The analysis of the blood flow in the great thoracic arteries does provide valuable information about the cardiac function and can diagnose the potential development of vascular diseases. Flow-sensitive four-dimensional flow cardiovascular magnetic resonance imaging (4D flow CMR) is often used to characterize patients' blood flow in the clinical environment. Nevertheless, limited spatial and temporal resolution hinders a detailed assessment of the hemodynamics. Computational fluid dynamics (CFD) could expand this information and, integrated with experimental velocity field, enable to derive the pressure maps. However, the limited resolution of the 4D flow CMR and the simplifications of CFD modeling compromise the accuracy of the computed flow parameters. In this article, a novel approach is proposed, where 4D flow CMR and CFD velocity fields are integrated synergistically to obtain an enhanced MR imaging (EMRI). The approach was first tested on a two-dimensional (2D) portion of a pipe, to understand the behavior of the parameters of the model in this novel framework, and afterwards in vivo, to apply it to the analysis of blood flow in a patient-specific human aorta. The outcomes of EMRI are assessed by comparing the computed velocities with the experimental one. The results demonstrate that EMRI preserves flow structures while correcting for experimental noise. Therefore, it can provide better insights into the hemodynamics of cardiovascular problems, overcoming the limitations of MRI and CFD, even when considering a small region of interest. EMRI confirmed its potential to provide more accurate noninvasive estimation of major cardiovascular risk predictors (e.g., flow patterns, endothelial shear stress) and become a novel diagnostic tool.

Giacomo Annio, Ryo Torii, Ben Ariff, Declan P. O{\textquotesingle}Regan, Vivek Muthurangu, Andrea Ducci, et al. (2019). Enhancing Magnetic Resonance Imaging With Computational Fluid Dynamics. JOURNAL OF ENGINEERING AND SCIENCE IN MEDICAL DIAGNOSTICS AND THERAPY, 2(4) [10.1115/1.4045493].

Enhancing Magnetic Resonance Imaging With Computational Fluid Dynamics

Gaetano Burriesci
Ultimo
2019-01-01

Abstract

The analysis of the blood flow in the great thoracic arteries does provide valuable information about the cardiac function and can diagnose the potential development of vascular diseases. Flow-sensitive four-dimensional flow cardiovascular magnetic resonance imaging (4D flow CMR) is often used to characterize patients' blood flow in the clinical environment. Nevertheless, limited spatial and temporal resolution hinders a detailed assessment of the hemodynamics. Computational fluid dynamics (CFD) could expand this information and, integrated with experimental velocity field, enable to derive the pressure maps. However, the limited resolution of the 4D flow CMR and the simplifications of CFD modeling compromise the accuracy of the computed flow parameters. In this article, a novel approach is proposed, where 4D flow CMR and CFD velocity fields are integrated synergistically to obtain an enhanced MR imaging (EMRI). The approach was first tested on a two-dimensional (2D) portion of a pipe, to understand the behavior of the parameters of the model in this novel framework, and afterwards in vivo, to apply it to the analysis of blood flow in a patient-specific human aorta. The outcomes of EMRI are assessed by comparing the computed velocities with the experimental one. The results demonstrate that EMRI preserves flow structures while correcting for experimental noise. Therefore, it can provide better insights into the hemodynamics of cardiovascular problems, overcoming the limitations of MRI and CFD, even when considering a small region of interest. EMRI confirmed its potential to provide more accurate noninvasive estimation of major cardiovascular risk predictors (e.g., flow patterns, endothelial shear stress) and become a novel diagnostic tool.
2019
Giacomo Annio, Ryo Torii, Ben Ariff, Declan P. O{\textquotesingle}Regan, Vivek Muthurangu, Andrea Ducci, et al. (2019). Enhancing Magnetic Resonance Imaging With Computational Fluid Dynamics. JOURNAL OF ENGINEERING AND SCIENCE IN MEDICAL DIAGNOSTICS AND THERAPY, 2(4) [10.1115/1.4045493].
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10447/667452
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