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

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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The resolution of an imaging system is usually determined by the width of its point spread function and is measured using the Rayleigh criterion. For most system, it is in the order of the imaging wavelength. However, super resolution techniques such as localization microscopy in optical and ultrasound imaging can resolve features an order of magnitude finer than the wavelength. The classical description of spatial resolution no longer applies and new methods need to be developed. In optical localization microscopy, the Fourier Ring Correlation has showed to be an effective and practical way to estimate spatial resolution for Single Molecule Localization Microscopy data. In this work, we wish to investigate how this tool can provide a direct and universal estimation of spatial resolution in Ultrasound Localization Microscopy. Moreover, we discuss the concept of spatial sampling in Ultrasound Localization Microscopy and demonstrate how the Nyquist criterion for sampling drives the spatial/temporal resolution tradeoff. We measured spatial resolution on five different datasets over rodent's brain, kidney and tumor finding values between 11~\mu \text{m} and 34~\mu \text{m} for precision of localization between 11~\mu \text{m} and 15~\mu \text{m}. Eventually, we discuss from those in vivo datasets how spatial resolution in Ultrasound Localization Microscopy depends on both the localization precision and the total number of detected microbubbles. This study aims to offer a practical and theoretical framework for image resolution in Ultrasound Localization Microscopy.

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