Temporal dynamics of the fluids in the vasculature can provide us information such as blood perfusion, pulse-rate, heart-rate variability. The team has expertise in both imaging and estimating temporal dynamics:
This project develops a new computational imaging technique to measure the speed of blood flow in skin tissue (known as perfusion). We produce speed maps where different colors indicate different speeds of flow.
Blood perfusion is the flow of oxygen-rich blood to the end organs and tissue through the blood vessels in the body. It is vital in ensuring oxygen delivery to the cells and in maintaining metabolic homeostasis. Measuring peripheral perfusion (or microcirculation) is needed to monitoring critical care patients in ICU, and to diagnose peripheral arterial disease. Significant research has demonstrated that regional blood flow is not well-captured by currently measured macro-circulatory parameters, like blood pressure and heart rate. Therefore, critical care doctors require information about micro-circulatory parameters to quantify regional blood flow in different body parts. But existing point modalities to measure regional blood flow such as laser Doppler flowmeter and perfusion index (measured using pulse oximeter) are not effective as they cannot capture spatial variations in blood flow which has diagnostic value. Therefore, one needs an imaging device which can continuously measure maps of regional blood flow.
Measuring and monitoring any patient’s vital signs is essential for their care – in fact, all care first begins by collecting vital signs like heart rate and blood pressure. The current standard of care is based on monitoring devices that require contact – electrocardiograms, pulse-oximeter, blood pressure cuffs, and chest straps. However, contact-based methods have serious limitations for monitoring vital signs of neonates as they have extremely sensitive skin. Most contact-based vital sign monitoring techniques result in skin abrasions, peeling and damage every time the leads or patches are removed. This further results in potentially dangerous sites for infection increasing the mortality risk to the neonates. We propose to use normal camera to measure the vital signs of a patient by simply recording video of their face in a non-contact manner. From the recorded video of the face, our algorithm, distancePPG, extracts pulse rate (PR), pulse rate variability (PRV) and breathing rate (BR). The algorithm is based on estimating tiny changes in skin color due to changes in blood volume underneath the skin surface (these changes are invisible to the naked eye, but can be captured by a camera).