Abstract
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.
Highlights
It is well-established that hemodynamics is associated with the onset and evolution of several cardiovascular disorders such as aneurysms, stenoses, or blood clot formation [1,2,3]
The computed MR images were segmented with a binary mask obtained from the signal magnitude of the simulated MRI
A first visual comparison shows good agreement of the two fields regardless of the phase considered, the simulated MRI (SMRI) velocity seems blurred as compared to the Computational Fluid Dynamics (CFD) field
Summary
It is well-established that hemodynamics is associated with the onset and evolution of several cardiovascular disorders such as aneurysms, stenoses, or blood clot formation [1,2,3]. There has been increasing interest in using time-resolved 3D phase contrast Magnetic Resonance Imaging (or 4D Flow MRI) [4] for detection and follow-up of numerous vascular diseases as well as for research purposes. Several acquisition parameters (e.g.: spatio-temporal resolution, encoding velocity, imaging artifacts) may limit the expected accuracy of the measurements and potentially lead to erroneous diagnosis [8, 9]. The intrinsic complexities of the multi-modal MRI acquisition process make it delicate to localize the sources of the measurement errors. The signal processing steps required to reconstruct an MR image as well as the large variety of user-dependent acquisition parameters are as many potential sources of errors that could alter the measurements, and possibly lead to misdiagnosis.
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