The field of contrast-enhanced ultrasound (CEUS) combines nonlinearly oscillating microbubbles (MBs) with dedicated pulse sequences to reveal the vascular function of organs. Clinical ultrasound contrast agents consist of polydisperse MB suspensions with diameters ranging from 0.5 to 10 μ⁢m and resonance frequencies ranging from 1 to 15 MHz. As a result, just a small fraction of MBs resonates at a given ultrasound frequency. MB suspensions with narrow size distributions can be tuned for a specific imaging frequency, boost CEUS sensitivity, and enable deeper vascular imaging. However, their enhanced nonlinear behavior makes imaging susceptible to nonlinear-wave-propagation artifacts. Here we numerically investigate the impact of the acoustic wavefront shape on the imaging of nonlinearly oscillating, monodisperse MBs. Specifically, our approach relies on an extension of the iterative nonlinear-contrast-source method that accounts for all nonlinear effects in CEUS. We demonstrate that supersonic X-shaped wavefronts referred to as “X waves” can be used to generate ultrasound images of monodisperse MBs without nonlinear-wave-propagation artifacts. In contrast, imaging based on focused, planar, and diverging wavefronts leads to significant nonlinear artifacts. Taken together, our results show that X waves can harness the full potential of monodisperse MBs by enabling their sensitive and specific detection in a tissue context.@en

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.@en

The influence of the transducer lens on image reconstruction is often overlooked. Lenses usually exhibit a lower sound speed than soft biological tissues. In academic research, the exact lens sound speed and thickness are typically unknown. Here we present a simple and nondestructive method to characterize the lens sound speed and thickness as well as the time to peak of the round-trip ultrasound waveform, another key parameter for optimal image reconstruction. We applied our method to three transducers with center frequencies of 2.5, 7.5 and 15 MHz. We estimated the three parameters with an element-by-element transmission sequence that records internal reflections within the lens. We validated the retrieved parameters using an autofocusing approach that estimates sound speed in water. We show that the combination of our parameters estimation method with two-layer ray tracing outperforms standard image reconstruction. For all transducers, we successfully improved the accuracy of medium sound speed estimation, spatial resolution and contrast. The proposed method is simple and robust and provides an accurate estimation of the transducer lens parameters and of the time to peak of the ultrasound waveform which leads to improved ultrasound image quality.

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Gas vesicles are gas-filled nanocompartments that allow a diverse group of bacteria and archaea to control their buoyancy. The molecular basis of their properties and assembly remains unclear. Here, we report the 3.2 Å cryo-EM structure of the gas vesicle shell made from the structural protein GvpA that self-assembles into hollow helical cylinders closed off by cone-shaped tips. Two helical half shells connect through a characteristic arrangement of GvpA monomers, suggesting a mechanism of gas vesicle biogenesis. The fold of GvpA features a corrugated wall structure typical for force-bearing thin-walled cylinders. Small pores enable gas molecules to diffuse across the shell, while the exceptionally hydrophobic interior surface effectively repels water. Comparative structural analysis confirms the evolutionary conservation of gas vesicle assemblies and demonstrates molecular features of shell reinforcement by GvpC. Our findings will further research into gas vesicle biology and facilitate molecular engineering of gas vesicles for ultrasound imaging.

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Ultrasound localization microscopy (ULM) is a vascular imaging method that provides a 10-fold improvement in resolution compared to ultrasound Doppler imaging. Because typical ULM acquisitions accumulate large numbers of synthetic microbubble (MB) trajectories over hundreds of cardiac cycles, transient hemodynamic variations such as pulsatility get averaged out. Here we introduce two independent processing methods to retrieve pulsatile flow characteristics from MB trajectories sampled at kilohertz frame rates and demonstrate their potential on a simulated dataset. The first approach follows a Lagrangian description of the flow. We filter the MB trajectories to eliminate ULM localization grid artifacts and successfully recover the pulsatility fraction Pf with a root mean square error (RMSE) of 3.3%. Our second approach follows a Eulerian description of the flow. It relies on the accumulation of MB velocity estimates as observed from a stationary observer. We show that pulsatile flow gives rise to a bimodal velocity distribution with peaks indicating the maximum and minimum velocities of the cardiac cycle. In this second method, we recovered the pulsatility fraction Pf by measuring the location of these distribution peaks with a RMSE of 5.2%. We evaluated the impact of the MB localization precision σ on our ability to retrieve the bimodal signature of a pulsatile flow. Together, our results demonstrate that pulsatility can be retrieved from ULM acquisitions at kilohertz frame rate and that the estimation of the pulsatility fraction improves with localization precision.@en

Ultrasound imaging is one of the most widely used modalities in clinical practice, revealing human prenatal development but also arterial function in the adult brain. Ultrasound waves travel deep within soft biological tissues and provide information about the motion and mechanical properties of internal organs. A drawback of ultrasound imaging is its limited ability to detect molecular targets due to a lack of cell-type specific acoustic contrast. To date, this limitation has been addressed by targeting synthetic ultrasound contrast agents to molecular targets. This molecular ultrasound imaging approach has proved to be successful but is restricted to the vascular space. Here, we introduce the nascent field of biomolecular ultrasound imaging, a molecular imaging approach that relies on genetically encoded acoustic biomolecules to interface ultrasound waves with cellular processes. We review ultrasound imaging applications bridging wave physics and chemical engineering with potential for deep brain imaging.

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Visualizing biomolecular and cellular processes inside intact living organisms is a major goal of chemical biology. However, existing molecular biosensors, based primarily on fluorescent emission, have limited utility in this context due to the scattering of light by tissue. In contrast, ultrasound can easily image deep tissue with high spatiotemporal resolution, but lacks the biosensors needed to connect its contrast to the activity of specific biomolecules such as enzymes. To overcome this limitation, we introduce the first genetically encodable acoustic biosensors—molecules that ‘light up’ in ultrasound imaging in response to protease activity. These biosensors are based on a unique class of air-filled protein nanostructures called gas vesicles, which we engineered to produce nonlinear ultrasound signals in response to the activity of three different protease enzymes. We demonstrate the ability of these biosensors to be imaged in vitro, inside engineered probiotic bacteria, and in vivo in the mouse gastrointestinal tract. [Figure not available: see fulltext.].

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An amendment to this paper has been published and can be accessed via a link at the top of the paper.

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Atherosclerosis is associated with the formation of microvessels in the arterial wall, referred to as vasa vasorum (VV). VV imaging may constitute a new intravascular ultrasound (IVUS) metric of coronary artery plaque vulnerability. The potential of nonlinear contrast IVUS to detect VV in vivo was demonstrated using a prototype transducer with dual-peak frequency response. In this study, we report the feasibility of pulse-inversion ultraharmonic IVUS contrast imaging and6 chirp reversal contrast IVUS imaging. Their performance is compared for VV detection. Both sequences operate with limited transducer bandwidths (<60%), and are therefore implementable on clinical IVUS catheters.

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