Sensing central blood pressure with advanced imaging and AI technologies

Essential background
Some cardiac conditions cause an obstruction to the blood flow, and thus an extra burden to our heart that needs to be measured. A second problem is the limited ability of cardiologists to assess central blood pressure, inside the heart, key to risk stratify several conditions such as heart failure. In these two scenarios cardiologists need to use either accurate but risky sensors, or non-invasive but less accurate measurements or surrogates.

In our team at KCL we have approached this problem by exploiting two complementary approaches, the possibility to derive pressure differences based on the observation of blood velocity [1], and the ability to estimate absolute pressure with changes in the sub-harmonic response of microbubbles [2].

Aim of the investigation
The aim is to equip the cardiologist with the non-invasive and accurate measurement of central blood pressure and the extra burden caused by a flow obstruction. This will be possible combining sophisticated imaging, the properties of contrast agents and computational technologies. By building on existing unique expertise and equipment [1,2], the specific objective is to exploit the synergies between the two complementary approaches, exploring the hypothesis that the accuracy of the relative pressures obtained from flow dynamics can be used to calibrate the sub-harmonic response of microbubbles to deliver the absolute pressure.