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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]

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]

Traditional implanted drug delivery systems cannot easily change their release profile in real time to respond to physiological changes. Here we present a microfluidic aqueous two‐phase system to generate microcapsules that can release drugs on demand as triggered by focused ultrasound (FUS). The biphasic microcapsules are made of hydrogels with an outer phase of mixed molecular weight (MW) poly(ethylene glycol) diacrylate that mitigates premature payload release and an inner phase of high MW dextran with payload that breaks down in response to FUS. Compound release from microcapsules could be triggered as desired; 0.4 μg of payload was released across 16 on‐demand steps over days. We detected broadband acoustic signals amidst low heating, suggesting inertial cavitation as a key mechanism for payload release. Overall, FUS‐responsive microcapsules are a biocompatible and wirelessly triggerable structure for on‐demand drug delivery over days to weeks. [ABSTRACT FROM AUTHOR]

Traditional implanted drug delivery systems cannot easily change their release profile in real time to respond to physiological changes. Here we present a microfluidic aqueous two‐phase system to generate microcapsules that can release drugs on demand as triggered by focused ultrasound (FUS). The biphasic microcapsules are made of hydrogels with an outer phase of mixed molecular weight (MW) poly(ethylene glycol) diacrylate that mitigates premature payload release and an inner phase of high MW dextran with payload that breaks down in response to FUS. Compound release from microcapsules could be triggered as desired; 0.4 μg of payload was released across 16 on‐demand steps over days. We detected broadband acoustic signals amidst low heating, suggesting inertial cavitation as a key mechanism for payload release. Overall, FUS‐responsive microcapsules are a biocompatible and wirelessly triggerable structure for on‐demand drug delivery over days to weeks. [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]

Traditional implanted drug delivery systems cannot easily change their release profile in real time to respond to physiological changes. Here we present a microfluidic aqueous two‐phase system to generate microcapsules that can release drugs on demand as triggered by focused ultrasound (FUS). The biphasic microcapsules are made of hydrogels with an outer phase of mixed molecular weight (MW) poly(ethylene glycol) diacrylate that mitigates premature payload release and an inner phase of high MW dextran with payload that breaks down in response to FUS. Compound release from microcapsules could be triggered as desired; 0.4 μg of payload was released across 16 on‐demand steps over days. We detected broadband acoustic signals amidst low heating, suggesting inertial cavitation as a key mechanism for payload release. Overall, FUS‐responsive microcapsules are a biocompatible and wirelessly triggerable structure for on‐demand drug delivery over days to weeks. [ABSTRACT FROM AUTHOR]