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Significance Affordable hand-held ultrasound is transforming health care as a diagnostic tool with the potential to be as ubiquitous as the stethoscope. Here, we present a platform for advancing diagnostic care consisting of an ultrasound-on-chip probe, leveraging state-of-the-art silicon-based semiconductor foundries, paired with a mobile device and artificial-intelligence–guided image interpretation and cloud interconnectivity. Demonstrations across key organs and modes illustrate the imaging capabilities. Presentations of automated guidance for untrained ultrasound users show the potential for further broadening accessibility and utility., 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 seven-level 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.

Point-of-care ultrasound (POCUS) is transforming healthcare worldwide as a diagnostic tool with the potential to significantly reduce the delay between symptom onset and initiation of therapy. Conventional POCUS systems are based on piezoelectric transducers and cable-connected electronics, which require a costly manufacturing process and usually come with an undesirably limited channel count. Such devices typically serve a specific subset of clinical applications, as imaging at different body parts calls for different ultrasound frequencies that are beyond the bandwidth of a single piezoelectric transducer. To enable whole-body imaging, multiple probes with different frequencies, apertures and beamforming (BF) methods are generally required. This further limits the affordability and accessibility of POCUS. Recent advances in micromachined ultrasound transducers (MUTs) have offered an alternative path to addressing these challenges. However, previous attempts to integrate MUTs with chips have been incomplete, neither solving the integration problem [1, 2] nor achieving full ultrasound processing capabilities [3, 4].

We report on the MIT Epoch of Reionization (MITEoR) experiment, a pathfinder low-frequency radio interferometer whose goal is to test technologies that improve the calibration precision and reduce the cost of the high-sensitivity 3D mapping required for 21 cm cosmology. MITEoR accomplishes this by using massive baseline redundancy, which enables both automated precision calibration and correlator cost reduction. We demonstrate and quantify the power and robustness of redundancy for scalability and precision. We find that the calibration parameters precisely describe the effect of the instrument upon our measurements, allowing us to form a model that is consistent with $��^2$ per degree of freedom < 1.2 for as much as 80% of the observations. We use these results to develop an optimal estimator of calibration parameters using Wiener filtering, and explore the question of how often and how finely in frequency visibilities must be reliably measured to solve for calibration coefficients. The success of MITEoR with its 64 dual-polarization elements bodes well for the more ambitious Hydrogen Epoch of Reionization Array (HERA) project and other next-generation instruments, which would incorporate many identical or similar technologies., 22 pages, 20 figures. Updated to match the accepted version for MNRAS. Supersedes arXiv:1309.2639. Movies, data and links at http://space.mit.edu/home/tegmark/omniscope.html

We present a new method for interferometric imaging that is ideal for the large fields of view and compact arrays common in 21 cm cosmology. We first demonstrate the method with the simulations for two very different low-frequency interferometers, the Murchison Widefield Array and the MIT Epoch of Reionization (MITEoR) experiment. We then apply the method to the MITEoR data set collected in 2013 July to obtain the first northern sky map from 128 to 175 MHz at ∼2° resolution and find an overall spectral index of −2.73 ± 0.11. The success of this imaging method bodes well for upcoming compact redundant low-frequency arrays such as Hydrogen Epoch of Reionization Array. Both the MITEoR interferometric data and the 150 MHz sky map are available at http://space.mit.edu/home/tegmark/omniscope.html., National Science Foundation (U.S.) (AST-0908848), National Science Foundation (U.S.) (AST-1105835), National Science Foundation (U.S.) (AST-1440343)

The so-called corner turning problem is a major bottleneck for radio telescopes with large numbers of antennas. The problem is essentially that of rapidly transposing a matrix that is too large to store on one single device; in radio interferometry, it occurs because data from each antenna needs to be routed to an array of processors that will each handle a limited portion of the data (a frequency range, say) but requires input from each antenna. We present a low-cost solution allowing the correlator to transpose its data in real time, without contending for bandwidth, via a butterfly network requiring neither additional RAM memory nor expensive general-purpose switching hardware. We discuss possible implementations of this using FPGA, CMOS, analog logic and optical technology, and conclude that the corner turner cost can be small even for upcoming massive radio arrays.