Doppler flow imaging for the visualisation of neonatal intraventricular blood flow currently still has inherent limitations: beam-to-flow angle dependency, aliasing and a too low frame rate. Ultrafast imaging and vector flow estimation may resolve these limitations, yet both still require thorough validation for the pediatric cardiac setting. Hence, a computational modelling approach in the neonatal left ventricle was employed to investigate (i) diverging wave emission to acquire images at very high frame rate and (ii) subsequent speckle tracking algorithms for vector flow estimation. Single non-tilted diverging waves with an opening angle of 60° were transmitted, at a pulse repetition frequency of 9 kHz. Speckle tracking on the acquired ultrasound images provided 2D intraventricular flow estimates at a frame rate of 180 Hz for both the apical four chamber and parasternal short axis view, and this over an entire cardiac cycle. Overall, the blood flow was reasonably accurately tracked throughout the cardiac cycle, yet several imaging artefacts were observed. Zones of low flow proved very difficult to track due to clutter filtering issues, while high spatial flow gradients caused strong underestimation of systolic outflow.
DC-excited discharges in vapour bubbles in capillaries are studied. A bubble is generated in a capillary filled with a NaCl solution due to Joule heating. The fluid columns on either side of the bubble serve as electrodes for the electrical discharges inside the bubble. The electrical breakdown (corresponding to a corona-to-spark transition) of quasi-static vapour bubbles is discussed. The breakdown electrical field decreases with bubble length. For larger bubbles, the reduced electrical field is smaller than the electrical field where electron attachment equals ionization, indicating that the discharge is a surface discharge. Linear translation of bubbles in the cathode direction, coinciding with intense discharges inside the bubbles, is observed and can be explained by asymmetric heating due to the plasma. The optical emission spectrum of a vapour bubble discharge consists of excited hydroxyl, hydrogen and sodium emission. A delay in the range of 0.1 s is observed between the emission of hydroxyl and sodium. The sodium emission is most intense on the anode side of the bubble where orange anode spots are visible.
Stimulated by a recent controversy regarding pressure drops predicted in a giant aneurysm with a proximal stenosis, the present study sought to assess variability in the prediction of pressures and flow by a wide variety of research groups. In phase I, lumen geometry, flow rates, and fluid properties were specified, leaving each research group to choose their solver, discretization, and solution strategies. Variability was assessed by having each group interpolate their results onto a standardized mesh and centerline. For phase II, a physical model of the geometry was constructed, from which pressure and flow rates were measured. Groups repeated their simulations using a geometry reconstructed from a micro-computed tomography (CT) scan of the physical model with the measured flow rates and fluid properties. Phase I results from 25 groups demonstrated remarkable consistency in the pressure patterns, with the majority predicting peak systolic pressure drops within 8% of each other. Aneurysm sac flow patterns were more variable with only a few groups reporting peak systolic flow instabilities owing to their use of high temporal resolutions. Variability for phase II was comparable, and the median predicted pressure drops were within a few millimeters of mercury of the measured values but only after accounting for submillimeter errors in the reconstruction of the life-sized flow model from micro-CT. In summary, pressure can be predicted with consistency by CFD across a wide range of solvers and solution strategies, but this may not hold true for specific flow patterns or derived quantities. Future challenges are needed and should focus on hemodynamic quantities thought to be of clinical interest.
In the last years there is an increasing interest in patient-specific simulations of the fluid-structure interaction in aortic aneurysms, a.o. to better understand the growth and development of the aneurysm and to support diagnosis through assessment of its rupture potential. In order to verify these simulations, validation is an important and difficult task. Given ethical constraints, the slow time course of the disease in humans and the absence of true baseline data in a healthy aorta, these studies are difficult to perform in humans. This is particularly true for aneurysm rupture research, as rupture will normally be prevented by surgery and access to post-mortem tissue is not always possible. In order to overcome these problems an AAA mouse model can be used [1].
