Dynamic laser speckle imaging for velocimetry in blood flow: A numerical study

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.