest5, N P van Orshoven6, A H Schreuder7, W H Backes8, M J A P Daemen9, J E Wildberger1, M E Kooi1. 1. Department of Radiology, Maastricht University Medical Center, Maastricht 6202 AZ, The Netherlands and CARIM School for Cardiovascular Diseases, Maastricht University, Maastricht 6200 MD, The Netherlands. 2. Department of Radiology, Maastricht University Medical Center, Maastricht 6202 AZ, The Netherlands; CARIM School for Cardiovascular Diseases, Maastricht University, Maastricht 6200 MD, The Netherlands; and Department of Clinical Neurophysiology, Maastricht University Medical Center, Maastricht 6202 AZ, The Netherlands. 3. Department of Neurology, Maastricht University Medical Center, Maastricht 6202 AZ, The Netherlands and CARIM School for Cardiovascular Diseases, Maastricht University, Maastricht 6200 MD, The Netherlands. 4. Department of Surgery, Maastricht University Medical Center, Maastricht 6202 AZ, The Netherlands. 5. Department of Radiology, Leiden University Medical Center, Leiden 2333 ZA, The Netherlands. 6. Department of Neurology, Orbis Medical Center, Sittard 6130 MB, The Netherlands. 7. Department of Neurology, Atrium Medical Center, Heerlen 6401 CX, The Netherlands. 8. Department of Radiology, Maastricht University Medical Center, Maastricht 6202 AZ, The Netherlands. 9. Department of Pathology, Academic Medical Center, Amsterdam 1100 DD, The Netherlands.
Abstract
PURPOSE: Quantitative pharmacokinetic modeling of dynamic contrast-enhanced (DCE)-MRI can be used to assess atherosclerotic plaque microvasculature, which is an important marker of plaque vulnerability. Purpose of the present study was (1) to compare magnitude- versus phase-based vascular input functions (m-VIF vs ph-VIF) used in pharmacokinetic modeling and (2) to perform model calculations and flow phantom experiments to gain more insight into the differences between m-VIF and ph-VIF. METHODS: Population averaged m-VIF and ph-VIFs were acquired from 11 patients with carotid plaques and used for pharmacokinetic analysis in another 17 patients. Simulations, using the Bloch equations and the MRI scan geometry, and flow phantom experiments were performed to determine the effect of local blood velocity on the magnitude and phase signal enhancement. RESULTS: Simulations and flow phantom experiments revealed that flow within the lumen can lead to severe underestimation of m-VIF, while this is not the case for the ph-VIF. In line, the peak concentration of the m-VIF is significantly lower than ph-VIF (p < 0.001), in vivo. Quantitative model parameters for m- and ph-VIF differed in absolute values but were moderate to strongly correlated with each other [K(trans) Spearman's ρ > 0.93 (p < 0.001) and vp Spearman's ρ > 0.58 (p < 0.05)]. CONCLUSIONS: m-VIF is strongly influenced by local blood velocity, which leads to underestimation of the contrast medium concentration. Therefore, it is advised to use ph-VIF for DCE-MRI analysis of carotid plaques for accurate quantification.
PURPOSE: Quantitative pharmacokinetic modeling of dynamic contrast-enhanced (DCE)-MRI can be used to assess atherosclerotic plaque microvasculature, which is an important marker of plaque vulnerability. Purpose of the present study was (1) to compare magnitude- versus phase-based vascular input functions (m-VIF vs ph-VIF) used in pharmacokinetic modeling and (2) to perform model calculations and flow phantom experiments to gain more insight into the differences between m-VIF and ph-VIF. METHODS: Population averaged m-VIF and ph-VIFs were acquired from 11 patients with carotid plaques and used for pharmacokinetic analysis in another 17 patients. Simulations, using the Bloch equations and the MRI scan geometry, and flow phantom experiments were performed to determine the effect of local blood velocity on the magnitude and phase signal enhancement. RESULTS: Simulations and flow phantom experiments revealed that flow within the lumen can lead to severe underestimation of m-VIF, while this is not the case for the ph-VIF. In line, the peak concentration of the m-VIF is significantly lower than ph-VIF (p < 0.001), in vivo. Quantitative model parameters for m- and ph-VIF differed in absolute values but were moderate to strongly correlated with each other [K(trans) Spearman's ρ > 0.93 (p < 0.001) and vp Spearman's ρ > 0.58 (p < 0.05)]. CONCLUSIONS: m-VIF is strongly influenced by local blood velocity, which leads to underestimation of the contrast medium concentration. Therefore, it is advised to use ph-VIF for DCE-MRI analysis of carotid plaques for accurate quantification.
Authors: Raf H M van Hoof; Floris H B M Schreuder; Patty Nelemans; Martine T B Truijman; Narender P van Orshoven; Tobien H Schreuder; Werner H Mess; Sylvia Heeneman; Robert J van Oostenbrugge; Joachim E Wildberger; M Eline Kooi Journal: Cerebrovasc Dis Date: 2017-09-26 Impact factor: 2.762
Authors: Bram F Coolen; Claudia Calcagno; Pim van Ooij; Zahi A Fayad; Gustav J Strijkers; Aart J Nederveen Journal: MAGMA Date: 2017-08-14 Impact factor: 2.310
Authors: Geneviève A J C Crombag; Raf H M van Hoof; Robert J Holtackers; Floris H B M Schreuder; Martine T B Truijman; Tobien A H C M L Schreuder; Narender P van Orshoven; Werner H Mess; Paul A M Hofman; Robert J van Oostenbrugge; Joachim E Wildberger; M Eline Kooi Journal: J Am Heart Assoc Date: 2019-04-16 Impact factor: 5.501
Authors: Geneviève A J C Crombag; Floris H B M Schreuder; Raf H M van Hoof; Martine T B Truijman; Nicky J A Wijnen; Stefan A Vöö; Patty J Nelemans; Sylvia Heeneman; Paul J Nederkoorn; Jan-Willem H Daemen; Mat J A P Daemen; Werner H Mess; J E Wildberger; Robert J van Oostenbrugge; M Eline Kooi Journal: J Cardiovasc Magn Reson Date: 2019-03-04 Impact factor: 5.364