Navier-Stokes-Based Regularization for 4d Flow MRI Super-Resolution

Abstract

4D flow MRI is a promising tool in cardiovascular imaging. However, its lack of resolution can degrade some biomarkers' evaluation accuracy. The computational fluid dynamics (CFD) simulation is considered as the reference method to improve numerically the image resolution. However, CFD simulations are complex and time consuming, and matching their results with 4D Flow MRI data is very challenging. This paper aims to introduce a fast and efficient super-resolution (SR) approach thanks to the minimization of a L 2 -penalized criterion, which combines a weighted least-squares data fidelity term and Navier-Stokes equations. The algorithm has been validated on synthetic and phantom datasets and compared to state-of-the-art solutions. Moreover, a prospective study is conducted on the segmentationfree application of the proposed algorithm.

Segmentation-Free Super-Resolved 4D flow MRI Reconstruction Exploiting Navier-Stokes Equations and Spatial Regularization

“…The proposed approach SFSR, Rispoli et al [11] named SbSR for SIMPLER based-SR, and the previous solution [15] called Penalized-SR (PSR) are evaluated in terms of Root- Mean-Square Error (RMSE) and computation time (CT). The SR estimation is compared to the corresponding theoretical or numerical reference velocity field.…”

“…We proposed in [15], an efficient SR algorithm relying on the inverse problem resolution methodology [16]. A L 2penalized formulation is used for convection-diffusion and mass conservation instead of applying hard constraints.…”

“…Section 3 presents 2D sections of synthetic data and experimental 4D flow MRI measurements on a phantom [17]. In section 4, SR reconstructions are evaluated in terms of root-mean-square error of the velocity vector in the fluid domain and the computation time, especially in comparison to Rispoli et al [11] solution and to our previous contribution [15].…”