Ultrasound-on-chip platform for medical imaging, analysis, and collective intelligence.

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.

Accelerated Computing in Magnetic Resonance Imaging: Real-Time Imaging Using Nonlinear Inverse Reconstruction.

PURPOSE: To develop generic optimization strategies for image reconstruction using graphical processing units (GPUs) in magnetic resonance imaging (MRI) and to exemplarily report on our experience with a highly accelerated implementation of the nonlinear inversion (NLINV) algorithm for dynamic MRI with high frame rates. METHODS: The NLINV algorithm is optimized and ported to run on a multi-GPU single-node server. The algorithm is mapped to multiple GPUs by decomposing the data domain along the channel dimension. Furthermore, the algorithm is decomposed along the temporal domain by relaxing a temporal regularization constraint, allowing the algorithm to work on multiple frames in parallel. Finally, an autotuning method is presented that is capable of combining different decomposition variants to achieve optimal algorithm performance in different imaging scenarios. RESULTS: The algorithm is successfully ported to a multi-GPU system and allows online image reconstruction with high frame rates. Real-time reconstruction with low latency and frame rates up to 30 frames per second is demonstrated. CONCLUSION: Novel parallel decomposition methods are presented which are applicable to many iterative algorithms for dynamic MRI. Using these methods to parallelize the NLINV algorithm on multiple GPUs, it is possible to achieve online image reconstruction with high frame rates.

Real-time magnetic resonance imaging of cardiac function and flow-recent progress.

Cardiac structure, function and flow are most commonly studied by ultrasound, X-ray and magnetic resonance imaging (MRI) techniques. However, cardiovascular MRI is hitherto limited to electrocardiogram (ECG)-synchronized acquisitions and therefore often results in compromised quality for patients with arrhythmias or inabilities to comply with requested protocols-especially with breath-holding. Recent advances in the development of novel real-time MRI techniques now offer dynamic imaging of the heart and major vessels with high spatial and temporal resolution, so that examinations may be performed without the need for ECG synchronization and during free breathing. This article provides an overview of technical achievements, physiological validations, preliminary patient studies and translational aspects for a future clinical scenario of cardiovascular MRI in real time.

Real-time flow MRI of the aorta at a resolution of 40 msec.

PURPOSE: To evaluate a novel real-time phase-contrast magnetic resonance imaging (MRI) technique for the assessment of through-plane flow in the ascending aorta. MATERIALS AND METHODS: Real-time MRI was based on a radial fast low-angle shot (FLASH) sequence with about 30-fold undersampling and image reconstruction by regularized nonlinear inversion. Phase-contrast maps were obtained from two (interleaved or sequential) acquisitions with and without a bipolar velocity-encoding gradient. Blood flow in the ascending aorta was studied in 10 healthy volunteers at 3 T by both real-time MRI (15 sec during free breathing) and electrocardiogram (ECG)-synchronized cine MRI (with and without breath holding). Flow velocities and stroke volumes were evaluated using standard postprocessing software. RESULTS: The total acquisition time for a pair of phase-contrast images was 40.0 msec (TR/TE = 2.86/1.93 msec, 10° flip angle, 7 spokes per image) for a nominal in-plane resolution of 1.3 mm and a section thickness of 6 mm. Quantitative evaluations of spatially averaged flow velocities and stroke volumes were comparable for real-time and cine methods when real-time MRI data were averaged across heartbeats. For individual heartbeats real-time phase-contrast MRI resulted in higher peak velocities for values above 120 cm s(-1). CONCLUSION: Real-time phase-contrast MRI of blood flow in the human aorta yields functional parameters for individual heartbeats. When averaged across heartbeats real-time flow velocities and stroke volumes are comparable to values obtained by conventional cine MRI.