Network-Based Characterization of Blood Large-Scale Coherent Motion in the Healthy Human Aorta With 4D Flow MRI.

Abstract

The need for distilling the hemodynamic complexity of aortic flows into clinically relevant quantities resulted in a loss of the information hidden in 4D aortic fluid structures. To reduce information loss, this study proposes a network-based approach to identify and characterize in vivo the large-scale coherent motion of blood in the healthy human aorta. The quantitative paradigm of the aortic flow as a "social network" was applied on 4D flow MRI acquisitions performed on forty-one healthy volunteers. Correlations between the aortic blood flow rate waveform at the proximal ascending aorta (AAo), assumed as one of the drivers of aortic hemodynamics, and the waveforms of the axial velocity in the whole aorta were used to build "one-to-all" networks. The impact of the driving flow rate waveform and of aortic geometric attributes on the transport of large-scale coherent fluid structures was investigated. The anatomical length of persistence of large-scale coherent motion was the 29.6% of the healthy thoracic aorta length (median value, IQR 23.1%-33.9%). Such length is significantly influenced by the average and peak-to-peak AAo blood flow rate values, suggesting a remarkable inertial effect of the AAo flow rate on the transport of large-scale fluid structures in the distal aorta. Aortic geometric attributes such as curvature, torsion and arch shape did not influence the anatomical length of persistence. The proposed in vivo approach allowed to quantitatively characterize the transport of large-scale fluid structures in the healthy aorta, strengthening the definition of coherent hemodynamic structures and identifying flow inertia rather than geometry as one of its main determinants. The findings on healthy aortas may be used as reference values to investigate the impact of aortic disease or implanted devices in disrupting/restoring the physiological spatiotemporal coherence of large-scale aortic flow.