A wearable orthogonal ultrasound array for continuous imaging of the heart - PROJECT SUMMARY Cardiovascular disease is a leading cause of mortality and morbidity worldwide. Currently, echocardiography (i.e., ultrasound imaging of the heart) is the standard-of-care for optimal assessment and measurement of patient heart function. Both transthoracic and transesophageal echocardiography are commonly used in hospitalized patients, but they have limitations. Transthoracic echocardiography is non-invasive but data is only collected while the probe is manually positioned on the patient's chest, which may result in missing important changes in a patient's clinical course and windows for potential intervention. Transesophageal echocardiography provides real-time measurement of different indices of cardiac function, but requires general anesthesia or deep sedation. To address these limitations, this proposed project aims to develop a wearable ultrasonic imager for continuous, non-invasive monitoring of cardiac functions in real-time via a transthoracic approach. Based on our previous work in wearable ultrasound blood pressure and flow sensors, the proposed device will utilize a stretchable ultrasonic device with reduced transducer element pitch, enabled by a multilayered, highly stretchable and highly integrated liquid metal electrode. The small pitch allows high transducer frequency and thus enhanced imaging performance. The device will be designed to monitor the heart in two orthogonal planes simultaneously and will use associated algorithms for B-mode imaging. To map the transducer locations on the dynamic human body, we will use shape sensing multicore optical fibers to acquire the exact coordinates of each transducer and use these location data for beamforming . We will use the wide-beam compounding transmission strategy, which can enhance the signal-to-noise ratio of ultrasound signals and therefore overall imaging performance of the device. The results will be benchmarked against those from a clinical ultrasound probe. We will construct a deep learning model based on Fully Convolutional Networks to process the acquired cardiac images automatically. We will extract the left ventricular wall motion waveforms and left ventricular volume, from which critical indices, such as stroke volume, ejection fraction, and cardiac output, can be generated continuously. The performance of the device will be evaluated on a commercial phantom and tested on human subjects. After verifying the safety of the proposed device, we will use it to collect the cardiac images from 138 healthy and patient subjects. The accuracy of the results collected by the proposed device will be compared and analyzed statistically with that obtained by a clinical-grade ultrasound machine. Our team is composed of highly interdisciplinary expertise that is required for the success of the proposed research. If successful, this technology has the potential to significantly improve cardiovascular monitoring and open new use cases for monitoring other critical visceral organ, which will exert a sustained and powerful impact on many NIH-related research areas s.
