Synthetic dataset generation for the analysis and the evaluation of image-based hemodynamics of the human aorta

Abstract

Here, we consider the issue of generating a suitable controlled environment for the evaluation of phase contrast (PC) MRI measurements. The computational framework, tailored to build synthetic datasets, is based on a two-step approach, i.e., define and implement (1) an accurate CFD model and (2) an image generator able to mime the overall outcomes of a PC MRI acquisition starting from datasets retrieved by the computational model. About 20 different datasets were built by changing relevant image parameters (pixel size, slice thickness, time frames per cardiac cycle). Focusing our attention on the thoracic aorta, synthetic images were processed in order to: (1) verify to which extent the fluid dynamics into the aortic arch is influenced by the image parameters; (2) establish the effect of spatial and temporal interpolation. Our study demonstrates that the integral scale of the aortic bulk flow could be described satisfactorily even when using images which are nowadays acquirable with MRI scanners. However, attention must be paid to near-wall velocities that can be affected by large inaccuracy. In detail, in bulk flow regions error values are well bounded (below 5% for most of the analyzed resolutions), while errors greater than 100% are systematically present at the vessel's wall. Moreover, also the data interpolation process can be responsible for large inaccuracies in new data generation, due to the inherent complexity of the flow field in some connected regions.

Helical flow in carotid bifurcation as surrogate marker of exposure to disturbed shear

“…Even if the accuracy of in vivo measured helicity-based bulk flow descriptors was not analyzed in this study, our conviction is enforced by recent proofs of evidence that in vivo quantitative helical blood flow analysis is feasible using PC MRI (Harloff et al, 2009;Morbiducci et al, 2011a;Lorenz et al, 2012). Morbiducci et al (2012) using a synthetic PC MRI dataset observed that the calculation of bulk flow quantities is less sensitive to spatiotemporal resolution with respect to WSS-based descriptors. Hence, bulk flow descriptors might be more suitable for in vivo applications.…”

The effect of resolution on viscous dissipation measured with 4D flow MRI in patients with Fontan circulation: Evaluation using computational fluid dynamics

“…Firstly, the velocities obtained from CFD were mapped into equally sized voxels of 0.1 mm, followed by 3D convolution integration with a 3D Gaussian operator (Casas et al, 2015; Morbiducci et al, 2012). Finally, down-sampled velocities were obtained by averaging the high resolution velocities within the voxels of the down-sampled grid (Cibis et al, 2014).…”

Physiological Significance of Helical Flow in the Arterial System and its Potential Clinical Applications

“…3 On the other hand, hemodynamic indicator such as helical flow index was proposed to reveal the global organization of blood flow, which was calculated from a Lagrangian-based method for helical flow quantification based on the velocity data from either computational fluid dynamics or phase contrast magnetic resonance imaging. 33,34,68,69,71,72 Through simulating the blood flow in an idealized model of aortocoronary bypass, Morbiducci et al found a linear inverse relationship between the oscillating shear index (a well-known risk index for atherogenesis) and the HFI. 51,71 Furthermore, based on hemodynamic simulations data of 50 carotid bifurcations, Gallo et al 33 demonstrated that the exposure of each carotid bifurcation to disturbed flow measured by time-averaged wall shear stress, oscillatory shear index and relative residence time decreased as the timeaveraged value of the absolute value of helicity increased.…”