Exploring wall shear stress spatiotemporal heterogeneity in coronary arteries combining correlation-based analysis and complex networks with computational hemodynamics

Abstract

Atherosclerosis at the early stage in coronary arteries has been associated with low cycle-average wall shear stress magnitude. However, parallel to the identification of an established active role for low wall shear stress in the onset/progression of the atherosclerotic disease, a weak association between lesions localization and low/oscillatory wall shear stress has been observed. In the attempt to fully identify the wall shear stress phenotype triggering early atherosclerosis in coronary arteries, this exploratory study aims at enriching the characterization of wall shear stress emerging features combining correlation-based analysis and complex networks theory with computational hemodynamics. The final goal is the characterization of the spatiotemporal and topological heterogeneity of wall shear stress waveforms along the cardiac cycle. In detail, here time-histories of wall shear stress magnitude and wall shear stress projection along the main flow direction and orthogonal to it (a measure of wall shear stress multidirectionality) are analyzed in a representative dataset of 10 left anterior descending pig coronary artery computational hemodynamics models. Among the main findings, we report that the proposed analysis quantitatively demonstrates that the model-specific inlet flow-rate shapes wall shear stress time-histories. Moreover, it emerges that a combined effect of low wall shear stress magnitude and of the shape of the wall shear stress–based descriptors time-histories could trigger atherosclerosis at its earliest stage. The findings of this work suggest for new experiments to provide a clearer determination of the wall shear stress phenotype which is at the basis of the so-called arterial hemodynamic risk hypothesis in coronary arteries.

Combining 4D Flow MRI and Complex Networks Theory to Characterize the Hemodynamic Heterogeneity in Dilated and Non-dilated Human Ascending Aortas

“…A main limitation that could potentially weaken the findings of this study is the choice of the correlation threshold used to build the CNs. Consistently with previous CNs-based in silico studies, 7 , 8 , 13 the CNs threshold for a specific hemodynamic quantity was set equal to the median value of the correlation coefficients distribution obtained from a dataset of computational hemodynamic models of healthy aortas. Further studies on a 4D flow MRI dataset of healthy subjects could be useful to investigate the robustness of the selected thresholds.…”

“…The spatiotemporal heterogeneity of the aortic intravascular flow was investigated by using 4D flow MRI data to build patient-specific correlation-based CNs, according to the scheme recently applied to in silico data. 7 , 8 , 13 For each patient, three CNs were built from | V |, V ax and V sc time-histories along the cardiac cycle. Each node of a CN was defined by the voxel belonging to the aortic fluid domain where the time-history of the considered hemodynamic quantity was acquired.…”

“…14,28,36,46 In recent years, 4D flow magnetic resonance imaging (MRI) has been increasingly used to obtain information on both aortic morphology and hemodynamics 5,14,31,35,43 -in particular in the presence of aortic vascular/valve pathologies 4,16,23,31 -providing risk markers of AAo wall degeneration. 14,25,43 With the objective of providing a comprehensive characterization of the spatiotemporal heterogeneity of large-scale aortic flow features and of their possible links with AAo dilation, a recently proposed approach integrating computational hemodynamics with Complex Networks (CNs) theory 7,8,13,37 is here extended for the first time to 4D flow MRI in patients with and without aortic dilation. In this study we explored the capability of CNs to characterize in vivo the dynamics of dominant aortic flow features using the time-histories of the measured velocity data along the cardiac cycle.…”

“…With the objective of providing a comprehensive characterization of the spatiotemporal heterogeneity of large-scale aortic flow features and of their possible links with AAo dilation, a recently proposed approach integrating computational hemodynamics with Complex Networks (CNs) theory 7 , 8 , 13 , 37 is here extended for the first time to 4D flow MRI in patients with and without aortic dilation. In this study we explored the capability of CNs to characterize in vivo the dynamics of dominant aortic flow features using the time-histories of the measured velocity data along the cardiac cycle.…”

Physiological fluid mechanics: A special Issue with a taster of forefront research

“…While the previous studies report on and/or apply well established and widely used wall shear stress metrics, the contribution of Calo' et al 3 is of a more fundamental fluid mechanics nature. Rather than seeking to grasp and quantify the complexity of blood flow in a single parameter like the time averaged wall shear stress or the oscillatory shear index -most convenient for statistical analysis thoughthey embrace the complexity of the flow field and characterize time-histories of WSS magnitude and WSS projection along the main flow direction and orthogonal to it.…”