Blood flow imaging by optimal matching of computational fluid dynamics to 4D-flow data
Research output: Contribution to journal › Article
Purpose: Three-dimensional, time-resolved blood flow measurement (4D-flow) is a powerful research and clinical tool, but improved resolution and scan times are needed. Therefore, this study aims to (1) present a postprocessing framework for optimization-driven simulation-based flow imaging, called 4D-flow High-resolution Imaging with a priori Knowledge Incorporating the Navier-Stokes equations and the discontinuous Galerkin method (4D-flow HIKING), (2) investigate the framework in synthetic tests, (3) perform phantom validation using laser particle imaging velocimetry, and (4) demonstrate the use of the framework in vivo. Methods: An optimizing computational fluid dynamics solver including adjoint-based optimization was developed to fit computational fluid dynamics solutions to 4D-flow data. Synthetic tests were performed in 2D, and phantom validation was performed with pulsatile flow. Reference velocity data were acquired using particle imaging velocimetry, and 4D-flow data were acquired at 1.5 T. In vivo testing was performed on intracranial arteries in a healthy volunteer at 7 T, with 2D flow as the reference. Results: Synthetic tests showed low error (0.4%-0.7%). Phantom validation showed improved agreement with laser particle imaging velocimetry compared with input 4D-flow in the horizontal (mean −0.05 vs −1.11 cm/s, P <.001; SD 1.86 vs 4.26 cm/s, P <.001) and vertical directions (mean 0.05 vs −0.04 cm/s, P =.29; SD 1.36 vs 3.95 cm/s, P <.001). In vivo data show a reduction in flow rate error from 14% to 3.5%. Conclusions: Phantom and in vivo results from 4D-flow HIKING show promise for future applications with higher resolution, shorter scan times, and accurate quantification of physiological parameters.
|Research areas and keywords||
Subject classification (UKÄ) – MANDATORY
|Journal||Magnetic Resonance in Medicine|
|Publication status||E-pub ahead of print - 2020 Apr 8|