Ultrasound-On-Chip Platform For Medical Imaging, Analysis, And Collective Intelligence

Jonathan M Rothberg,Tyler S Ralston,Alex G Rothberg,John Martin, Jaime S Zahorian, Susan A Alie,Nevada J Sanchez,Kailiang Chen,Chao Chen, Karl Thiele, David Grosjean,Jungwook Yang,Liewei Bao, Rob Schneider,Sebastian Schaetz, Christophe Meyer, Abraham Neben,Bob Ryan,J R Petrus, Joe Lutsky,Dan McMahill, Gregory Corteville, Matthew R Hageman, Larry Miller,Keith G Fife

PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA(2021)

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摘要
Over the past half-century, ultrasound imaging has become a key technology for assessing an ever-widening range of medical conditions at all stages of life. Despite ultrasound's proven value, expensive systems that require domain expertise in image acquisition and interpretation have limited its broad adoption. The proliferation of portable and low-cost ultrasound imaging can improve global health and also enable broad clinical and academic studies with great impact on the fields of medicine. Here, we describe the design of a complete ultrasound-on-chip, the first to be cleared by the Food and Drug Administration for 13 indications, comprising a two-dimensional array of silicon-based microelectromechanical systems (MEMS) ultrasonic sensors directly integrated into complementary metal-oxide-semiconductor-based control and processing electronics to enable an inexpensive whole-body imaging probe. The fabrication and design of the transducer array with on-chip analog and digital circuits, having an operating power consumption of 3 W or less, are described, in which approximately 9,000 sevenlevel feedback-based pulsers are individually addressable to each MEMS element and more than 11,000 amplifiers, more than 1,100 analog-to-digital converters, and more than 1 trillion operations per second are implemented. We quantify the measured performance and the ability to image areas of the body that traditionally takes three separate probes. Additionally, two applications of this platform are described-augmented reality assistance that guides the user in the acquisition of diagnostic-quality images of the heart and algorithms that automate the measurement of cardiac ejection fraction, an indicator of heart health.
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关键词
ultrasound, cardiology, semiconductors, global health, machine learning
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