Abstract
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. 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. 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%. 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.
Highlights
Recent advances in compressed sensing[9] has enabled 4D-flow with reduced scan times and improved image quality[10,11,12,13] by introducing a priori information
Current methods for matching computational fluid dynamics (CFD) to 4D-flow typically consist of three parts: an accurate CFD simulation with free parameters to optimize, a metric to measure the difference between CFD and 4D-flow, and an efficient strategy to update the parameters to minimize the difference metric
We have developed a new optimization-driven simulationbased framework for efficient matching of high-order accurate CFD to 4D-flow data, called the 4D-flow High-resolution Imaging with a priori Knowledge Incorporating the Navier-Stokes equations and the discontinuous Galerkin method (4D-flow HIKING)
Summary
Three-dimensional, time-resolved, three-directional MR flow imaging (4D-flow) is a powerful method for investigation of cardiovascular physiology[1,2] and pathophysiology.[3,4,5,6,7,8] Current 4D-flow acquisition schemes provide comprehensive information on hemodynamics, but the tradeoff between image quality and scan time still limits widespread use in patient studies and the clinic.Recent advances in compressed sensing[9] has enabled 4D-flow with reduced scan times and improved image quality[10,11,12,13] by introducing a priori information (ie, knowledge that the image is sparse in some transformed domain). Most studies to date use CFD simulations with low order of accuracy, such as particle methods, finite differences, or low-order finiteelement methods.[14,15,21,22,23,24] the difference metric is usually not designed to model the 4D-flow measurement process.[14,15,16,20,21,22,23] efficient optimization can be limited by a lack of efficient computation of the gradient of the difference metric.[20,22] Funke et al[16] used an adjoint CFD solver to compute gradients for efficient optimization. They used a low-order CFD method and a difference metric that does not take the 4D-flow measurement process into account
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