Iman Khodarahmi1. 1. Department of Electrical and Computer Engineering, University of Louisville, 2210 S Brook St, Shumaker Research Building, Room 216, Louisville, KY, 40292, USA, imankhodarahmi@yahoo.com.
Abstract
OBJECT: This study aims to validate phase-contrast magnetic resonance imaging (PC-MRI) measurements of a steady flow through a severe stenotic phantom using particle image velocimetry (PIV) and computational fluid dynamics (CFD). MATERIALS AND METHODS: The study was performed in an axisymmetric 87 % area stenosis model using an inlet Reynolds number (Re) of 160, corresponding to a jet Re of 444. Velocity patterns and estimated fluid shear stresses from three modalities were analyzed and compared qualitatively and quantitatively. RESULTS: Visual analysis via contour subtraction and Bland-Altman plots showed good agreement for flow velocities and less agreement for maximum shear stress (MSS). The Pearson's coefficients of correlation between PC-MRI and PIV were 0.97 for the velocity field and 0.82 for the MSS. The corresponding parameters between PC-MRI and CFD were 0.96 and 0.84, respectively. CONCLUSION: Findings indicate that PC-MRI can be implemented to estimate velocity flow fields and MSS; however, this method is not sufficiently accurate to quantify the MSS at regions of high shear rate.
OBJECT: This study aims to validate phase-contrast magnetic resonance imaging (PC-MRI) measurements of a steady flow through a severe stenotic phantom using particle image velocimetry (PIV) and computational fluid dynamics (CFD). MATERIALS AND METHODS: The study was performed in an axisymmetric 87 % area stenosis model using an inlet Reynolds number (Re) of 160, corresponding to a jet Re of 444. Velocity patterns and estimated fluid shear stresses from three modalities were analyzed and compared qualitatively and quantitatively. RESULTS: Visual analysis via contour subtraction and Bland-Altman plots showed good agreement for flow velocities and less agreement for maximum shear stress (MSS). The Pearson's coefficients of correlation between PC-MRI and PIV were 0.97 for the velocity field and 0.82 for the MSS. The corresponding parameters between PC-MRI and CFD were 0.96 and 0.84, respectively. CONCLUSION: Findings indicate that PC-MRI can be implemented to estimate velocity flow fields and MSS; however, this method is not sufficiently accurate to quantify the MSS at regions of high shear rate.
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