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Detection of inertial and stable cavitation is important for guiding high-intensity focused ultrasound (HIFU). Acoustic transducers can passively detect broadband noise from inertial cavitation and the scattering of HIFU harmonics from stable cavitation bubbles. Conventional approaches to cavitation noise diagnostics typically involve computing the Fourier transform of the time-domain noise signal, applying a custom comb filter to isolate the frequency components of interest, followed by an inverse Fourier transform. We present an alternative technique based on singular value decomposition (SVD) that efficiently separates the broadband emissions and HIFU harmonics. Spatiotemporally resolved cavitation detection was achieved using a 128-element, 5-MHz linear-array ultrasound imaging system operating in the receive mode at 15 frames/s. A 1.1-MHz transducer delivered HIFU to tissue-mimicking phantoms and excised liver tissue for a duration of 5 s. Beamformed radio frequency signals corresponding to each scan line in a frame were assembled into a matrix, and SVD was performed. Spectra of the singular vectors obtained from a tissue-mimicking gel phantom were analyzed by computing the peak ratio (${R}$), defined as the ratio of the peak of its fifth-order polynomial fit and the maximum spectral peak. Singular vectors that produced an ${R} < 0.048$ were classified as those representing stable cavitation, i.e., predominantly containing harmonics of HIFU. The projection of data onto this singular base reproduced stable cavitation signals. Similarly, singular vectors that produced an ${R} >0.2$ were classified as those predominantly containing broadband noise associated with inertial cavitation. These singular vectors were used to isolate the inertial cavitation signal. The ${R}$ -value thresholds determined using gel data were then employed to analyze cavitation data obtained from bovine liver ex vivo. The SVD-based method faithfully reproduced the structural details in the spatiotemporal cavitation maps produced using the more cumbersome comb-filter approach with a maximum root-mean-squared error of 10%. [ABSTRACT FROM AUTHOR]

Simulation tools for photoacoustic wave propagation have played a key role in advancing photoacoustic imaging by providing quantitative and qualitative insights into parameters affecting image quality. Classical methods for numerically solving the photoacoustic wave equation rely on a fine discretization of space and can become computationally expensive for large computational grids. In this work, we applied Fourier Neural Operator (FNO) networks as a fast data-driven deep learning method for solving the 2D photoacoustic wave equation in a homogeneous medium. Comparisons between the FNO network and pseudo-spectral time domain approach were made for the forward and adjoint simulations. Results demonstrate that the FNO network generated comparable simulations with small errors and was orders of magnitude faster than the pseudo-spectral time domain methods (~26× faster on a 64 × 64 computational grid and ~15× faster on a 128 × 128 computational grid). Moreover, the FNO network was generalizable to the unseen out-of-domain test set with a root-mean-square error of 9.5 × 10−3 in Shepp–Logan, 1.5 × 10−2 in synthetic vasculature, 1.1 × 10−2 in tumor and 1.9 × 10−2 in Mason-M phantoms on a 64 × 64 computational grid and a root mean squared of 6.9 ± 5.5 × 10−3 in the AWA2 dataset on a 128 × 128 computational grid. [ABSTRACT FROM AUTHOR]

The Oak Ridge National Laboratory Spallation Neutron Source employs a high-energy pulsed proton beam incident on a mercury target to generate short bursts of neutrons. Absorption of the proton beam produces rapid heating of the mercury, resulting in the formation of acoustic shock waves and the nucleation of cavitation bubbles. The subsequent collapse of these cavitation bubbles promote erosion of the steel target walls. Preliminary measurements using two passive cavitation detectors (megahertz-frequency focused and unfocused piezoelectric transducers) installed in a mercury test target to monitor cavitation generated by proton beams with charges ranging from 0.041 to 4.1 μC will be reported on. Cavitation was initially detected for a beam charge of 0.082 μC by the presence of an acoustic emission approximately 250 μs after arrival of the incident proton beam. This emission was consistent with an inertial cavitation collapse of a bubble with an estimated maximum bubble radius of 0.19 mm, based on collapse time. The peak pressure in the mercury for the initiation of cavitation was predicted to be 0.6 MPa. For a beam charge of 0.41 μC and higher, the lifetimes of the bubbles exceeded the reverberation time of the chamber (∼300 μs), and distinct windows of cavitation activity were detected, a phenomenon that likely resulted from the interaction of the reverberation in the chamber and the cavitation bubbles. [ABSTRACT FROM AUTHOR]

A method for producing a prescribed acoustic pressure field from a piezoelectric array was investigated. The array consisted of 170 elements placed on the inner surface of a 15 cm radius spherical cap. Each element was independently driven by using individual pulsers each capable of generating 1.2 kV. Acoustic field customization was achieved by independently controlling the time when each element was excited. The set of time delays necessary to produce a particular acoustic field was determined by using an optimization scheme. The acoustic field at the focal plane was simulated by using the angular spectrum method, and the optimization searched for the time delays that minimized the least squared difference between the magnitudes of the simulated and desired pressure fields. The acoustic field was shaped in two different ways: the -6 dB focal width was increased to different desired widths and the ring-shaped pressure distributions of various prescribed diameters were produced. For both cases, the set of delays resulting from the respective optimization schemes were confirmed to yield the desired pressure distributions by using simulations and measurements. The simulations, however, predicted peak positive pressures roughly half those obtained from the measurements, which was attributed to the exclusion of nonlinearity in the simulations. [ABSTRACT FROM AUTHOR]

Measurements are presented of acoustic emissions from cavitation collapses on the surface of a synthetic kidney stone in response to shock waves (SWs) from an electrohydraulic lithotripter. A fiber optic probe hydrophone was used for pressure measurements, and passive cavitation detection was used to identify acoustic emissions from bubble collapse. At a lithotripter charging voltage of 20 kV, the focused SW incident on the stone surface resulted in a peak pressure of 43±6 MPa compared to 23±4 MPa in the free field. The focused SW incident upon the stone appeared to be enhanced due to the acoustic emissions from the forced cavitation collapse of the preexisting bubbles. The peak pressure of the acoustic emission from a bubble collapse was 34±15 MPa, that is, the same magnitude as the SWs incident on the stone. These data indicate that stresses induced by focused SWs and cavitation collapses are similar in magnitude thus likely play a similar role in stone fragmentation. [ABSTRACT FROM AUTHOR]

Fluorescence and photoacoustic imaging techniques offer valuable insights into cell- and tissue-level processes. However, these optical imaging modalities are limited by scattering and absorption in tissue, resulting in the low-depth penetration of imaging. Contrast-enhanced imaging in the near-infrared window improves imaging penetration by taking advantage of reduced autofluorescence and scattering effects. Current contrast agents for fluorescence and photoacoustic imaging face several limitations from photostability and targeting specificity, highlighting the need for a novel imaging probe development. This review covers a broad range of near-infrared fluorescent and photoacoustic contrast agents, including organic dyes, polymers, and metallic nanostructures, focusing on their optical properties and applications in cellular and animal imaging. Similarly, we explore encapsulation and functionalization technologies toward building targeted, nanoscale imaging probes. Bioimaging applications such as angiography, tumor imaging, and the tracking of specific cell types are discussed. This review sheds light on recent advancements in fluorescent and photoacoustic nanoprobes in the near-infrared window. It serves as a valuable resource for researchers working in fields of biomedical imaging and nanotechnology, facilitating the development of innovative nanoprobes for improved diagnostic approaches in preclinical healthcare. [ABSTRACT FROM AUTHOR]

In this study, we investigated the performance of four deep learning frameworks of U-Net, U-NeXt, DeepLabV3+, and ConResNet in multi-class pixel-based segmentation of the extraocular muscles (EOMs) from coronal MRI. Performances of the four models were evaluated and compared with the standard F-measure-based metrics of intersection over union (IoU) and Dice, where the U-Net achieved the highest overall IoU and Dice scores of 0.77 and 0.85, respectively. Centroid distance offset between identified and ground truth EOM centroids was measured where U-Net and DeepLabV3+ achieved low offsets (p > 0.05) of 0.33 mm and 0.35 mm, respectively. Our results also demonstrated that segmentation accuracy varies in spatially different image planes. This study systematically compared factors that impact the variability of segmentation and morphometric accuracy of the deep learning models when applied to segmenting EOMs from MRI. [ABSTRACT FROM AUTHOR]

The subharmonic threshold for ultrasound contrast agents has been defined as a 20–25 dB difference between the fundamental and subharmonic (2/1) spectral components of the backscatter signal. However, this Fourier-based criterion assumes a linear time-invariant signal. A more appropriate criterion for short cycle and frequency-modulated waveforms is proposed with an adaptive signal-processing approach based on the empirical mode decomposition (EMD) method. The signal is decomposed into an orthogonal basis known as intrinsic mode functions (IMFs) and a subharmonic threshold is defined with respect to the energy ratio of the subharmonic IMF component to that of the incident signal. The method is applied to backscatter data acquired from two polymer-shelled contrast agents, Philips (#38, mean diameter 2.0 \mu \textm ) and Point Biomedical (#12027, mean diameter 3.9 \mu \textm ). The acoustic backscatter signals are investigated for a single contrast agent subjected to monofrequency (20 MHz, 20 cycles) and chirp (15–25 MHz, 20 cycles) forcing for incident pressures ranging from 0.5 to 2.4 MPa. In comparison to the spectral peak difference (20 dB) criterion, the EMD method is more sensitive in determining subharmonic signals. [ABSTRACT FROM PUBLISHER]

We investigate the role of secondary shock waves (SSWs) generated by cavitation in lithotripsy. Acoustic pressure was measured with a fiber optic probe hydrophone and cavitation using a dual passive cavitation detector (PCD) consisting of two confocal transducers. An artificial stone (∼7 mm diameter and ∼9 mm length) was placed at the focus of an electrohydraulic lithotripter. The fiber was inserted through a hole drilled through the stone so that the tip was at the proximal surface. SSWs were identified by matching the time of arrival to that of the inertial collapse signature acquired by the PCD. Measurements of SSWs were obtained for 50% of SWs fired at 20 kV and 1 Hz. The peak positive pressure for the SSW was p+=33.7±14.8 MPa, which was comparable to the pressure induced by the incident SW (p+=42.6±6 MPa). The peak pressure in water was p+=23.2±4.4 MPa. The PCD also recorded acoustic emissions from forced collapse of pre-existing bubbles caused by the incident SW. We propose that both the reflection from the semi-rigid stone boundary and SSW from the forced collapse contribute to the observed increase in the peak pressure of the incident SW in presence of a stone. [Work supported by NIH.] [ABSTRACT FROM AUTHOR]

We report measurements of a number of different shock wave therapy (SWT) devices. Two devices were electrohydraulic (EH): one had a large shock source (HMT Ossatron) and the other was a small hand-held source (HMT Evotron). The other device was a pneumatically driven device (EMS Swiss Dolorclast) and two different hand pieces were measured, one with an ‘‘unfocused’’ head and the other with a ‘‘focused’’ head. We found that the EH sources generated focused shock waves with a positive phase about 1 microsec long and peak pressure around 40 MPa, however, the acoustic output of the HMT Evotron appeared to be independent of the power setting of the machine. For the pneumatic source the duration of the positive phase was greater than 4 microsec and the peak pressure about 7 MPa. There was no clear shock front present and the waveform had a complex tail structure that was dependent on the power setting of the machine. We found that the focused hand-piece did not generate a focused acoustic field. The results are compared to reports of measurements from electromagnetic SWT devices. We contrast measurements made with different hydrophone systems: fiber-optic probe hydrophone, PVDF membrane hydrophone and PVDF bullet-shaped hydrophone. [ABSTRACT FROM AUTHOR]

Photoacoustic tomography (PAT) is a non-ionizing imaging modality capable of acquiring high contrast and resolution images of optical absorption at depths greater than traditional optical imaging techniques. Practical considerations with instrumentation and geometry limit the number of available acoustic sensors and their "view" of the imaging target, which result in image reconstruction artifacts degrading image quality. Iterative reconstruction methods can be used to reduce artifacts but are computationally expensive. In this work, we propose a novel deep learning approach termed pixel-wise deep learning (Pixel-DL) that first employs pixel-wise interpolation governed by the physics of photoacoustic wave propagation and then uses a convolution neural network to reconstruct an image. Simulated photoacoustic data from synthetic, mouse-brain, lung, and fundus vasculature phantoms were used for training and testing. Results demonstrated that Pixel-DL achieved comparable or better performance to iterative methods and consistently outperformed other CNN-based approaches for correcting artifacts. Pixel-DL is a computationally efficient approach that enables for real-time PAT rendering and improved image reconstruction quality for limited-view and sparse PAT. [ABSTRACT FROM AUTHOR]