PROTEUS: A Physically Realistic Contrast-Enhanced Ultrasound Simulator-Part I: Numerical Methods.

Ultrasound contrast agents have been used as vascular reporters for the past 40 years. The ability to enhance vascular features in ultrasound images with engineered lipid-shelled microbubbles has enabled break-throughs such as the detection of tissue perfusion or super-resolution imaging of the microvasculature. However, advances in the field of contrast-enhanced ultra-sound are hindered by experimental variables that are difficult to control in a laboratory setting, such as complex vascular geometries, the lack of ground truth, and tissue nonlinearities. In addition, the demand for large datasets to train deep learning-based computational ultrasound imaging methods calls for the development of a simulation tool that can reproduce the physics of ultrasound wave interactions with tissues and microbubbles. Here, we introduce a physically realistic contrast-enhanced ultrasound simulator (PROTEUS) consisting of four inter-connected modules that account for blood flow dynamics in segmented vascular geometries, intravascular microbubble trajectories, ultrasound wave propagation, and nonlinear microbubble scattering. The first part of this study describes numerical methods that enabled this development. We demonstrate that PROTEUS can generate contrast-enhanced radiofrequency data in various vascular architectures across the range of medical ultrasound frequencies. PROTEUS offers a customizable framework to explore novel ideas in the field of contrast-enhanced ultrasound imaging. It is released as an open-source tool for the scientific community.

Super-Resolved Microbubble Localization in Single-Channel Ultrasound RF Signals Using Deep Learning.

Recently, super-resolution ultrasound imaging with ultrasound localization microscopy (ULM) has received much attention. However, ULM relies on low concentrations of microbubbles in the blood vessels, ultimately resulting in long acquisition times. Here, we present an alternative super-resolution approach, based on direct deconvolution of single-channel ultrasound radio-frequency (RF) signals with a one-dimensional dilated convolutional neural network (CNN). This work focuses on low-frequency ultrasound (1.7 MHz) for deep imaging (10 cm) of a dense cloud of monodisperse microbubbles (up to 1000 microbubbles in the measurement volume, corresponding to an average echo overlap of 94%). Data are generated with a simulator that uses a large range of acoustic pressures (5-250 kPa) and captures the full, nonlinear response of resonant, lipid-coated microbubbles. The network is trained with a novel dual-loss function, which features elements of both a classification loss and a regression loss and improves the detection-localization characteristics of the output. Whereas imposing a localization tolerance of 0 yields poor detection metrics, imposing a localization tolerance corresponding to 4% of the wavelength yields a precision and recall of both 0.90. Furthermore, the detection improves with increasing acoustic pressure and deteriorates with increasing microbubble density. The potential of the presented approach to super-resolution ultrasound imaging is demonstrated with a delay-and-sum reconstruction with deconvolved element data. The resulting image shows an order-of-magnitude gain in axial resolution compared to a delay-and-sum reconstruction with unprocessed element data.

SonoTweezer: An Acoustically Powered End-Effector for Underwater Micromanipulation.

Recent advances in contactless micromanipulation strategies have revolutionized prospects of robotic manipulators as next-generation tools for minimally invasive surgeries. In particular, acoustically powered phased arrays offer dexterous means of manipulation both in air and water. Inspired by these phased arrays, we present SonoTweezer: a compact, low-power, and lightweight array of immersible ultrasonic transducers capable of trapping and manipulation of sub-mm sized agents underwater. Based on a parametric investigation with numerical pressure field simulations, we design and create a six-transducer configuration, which is small compared to other reported multi-transducer arrays (16-256 elements). Despite the small size of array, SonoTweezer can reach pressure magnitudes of 300 kPa at a low supply voltage of 25 V to the transducers, which is in the same order of absolute pressure as multi-transducer arrays. Subsequently, we exploit the compactness of our array as an end-effector tool for a robotic manipulator to demonstrate long-range actuation of sub-millimeter agents over a hundred times the agent's body length. Furthermore, a phase-modulation over its individual transducers allows our array to locally maneuver its target agents at sub-mm steps. The ability to manipulate agents underwater makes SonoTweezer suitable for clinical applications considering water's similarity to biological media, e.g., vitreous humor and blood plasma. Finally, we show trapping and manipulation of micro-agents under medical ultrasound (US) imaging modality. This application of our actuation strategy combines the usage of US waves for both imaging and micromanipulation.

Rebound of self-lubricating compound drops.

Drop impact on solid surfaces is encountered in numerous natural and technological processes. Although the impact of single-phase drops has been widely explored, the impact of compound drops has received little attention. Here, we demonstrate a self-lubrication mechanism for water-in-oil compound drops impacting on a solid surface. Unexpectedly, the core water drop rebounds from the surface below a threshold impact velocity, irrespective of the substrate wettability. This is interpreted as the result of lubrication from the oil shell that prevents contact between the water core and the solid surface. We combine side and bottom view high-speed imaging to demonstrate the correlation between the water core rebound and the oil layer stability. A theoretical model is developed to explain the observed effect of compound drop geometry. This work sets the ground for precise complex drop deposition, with a strong impact on two- and three-dimensional printing technologies and liquid separation.