Light-sheet fluorescence microscopy has revolutionized biology by visualizing dynamic cellular processes in three dimensions. However, light scattering in thick tissue and photobleaching of fluorescent reporters limit this method to studying thin or translucent specimens. In this study, we applied nondiffractive ultrasound beams in conjunction with a cross-amplitude modulation sequence and nonlinear acoustic reporters to enable fast and volumetric imaging of targeted biological functions. We reported volumetric imaging of tumor gene expression at the cubic centimeter scale using genetically encoded gas vesicles and localization microscopy of cerebral capillary networks using intravascular microbubble contrast agents. Nonlinear sound-sheet microscopy provides a ~64× acceleration in imaging speed, ~35× increase in imaged volume, and ~4× increase in classical imaging resolution compared with the state of the art in biomolecular ultrasound.

Super-resolution ultrasound (SRUS) has evolved significantly with the advent of Ultrasound Localization Microscopy (ULM). This technique enables sub-wavelength resolution imaging using microbubble contrast agents. Initially confined to 2D imaging, ULM has progressed towards volumetric approaches, allowing for comprehensive three-dimensional visualization of microvascular networks. This review explores the technological advancements and challenges associated with volumetric ULM, focusing on key aspects such as transducer design, acquisition speed, data processing algorithms, or integration into clinical practice. We discuss the limitations of traditional 2D ULM, including dependency on precise imaging plane selection and compromised resolution in microvasculature quantification. In contrast, volumetric ULM offers enhanced spatial resolution and allowed motion correction in all direction, promising transformative insights into microvascular pathophysiology. By examining current research and future directions, this review highlights the potential of volumetric ULM to contribute significantly to diagnostic across various medical conditions, including cancers, arteriosclerosis, strokes, diabetes, and neurodegenerative diseases.

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.

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.

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.