Cardiovascular diseases (CVD) are the leading cause of death globally, affecting 422.7M people per year. Ischemia with non-obstructive coronary artery (INOCA) increases the risk of major cardiac events, with a 1.5x increased mortality rate. The lack of effective and accurate tools for the timely evaluation of coronary impairments creates an unmet need with a substantial market opportunity. This project aims to develop a blood flow velocity called PhysioCath based on the thermo-convection principle and to be used for the effective diagnosis of INOCA.
Medyria is developing a proprietary sensor for measuring local, time-resolved blood flow velocities based on the thermal convection principle. The sensor can be small enough for a micro endovascular device and it is combined with an off-the-shelf pressure sensor in a specifically developed microcatheter, the so-called PhysioCath. The system is simple enough to be used during an angiography allowing the operator
to see the various cardiac indexes in real-time on a portable monitor.
Based on measurements with a PhysioCath prototype it was found that under external flow conditions, common cardiac indexes used in INOCA diagnostics could be obtained with much higher accuracy and
reliability compared to the current state-of-the-art. Also, due to the more powerful velocity measuring principle, more refined cardiac indexes could be obtained which has stirred great interest among the clinical partners from USZ and Cardiocentro Ticino.
Calculation of physiological indices requires the measurement of flow velocity and pressure. At a first glance, these measurements appear to be simple tasks. However, pulsating blood flow within the coronary artery system is very dynamic and complex. The local morphology of the artery also affects the flow pattern. In addition, the PhysioCath signals are prone to artifacts such as the presence of the microcatheter itself in the blood and mismatches between the orientations of the velocity sensors and the flow.
Hence, for a precise measurement of velocity and pressure, it is necessary to describe blood flow and its physical interactions with the sensor on a quantitative level. Experimental blood flow data together with simulation data will be used at ZHAW to train machine learning algorithms to compensate for the sensor signals from measurement errors. If compensation is not possible, these algorithms should then discriminate between usable and unusable sensor signals to prompt the operator to accordingly adjust the position of the PhysioCath tip. This way, physicians can continue to use their standard tools and procedures for the angiography, whilst receiving additional data from the PhysioCath about possible INOCA impairments, critical to an early and accurate diagnosis.
At ICP, the initial phase of the project was devoted to developing a FE/CFD-model of the PhysioCath system in order to supply a large amount of simulation data to train machine learning. The major challenge of this thermal-flow model is given by its operational mode. The velocity sensor is based on the anemometer principle. Given a heating source and a temperature sensor both immersed or close to a fluid, by keeping a constant temperature above ambient on the sensor, the supplied electric power to the heater will depend on the flow velocity. By knowing this latter relation, measuring the dissipated power yields the flow velocity. The particularity of Medyria's system is that the heating source is also used as a temperature sensor which can be realized if the electric conductivity of the heating resistor shows some temperature dependency. By knowing this dependency and by measuring voltage and current at the resistor, one can correlate these two values with some average of the sensor's temperature. Hence, we can control the sensor's temperature, and the dissipated power yields the flow velocity.
The numerical challenge is that the sensor's temperature is not constant and its averaged value is controlled externally up to six flow sensors may be used for compensating signal errors. Therefore, computing a consistent thermal-flow solution is everything else as standard.