The hemodynamics within the aorta of five healthy humans were investigated to gain insight into the complex helical flow patterns that arise from the existence of asymmetries in the aortic region. The adopted approach is aimed at (1) overcoming the relative paucity of quantitative data regarding helical blood flow dynamics in the human aorta and (2) identifying common characteristics in physiological aortic flow topology, in terms of its helical content. Four-dimensional phase-contrast magnetic resonance imaging (4D PC MRI) was combined with algorithms for the calculation of advanced fluid dynamics in this study. These algorithms allowed us to obtain a 4D representation of intra-aortic flow fields and to quantify the aortic helical flow. For our purposes, helicity was used as a measure of the alignment of the velocity and the vorticity. There were two key findings of our study: (1) intra-individual analysis revealed a statistically significant difference in the helical content at different phases of systole and (2) group analysis suggested that aortic helical blood flow dynamics is an emerging behavior that is common to normal individuals. Our results also suggest that helical flow might be caused by natural optimization of fluid transport processes in the cardiovascular system, aimed at obtaining efficient perfusion. The approach here applied to assess in vivo helical blood flow could be the starting point to elucidate the role played by helicity in the generation and decay of rotating flows in the thoracic aorta.

The mechanics of blood flow in arteries plays a key role in the health of individuals. In this framework, the role played by the presence of helical flow in the human aorta is still not clear in its relation to physiology and pathology. We report here a method for quantifying helical flow in vivo employing time-resolved cine phase contrast magnetic resonance imaging to obtain the complete spatio-temporal description of the three-dimensional pulsatile blood flow patterns in aorta. The method is applied to data of one healthy volunteer. Particle traces were calculated from velocity data: to them we applied a Lagrangian-based method for helical flow quantification, the Helical Flow Index, which has been developed and evaluated in silico in order to reveal global organization of blood flow. Our results: (i) put in evidence that the systolic hemodynamics in aorta is characterized by an evolving helical flow (we quantified a 24% difference in terms of the content of helicity in the streaming blood, between mid and early systole); (ii) indicate that in the first part of the systole helicity is ascrivable mainly to the asymmetry of blood flow in the left ventricle, joined with the laterality of the aorta. In conclusion, this study shows that the quantification of helical blood flow in vivo is feasible, and it might allow detection of anomalies in the expected physiological development of helical flow in aorta and accordingly, could be used in a diagnostic/prognostic index for clinical practice.

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Heartbeat measurement is important in assesssing cardiac function because variations in heart rhythm can be the cause as well as an effect of hidden pathological heart conditions. Zebrafish (Danio rerio) has emerged as one of the most useful model organisms for cardiac research. Indeed, the zebrafish heart is easily accessible for optical analyses without conducting invasive procedures and shows anatomical similarity to the human heart. In this study, we present a non-invasive, simple, cost-effective process to quantify the heartbeat in embryonic zebrafish. To achieve reproducibility, high throughput and flexibility (i.e., adaptability to any existing confocal microscope system and with a user-friendly interface that can be easily used by researchers), we implemented this method within a software program. We show here that this platform, called ZebraBeat, can successfully detect heart rate variations in embryonic zebrafish at various developmental stages, and it can record cardiac rate fluctuations induced by factors such as temperature and genetic- and chemical-induced alterations. Applications of this methodology may include the screening of chemical libraries affecting heart rhythm and the identification of heart rhythm variations in mutants from large-scale forward genetic screens.

The purpose of this study is to investigate how the imposition of personalized, non-invasively measured blood flow rates as boundary conditions (BCs) influences image-based computational hemodynamic studies in the human aorta. We extracted from 4D phase-contrast MRI acquisitions of a healthy human (1) the geometry of the thoracic aorta with supra-aortic arteries and (2) flow rate waveforms at all boundaries. Flow simulations were carried out, and the implications that the imposition of different BC schemes based on the measured flow rates have on wall shear stress (WSS)-based indicators of abnormal flow were analyzed. Our results show that both the flow rate repartition among the multiple outlets of the aorta and the distribution and magnitude of the WSS-based indicators are strongly influenced by the adopted BC strategy. Keeping as reference hemodynamic model the one where the applied BC scheme allowed to obtain a satisfactory agreement between the computed and the measured flow rate waveforms, differences in WSS-based indicators up to 49% were observed when the other BC strategies were applied. In conclusion, we demonstrate that in subject-specific computational hemodynamics models of the human aorta the imposition of BC settings based on non-invasively measured flow rate waveforms influences indicators of abnormal flow to a large extent. Hence, a BCs set-up assuring realistic, subject-specific instantaneous flow rate distribution must be applied when BCs such as flow rates are prescribed.

Computational fluid dynamics (CFD) models have become very effective tools for predicting the flow field within the carotid bifurcation, and for understanding the relationship between local hemodynamics, and the initiation and progression of vascular wall pathologies. As prescribing proper boundary conditions can affect the solutions of the equations governing blood flow, in this study, we investigated the influence to assumptions regarding the outflow boundary conditions in an image-based CFD model of human carotid bifurcation. Four simulations were conducted with identical geometry, inlet flow rate, and fluid parameters. In the first case, a physiological time-varying flow rate partition at branches along the cardiac cycle was obtained by coupling the 3D model of the carotid bifurcation at outlets with a lumped-parameter model of the downstream vascular network. Results from the coupled model were compared with those obtained by imposing three fixed flow rate divisions (50/50, 60/40, and 70/30) between the two branches of the isolated 3D model of the carotid bifurcation. Three hemodynamic wall parameters were considered as indicators of vascular wall dysfunction. Our findings underscore that the overall effect of the assumptions done in order to simulate blood flow within the carotid bifurcation is mainly in the hot-spot modulation of the hemodynamic descriptors of atherosusceptible areas, rather than in their distribution. In particular, the more physiological, time-varying flow rate division deriving from the coupled simulation has the effect of damping wall shear stress (WSS) oscillations (differences among the coupled and the three fixed flow partition models are up to 37.3% for the oscillating shear index). In conclusion, we recommend to adopt more realistic constraints, for example, by coupling models at different scales, as in this study, when the objective is the outcome prediction of alternate therapeutic interventions for individual patients, or to test hypotheses related to the role of local fluid dynamics and other biomechanical factors in vascular diseases.