Julio Sotelo1,2, Lydia Dux-Santoy3, Andrea Guala3, José Rodríguez-Palomares3, Arturo Evangelista3, Carlos Sing-Long1,4,5, Jesús Urbina1,6, Joaquín Mura1, Daniel E Hurtado5,7, Sergio Uribe1,5,6. 1. Biomedical Imaging Center, Pontificia Universidad Católica de Chile, Santiago, Chile. 2. Department of Electrical Engineering, Schools of Engineering, Pontificia Universidad Católica de Chile, Santiago, Chile. 3. Department of Cardiology, Hospital Universitari Vall d'Hebron, Vall d'Hebron Institut de Recerca, Universitat Autònoma de Barcelona, Barcelona, Spain. 4. Mathematical and Computational Engineering, School of Engineering, Pontificia Universidad Católica de Chile, Santiago, Chile. 5. Institute for Biological and Medical Engineering, Schools of Engineering, Medicine, and Biological Sciences, Pontificia Universidad Católica de Chile, Santiago, Chile. 6. Department of Radiology, School of Medicine, Pontificia Universidad Catolica de Chile, Santiago, Chile. 7. Department of Structural and Geotechnical Engineering, Schools of Engineering, Pontificia Universidad Católica de Chile, Santiago, Chile.
Abstract
PURPOSE: To decompose the 3D wall shear stress (WSS) vector field into its axial (WSSA ) and circumferential (WSSC ) components using a Laplacian finite element approach. METHODS: We validated our method with in silico experiments involving different geometries and a modified Poiseuille flow. We computed 3D maps of the WSS, WSSA , and WSSC using 4D flow MRI data obtained from 10 volunteers and 10 patients with bicuspid aortic valve (BAV). We compared our method with the centerline method. The mean value, standard deviation, root mean-squared error, and Wilcoxon signed rank test are reported. RESULTS: We obtained an error <0.05% processing analytical geometries. We found good agreement between our method and the modified Poiseuille flow for the WSS, WSSA , and WSSC . We found statistically significance differences between our method and a 3D centerline method. In BAV patients, we found a 220% significant increase in the WSSC in the ascending aorta with respect to volunteers. CONCLUSION: We developed a novel methodology to decompose the WSS vector in WSSA and WSSC in 3D domains, using 4D flow MRI data. Our method provides a more robust quantification of WSSA and WSSC in comparison with other reported methods. Magn Reson Med 79:2816-2823, 2018.
PURPOSE: To decompose the 3D wall shear stress (WSS) vector field into its axial (WSSA ) and circumferential (WSSC ) components using a Laplacian finite element approach. METHODS: We validated our method with in silico experiments involving different geometries and a modified Poiseuille flow. We computed 3D maps of the WSS, WSSA , and WSSC using 4D flow MRI data obtained from 10 volunteers and 10 patients with bicuspid aortic valve (BAV). We compared our method with the centerline method. The mean value, standard deviation, root mean-squared error, and Wilcoxon signed rank test are reported. RESULTS: We obtained an error <0.05% processing analytical geometries. We found good agreement between our method and the modified Poiseuille flow for the WSS, WSSA , and WSSC . We found statistically significance differences between our method and a 3D centerline method. In BAV patients, we found a 220% significant increase in the WSSC in the ascending aorta with respect to volunteers. CONCLUSION: We developed a novel methodology to decompose the WSS vector in WSSA and WSSC in 3D domains, using 4D flow MRI data. Our method provides a more robust quantification of WSSA and WSSC in comparison with other reported methods. Magn Reson Med 79:2816-2823, 2018.
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