RESUMEN
PURPOSE: To evaluate hemodynamic markers obtained by accelerated GRAPPA (R = 2, 3, 4) and compressed sensing (R = 7.6) 4D flow MRI sequences under complex flow conditions. METHODS: The accelerated 4D flow MRI scans were performed on a pulsatile flow phantom, along with a nonaccelerated fully sampled k-space acquisition. Computational fluid dynamics simulations based on the experimentally measured flow fields were conducted for additional comparison. Voxel-wise comparisons (Bland-Altman analysis, L 2 $$ {L}_2 $$ -norm metric), as well as nonderived quantities (velocity profiles, flow rates, and peak velocities), were used to compare the velocity fields obtained from the different modalities. RESULTS: 4D flow acquisitions and computational fluid dynamics depicted similar hemodynamic patterns. Voxel-wise comparisons between the MRI scans highlighted larger discrepancies at the voxels located near the phantom's boundary walls. A trend for all MR scans to overestimate velocity profiles and peak velocities as compared to computational fluid dynamics was noticed in regions associated with high velocity or acceleration. However, good agreement for the flow rates was observed, and eddy-current correction appeared essential for consistency of the flow rates measurements with respect to the principle of mass conservation. CONCLUSION: GRAPPA (R = 2, 3) and highly accelerated compressed sensing showed good agreement with the fully sampled acquisition. Yet, all 4D flow MRI scans were hampered by artifacts inherent to the phase-contrast acquisition procedure. Computational fluid dynamics simulations are an interesting tool to assess these differences but are sensitive to modeling parameters.
Asunto(s)
Hidrodinámica , Imagenología Tridimensional , Artefactos , Velocidad del Flujo Sanguíneo , Imagenología Tridimensional/métodos , Imagen por Resonancia Magnética/métodos , Fantasmas de ImagenRESUMEN
Several well-resolved 4D Flow MRI acquisitions of an idealized rigid flow phantom featuring an aneurysm, a curved channel as well as a bifurcation were performed under pulsatile regime. The resulting hemodynamics were processed to remove MRI artifacts. Subsequently, they were compared with CFD predictions computed on the same flow domain, using an in-house high-order low dissipative flow solver. Results show that reaching a good agreement is not straightforward but requires proper treatments of both techniques. Several sources of discrepancies are highlighted and their impact on the final correlation evaluated. While a very poor correlation (r2 = 0.63) is found in the entire domain between raw MRI and CFD data, correlation as high as r2 = 0.97 is found when artifacts are removed by post-processing the MR data and down sampling the CFD results to match the MRI spatial and temporal resolutions. This work demonstrates that, in a well-controlled environment, both PC-MRI and CFD might bring reliable and correlated flow quantities when a proper methodology to reduce the errors is followed.
Asunto(s)
Hidrodinámica , Imagen por Resonancia Magnética/métodos , Fantasmas de Imagen , Flujo Sanguíneo Regional , Estrés Mecánico , Sístole/fisiología , Factores de TiempoRESUMEN
A numerical approach is presented to efficiently simulate time-resolved 3D phase-contrast Magnetic resonance Imaging (or 4D Flow MRI) acquisitions under realistic flow conditions. The Navier-Stokes and Bloch equations are simultaneously solved with an Eulerian-Lagrangian formalism. A semi-analytic solution for the Bloch equations as well as a periodic particle seeding strategy are developed to reduce the computational cost. The velocity reconstruction pipeline is first validated by considering a Poiseuille flow configuration. The 4D Flow MRI simulation procedure is then applied to the flow within an in vitro flow phantom typical of the cardiovascular system. The simulated MR velocity images compare favorably to both the flow computed by solving the Navier-Stokes equations and experimental 4D Flow MRI measurements. A practical application is finally presented in which the MRI simulation framework is used to identify the origins of the MRI measurement errors.