Xingfeng Shao1, Ziwei Zhao1, Jonathan Russin2, Arun Amar2, Nerses Sanossian3, Danny Jj Wang1,3, Lirong Yan1,3. 1. Laboratory of FMRI Technology (LOFT), Mark & Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Los Angeles, California. 2. Center for Neurorestoration, Department of Neurological Surgery, Keck School of Medicine, University of Southern California, Los Angeles, California. 3. Department of Neurology, Keck School of Medicine, University of Southern California, Los Angeles, California.
Abstract
PURPOSE: Noncontrast enhanced dynamic magnetic resonance angiography delineates the pattern of dynamic blood flow of the cerebral vasculature. A model-free solution was proposed to quantify arterial blood flow (aBF) by using the monotonic property of the residual function. THEORY AND METHODS: Analytical simulations and in-vivo studies were performed to evaluate the performance of the proposed method by comparing the aBF values generated from the proposed and conventional singular value decomposition methods. The aBF values were compared with blood flow velocity measured by 2D phase contrast MRI, and compared between balanced steady-state free precession-based radial and spoiled GRE-based Cartesian acquisitions. Hemodynamic parametric maps were generated in 1 patient with arteriovenous malformation. RESULTS: The proposed method generates reliable aBF measurement at different signal-to-noise ratio levels, whereas overestimation/underestimation of aBF was observed when a high/low threshold was applied in the singular value decomposition method. Average aBF in large vascular branches was 214.4 and 214.5 mL/mL/min with radial and Cartesian acquisitions, respectively. Significant correlations were found between aBF and blood flow velocity measured by phase contrast MRI (P = 0.0008), and between Cartesian and radial acquisitions (P < 0.0001). Altered hemodynamics were observed at the lesion site of the arteriovenous malformation patient. CONCLUSION: A robust analytical solution was proposed for quantifying aBF. This model-free method is robust to noise, and its clinical value in the diagnosis of cerebrovascular disorders awaits further evaluation.
PURPOSE: Noncontrast enhanced dynamic magnetic resonance angiography delineates the pattern of dynamic blood flow of the cerebral vasculature. A model-free solution was proposed to quantify arterial blood flow (aBF) by using the monotonic property of the residual function. THEORY AND METHODS: Analytical simulations and in-vivo studies were performed to evaluate the performance of the proposed method by comparing the aBF values generated from the proposed and conventional singular value decomposition methods. The aBF values were compared with blood flow velocity measured by 2D phase contrast MRI, and compared between balanced steady-state free precession-based radial and spoiled GRE-based Cartesian acquisitions. Hemodynamic parametric maps were generated in 1 patient with arteriovenous malformation. RESULTS: The proposed method generates reliable aBF measurement at different signal-to-noise ratio levels, whereas overestimation/underestimation of aBF was observed when a high/low threshold was applied in the singular value decomposition method. Average aBF in large vascular branches was 214.4 and 214.5 mL/mL/min with radial and Cartesian acquisitions, respectively. Significant correlations were found between aBF and blood flow velocity measured by phase contrast MRI (P = 0.0008), and between Cartesian and radial acquisitions (P < 0.0001). Altered hemodynamics were observed at the lesion site of the arteriovenous malformationpatient. CONCLUSION: A robust analytical solution was proposed for quantifying aBF. This model-free method is robust to noise, and its clinical value in the diagnosis of cerebrovascular disorders awaits further evaluation.
Authors: S Wetzel; S Meckel; A Frydrychowicz; L Bonati; E-W Radue; K Scheffler; J Hennig; M Markl Journal: AJNR Am J Neuroradiol Date: 2007-03 Impact factor: 3.825
Authors: Jan-Karl Burkhardt; Xiaolin Chen; Ethan A Winkler; Daniel L Cooke; Helen Kim; Michael T Lawton Journal: World Neurosurg Date: 2017-04-27 Impact factor: 2.104