Cardiovascular diseases are one of the leading causes of death worldwide, for example by causing strokes. Timely diagnosis of such diseases is pivotal for a patient’s chance of survival. Furthermore, in the present world in which medical expenses are going through the roof, we can save greatly on costs if certain diseases are detected in an earlier stage. To that end, our research is focused on improving medical measurement techniques, to give doctors a greater arsenal to combat these diseases. Ideally, a measurement technique is cheap, accurate, and all while causing minimal discomfort to the patient. Light-based techniques have proven previously to have great potential to fulfil that role. For example, that tiny device that you can put on your finger, and similarly the sensor in a sports watch, are able to measure your heart rate using light. For our research we have developed a computer model, such that we can use the power of modern computing. Our model is able to predict how light is reflected by red blood cells flowing through an artery. The computer is then able to rapidly simulate many scenarios, producing a lot of data about what the reflected light looks like for each scenario. From that data, we are able to say something about what a certain pattern in the reflected light says about the underlying system: the flowing red blood cells. As a first step, we have used our model to figure out how we can determine the heart rate from the reflected light. You could argue that that’s nothing special, as your sports watch can already do precisely that, but it’s an important step nonetheless, since our technique is different than what your sports watch is doing. Namely, the data our technique provides is more complex, but as a consequence also contains much more information and thereby yields a greater potential if we just become able to extract that information from the data. Therefore, our second step was to determine the exact velocity of the red blood cells from the reflected light, which is quite of a magical thing when you think about it: even though we cannot ‘see’ the red blood cells directly, we can still ‘see’ how fast they are moving. Although we succeeded in determining the velocity, in reality a doctor will likely need to do some tweaking to account for patient-specific factors, such as skin tone. Finally, we studied the disease atherosclerosis, in which accumulating cholesterol causes arteries to become more narrow, which ultimately could lead to a stroke. The narrowing of an artery, alters the flow behavior of the red blood cells, which we were able to pick up by studying changing patterns in the reflected light from our simulations. By extension, it should be possible to use reflected light to detect atherosclerosis, rapidly and cheaply flagging patients who are at risk. We have shown the potential of reflected light techniques for medical diagnosis purposes. Although further research and work is still required to put these techniques into practice for doctor’s to use, we have set the groundwork to enable these techniques in the not-too-distant future. @en

We study how the speckle contrast depends on scatterer velocity, with the goal of further developing laser speckle imaging as a quantitative measurement technique. To that end, we perform interferometric computer simulations on a dilute plug flow. The results of our numerical experiment, that we compare with known analytical expressions to confirm their veracity, match well at low velocities with the Gaussian expression. Finally, we address the issue of how velocity depends on speckle decorrelation time, and show that the speckle size is most likely the relevant connecting length scale.

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The disease atherosclerosis causes stenosis inside the patient's arteries, which often eventually turns lethal. Our goal is to detect a stenosis in a non-invasive manner, preferably in an early stage. To that end, we study whether and how laser speckle contrast imaging (LSCI) can be deployed. We start out by using computational fluid dynamics on a patient-specific stenosed carotid artery to reveal the flow profile in the region surrounding the stenosis, which compares well with particle image velocimetry experiments. We then use our own fully interferometric dynamic light scattering routines to simulate the process of LSCI of the carotid artery. Our approach offers an advantage over the established Monte Carlo techniques because they cannot incorporate dynamics. From the simulated speckle images, we extract a speckle contrast time series at different sites inside the artery, of which we then compute the frequency spectrum. We observe an increase in speckle boiling in sites where the flow profile is more complex, e.g., containing regions of backflow. In the region surrounding the stenosis, the measured speckle contrast is considerably lower due to the higher local velocity, and the frequency signature becomes notably different with prominent higher-order frequency modes that were absent in the other sites. Although future work is still required to make our new approach more quantitative and more applicable in practice, we have provided a first insight into how a stenosis might be detected in vivo using LSCI.

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Laser speckle imaging (LSI) can be used to study dynamic processes in turbid media, such as blood flow. However, it is presently still challenging to obtain meaningful quantitative information from speckle, mainly because speckle is the interferometric summation of multiply scattered light. Consequently, speckle represents a convolution of the local dynamics of the medium. In this paper, we present a computational model for simulating the LSI process, which we aim to use for improving our understanding of the underlying physics. Thereby reliable methods for extracting meaningful information from speckle can be developed. To validate our code, we apply it to a case study resembling blood flow: a cylindrical fluid flow geometry seeded with small spherical particles and modulated with a heartbeat signal. From the simulated speckle pattern, we successfully retrieve the main frequency modes of the original heartbeat signal. By comparing Poiseuille flow to plug flow, we show that speckle boiling causes a small amount of uniform spectral noise. Our results indicate that our computational model is capable of simulating LSI and will therefore be useful in future studies for further developing LSI as a quantitative imaging tool.

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While various multiphase flow simulation techniques have found acceptance as predictive tools for processes involving immiscible fluids, none of them can be considered universally applicable. Focusing on accurate simulation of liquid-liquid emulsions at the scale of droplets, we present a comparative assessment of the single-component multiphase pseudopotential lattice Boltzmann method (PP-LB, classical and modified) and the Volume of Fluid method (VOF, classical and modified), highlighting particular strengths and weaknesses of these techniques. We show that a modified LB model produces spurious velocities 1–3 orders of magnitude lower than all VOF models tested, and find that LB is roughly 10 times faster in computation time, while VOF is more versatile. Simulating falling liquid droplets, a realistic problem, we find that despite identical setups, results can vary with the technique in certain flow regimes. At lower Reynolds numbers, all methods agree reasonably well with experimental values. At higher Reynolds numbers, all methods underpredict the droplet Reynolds number, while being in good agreement with each other. Particular issues regarding LB simulations at low density ratio are emphasized. Finally, we conclude with the applicability of VOF vis-à-vis PP-LB for a general range of multiphase flow problems relevant to myriad applications.

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