DNA-Based Near-Infrared Voltage Sensors.

Indocyanine green (ICG) is an FDA approved dye widely used for fluorescence imaging in research, surgical navigation, and medical diagnostics. However, ICG has a few drawbacks, such as concentration-dependent aggregation and absorbance, nonspecific cellular targeting, and rapid photobleaching. Here, we report a novel DNA-based nanosensor platform that utilizes monomers of ICG and cholesterol. Using DNA origami, we can attach ICG to a DNA structure, maintaining its concentration, preserving its near-infrared (NIR) absorbance, and allowing attachment of targeting moieties. We characterized the nanosensors' absorbance, stability in blood, and voltage sensing in vitro. This study presents a novel DNA-based ICG nanosensor platform for cellular voltage sensing for future in vivo applications.

NIR-II Nanoprobes: A Review of Components-Based Approaches to Next-Generation Bioimaging Probes.

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.

Deep-Learning-Based Segmentation of Extraocular Muscles from Magnetic Resonance Images.

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.

Relationships between surrogate measures of mechanical and psychophysiological load, patellar tendon adaptations, and neuromuscular performance in NCAA division I men's volleyball athletes.

Introduction: Patellar tendon adaptations occur in response to mechanical load. Appropriate loading is necessary to elicit positive adaptations with increased risk of injury and decreased performance likely if loading exceeds the capacity of the tendon. The aim of the current study was to examine intra-individual associations between workloads and patellar tendon properties and neuromuscular performance in collegiate volleyball athletes. Methods: National Collegiate Athletics Association Division I men's volleyball athletes (n = 16, age: 20.33 ± 1.15 years, height: 193.50 ± 6.50 cm, body mass: 84.32 ± 7.99 kg, bodyfat%: 13.18 ± 4.72%) competing across 9 weeks of in-season competition participated. Daily measurements of external workloads (i.e., jump count) and internal workloads [i.e., session rating of perceived exertion (sRPE)] were recorded. Weekly measurements included neuromuscular performance assessments (i.e., countermovement jump, drop jump), and ultrasound images of the patellar tendon to evaluate structural adaptations. Repeated measures correlations (r-rm) assessed intra-individual associations among performance and patellar tendon metrics. Results: Workload measures exhibited significant negative small to moderate (r-rm =-0.26-0.31) associations with neuromuscular performance, negative (r-rm = -0.21-0.30), and positive (r-rm = 0.20-0.32) small to moderate associations with patellar tendon properties. Discussion: Monitoring change in tendon composition and performance adaptations alongside workloads may inform evidence-based frameworks toward managing and reducing the risk of the development of patellar tendinopathy in collegiate men's volleyball athletes.

Fast iterative reconstruction for photoacoustic tomography using learned physical model: Theoretical validation.

Iterative reconstruction has demonstrated superior performance in medical imaging under compressed, sparse, and limited-view sensing scenarios. However, iterative reconstruction algorithms are slow to converge and rely heavily on hand-crafted parameters to achieve good performance. Many iterations are usually required to reconstruct a high-quality image, which is computationally expensive due to repeated evaluations of the physical model. While learned iterative reconstruction approaches such as model-based learning (MBLr) can reduce the number of iterations through convolutional neural networks, it still requires repeated evaluations of the physical models at each iteration. Therefore, the goal of this study is to develop a Fast Iterative Reconstruction (FIRe) algorithm that incorporates a learned physical model into the learned iterative reconstruction scheme to further reduce the reconstruction time while maintaining robust reconstruction performance. We also propose an efficient training scheme for FIRe, which releases the enormous memory footprint required by learned iterative reconstruction methods through the concept of recursive training. The results of our proposed method demonstrate comparable reconstruction performance to learned iterative reconstruction methods with a 9x reduction in computation time and a 620x reduction in computation time compared to variational reconstruction.

Size-tunable ICG-based contrast agent platform for targeted near-infrared photoacoustic imaging.

Near-infrared photoacoustic imaging (NIR-PAI) combines the advantages of optical and ultrasound imaging to provide anatomical and functional information of tissues with high resolution. Although NIR-PAI is promising, its widespread use is hindered by the limited availability of NIR contrast agents. J-aggregates (JA) made of indocyanine green dye (ICG) represents an attractive class of biocompatible contrast agents for PAI. Here, we present a facile synthesis method that combines ICG and ICG-azide dyes for producing contrast agents with tunable size down to 230 nm and direct functionalization with targeting moieties. The ICG-JA platform has a detectable PA signal in vitro that is two times stronger than whole blood and high photostability. The targeting ability of ICG-JA was measured in vitro using HeLa cells. The ICG-JA platform was then injected into mice and in vivo NIR-PAI showed enhanced visualization of liver and spleen for 90 min post-injection with a contrast-to-noise ratio of 2.42.

Ultrasound-Responsive Aqueous Two-Phase Microcapsules for On-Demand Drug Release.

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.

Toward a wearable monitor of local muscle fatigue during electrical muscle stimulation using tissue Doppler imaging.

Electrical muscle stimulation (EMS) is widely used in rehabilitation and athletic training to generate involuntary muscle contractions. However, EMS leads to rapid muscle fatigue, limiting the force a muscle can produce during prolonged use. Currently available methods to monitor localized muscle fatigue and recovery are generally not compatible with EMS. The purpose of this study was to examine whether Doppler ultrasound imaging can assess changes in stimulated muscle twitches that are related to muscle fatigue from electrical stimulation. We stimulated five isometric muscle twitches in the medial and lateral gastrocnemius of 13 healthy subjects before and after a fatiguing EMS protocol. Tissue Doppler imaging of the medial gastrocnemius recorded muscle tissue velocities during each twitch. Features of the average muscle tissue velocity waveforms changed immediately after the fatiguing stimulation protocol (peak velocity: -38%, p = .022; time-to-zero velocity: +8%, p = .050). As the fatigued muscle recovered, the features of the average tissue velocity waveforms showed a return towards their baseline values similar to that of the normalized ankle torque. We also found that features of the average tissue velocity waveform could significantly predict the ankle twitch torque for each participant (R2 = 0.255-0.849, p < .001). Our results provide evidence that Doppler ultrasound imaging can detect changes in muscle tissue during isometric muscle twitch that are related to muscle fatigue, fatigue recovery, and the generated joint torque. Tissue Doppler imaging may be a feasible method to monitor localized muscle fatigue during EMS in a wearable device.

Comparing Deep Learning Frameworks for Photoacoustic Tomography Image Reconstruction.

Conventional reconstruction methods for photoacoustic images are not suitable for the scenario of sparse sensing and geometrical limitation. To overcome these challenges and enhance the quality of reconstruction, several learning-based methods have recently been introduced for photoacoustic tomography reconstruction. The goal of this study is to compare and systematically evaluate the recently proposed learning-based methods and modified networks for photoacoustic image reconstruction. Specifically, learning-based post-processing methods and model-based learned iterative reconstruction methods are investigated. In addition to comparing the differences inherently brought by the models, we also study the impact of different inputs on the reconstruction effect. Our results demonstrate that the reconstruction performance mainly stems from the effective amount of information carried by the input. The inherent difference of the models based on the learning-based post-processing method does not provide a significant difference in photoacoustic image reconstruction. Furthermore, the results indicate that the model-based learned iterative reconstruction method outperforms all other learning-based post-processing methods in terms of generalizability and robustness.

Limited-View and Sparse Photoacoustic Tomography for Neuroimaging with Deep Learning.

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.

Fully Dense UNet for 2-D Sparse Photoacoustic Tomography Artifact Removal.

Photoacoustic imaging is an emerging imaging modality that is based upon the photoacoustic effect. In photoacoustic tomography (PAT), the induced acoustic pressure waves are measured by an array of detectors and used to reconstruct an image of the initial pressure distribution. A common challenge faced in PAT is that the measured acoustic waves can only be sparsely sampled. Reconstructing sparsely sampled data using standard methods results in severe artifacts that obscure information within the image. We propose a modified convolutional neural network (CNN) architecture termed fully dense UNet (FD-UNet) for removing artifacts from two-dimensional PAT images reconstructed from sparse data and compare the proposed CNN with the standard UNet in terms of reconstructed image quality.

SVD-Based Separation of Stable and Inertial Cavitation Signals Applied to Passive Cavitation Mapping During HIFU.

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 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 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%.

A frequency-domain non-contact photoacoustic microscope based on an adaptive interferometer.

A frequency-domain, non-contact approach to photoacoustic microscopy (PAM) that employs amplitude-modulated (0.1-1 MHz) laser for excitation (638-nm pump) in conjunction with a 2-wave mixing interferometer (532-nm probe) for non-contact detection of photoacoustic waves at the specimen surface is presented. A lock-in amplifier is employed to detect the photoacoustic signal. Illustrative images of tissue-mimicking phantoms, red-blood cells and retinal vasculature are presented. Single-frequency modulation of the pump beam directly provides an image that is equivalent to the 2-dimensional projection of the image volume. Targets located superficially produce phase modulations in the surface-reflected probe beam due to surface vibrations as well as direct intensity modulation in the backscattered probe light due to local changes in pressure and/or temperature. In comparison, the observed modulations in the probe beam due to targets located deeper in the specimen, for example, beyond the ballistic photon regime, predominantly consist of phase modulation.

Subharmonic Response of Polymer Contrast Agents Based on the Empirical Mode Decomposition.

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 [Formula: see text]) and Point Biomedical (#12027, mean diameter 3.9 [Formula: see text]). 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.

An implantable compound-releasing capsule triggered on demand by ultrasound.

Implantable devices have a large potential to improve human health, but they are often made of biofouling materials that necessitate special coatings, rely on electrical connections for external communication, and require a continuous power source. This paper demonstrates an alternative platform, which we call iTAG (implantable thermally actuated gel), where an implanted capsule can be wirelessly controlled by ultrasound to trigger the release of compounds. We constructed a millimeter-sized capsule containing a co-polymer gel (NiPAAm-co-AAm) that contracts above body temperature (i.e. at 45 °C) to release compounds through an opening. This gel-containing capsule is biocompatible and free of toxic electronic or battery components. An ultrasound hardware, with a focused ultrasound (FUS) transducer and a co-axial A-mode imaging transducer, was used to image the capsule (to monitor in real time its position, temperature, and effectiveness of dose delivery), as well as to trigger a rapid local rise in temperature, contraction of gel, and release of compounds in vitro and in vivo. The combination of this gel-based capsule and compact ultrasound hardware can serve as a platform for triggering local release of compounds, including potentially in deep tissue, to achieve tailored personalized therapy.

Coherence-Weighted Synthetic Focusing Applied to Photoacoustic Imaging Using a High-Frequency Annular-Array Transducer.

This paper presents an adaptive synthetic-focusing scheme that, when applied to photoacoustic (PA) data acquired using an annular array, improves focusing across a greater imaging depth and enhances spatial resolution. The imaging system was based on a 40-MHz, 5-element, annular-array transducer with a focal length of 12 mm and an 800-µm diameter hole through its central element to facilitate coaxial delivery of 532-nm laser. The transducer was raster-scanned to facilitate 3D acquisition of co-registered ultrasound and PA image data. Three synthetic-focusing schemes were compared for obtaining PA A-lines for each scan location: delay-and-sum (DAS), DAS weighted with a coherence factor (DAS + CF), and DAS weighted with a sign-coherence factor (DAS + SCF). Bench-top experiments that used an 80-µm hair were performed to assess the enhancement provided by the two coherence-based schemes. Both coherence-based schemes increased the signal-to-noise ratio by approximately 10 dB. When processed using the DAS-only scheme, the lateral dimension of the hair in a PA image with 20 dB dynamic range was between 300 µm and 1 mm for imaging depth ranging from 8 to 20 mm. In comparison, the DAS + CF scheme resulted in a lateral dimension of 200 to 450 µm over the same range. The DAS + SCF synthetic focusing further improved the smallest-resolvable dimension, which was between 150 and 400 µm over the same range of imaging depth. When used on PA data obtained from a 12-day-old mouse embryo, the DAS + SCF processing improved visualization of neurovasculature.

Ultrasound characterization of interface oscillation as a proxy for ventriculoperitoneal shunt function.

Hydrocephalus, where cerebrospinal fluid (CSF) production rate is greater than reabsorption rate, leads to impaired neurological function if left untreated. Ventriculoperitoneal shunts (VPS) are implanted in the brain ventricles to route CSF. VPS systems have a high failure rate, and failure symptoms resemble symptoms of common maladies. The current gold standard for shunt diagnosis, surgical intervention, poses high risk and requires an expensive procedure for patients. Current non-invasive methods lack proper insight to assist physicians. We propose a noninvasive method of characterizing the oscillation of the shunt's pressure-relief valve to assist physicians in shunt diagnosis. Brightness-mode and motion-mode ultrasound images can be used to determine fluid flow. Blockage in the system could be detected by observing the phase change of the ultrasound signal in different flow cases with or without perturbation. Future testing and implementation can allow for the use of this method in localizing and identifying the modality of failure.

Correlation of rupture dynamics to the nonlinear backscatter response from polymer-shelled ultrasound contrast agents.

Polymer-shelled ultrasound contrast agents (UCAs) may expel their encapsulated gas subject to ultrasound-induced shell buckling or rupture. Nonlinear oscillations of this gas bubble can produce a subharmonic component in the ultrasound backscatter. This study investigated the relationship between this gas-release mechanism and shell-thickness-to-radius ratios (STRRs) of polymer-shelled UCAs. Three types of polylactide-shelled UCAs with STRRs of 7.5, 40, and 100 nm/µm were studied. Each UCA population had a nominal mean diameter of 2 µm. UCAs were subjected to increasing static overpressure ranging from 2 to 330 kPa over a duration of 2 h in a custom-designed test chamber while being imaged using a 200× magnification video microscope at a frame rate of 5 frames/s. Digitized video images were binarized and processed to obtain the cross-sectional area of individual UCAs. Integration of the normalized cross-sectional area over normalized time, defined as buckling factor (Bf), provided a dimensionless parameter for quantifying and comparing the degree of pre-rupture buckling exhibited by the UCAs of different STRRs in response to overpressure. The UCAs with an STRR of 7.5 nm/µm exhibited a distinct shell-buckling phase before shell rupture (Bf < 1), whereas the UCAs with higher STRRs (40 and 100 nm/µm) did not undergo significant prerupture buckling (Bf ≈ 1). The difference in the overpressure response was correlated with the subharmonic response produced by these UCAs. When excited using 20-MHz ultrasound, individual UCAs (N = 3000) in populations that did not exhibit a buckling phase produced a subharmonic response that was an order of magnitude greater than the UCA population with a prominent pre-rupture buckling phase. These results indicate the mechanism of gas expulsion from these UCAs might be a relevant factor in determining the level of subharmonic response in response to high-frequency ultrasound.

High-frequency annular array with coaxial illumination for dual-modality ultrasonic and photoacoustic imaging.

This paper presents a combined ultrasound and photoacoustic (PA) imaging (PAI) system used to obtain high-quality, co-registered images of mouse-embryo anatomy and vasculature. High-frequency ultrasound (HFU, >20 MHz) is utilized to obtain high-resolution anatomical images of small animals while PAI provides high-contrast images of the vascular network. The imaging system is based on a 40 MHz, 5-element, 6 mm aperture annular-array transducer with a 800 µm diameter hole through its central element. The transducer was integrated in a cage-plate assembly allowing for a collimated laser beam to pass through the hole so that the optical and acoustic beams were collinear. The assembly was mounted on a two-axis, motorized stage to enable the simultaneous acquisition of co-registered HFU and PA volumetric data. Data were collected from all five elements in receive and a synthetic-focusing algorithm was applied in post-processing to beamform the data and increase the spatial resolution and depth-of-field (DOF) of the HFU and PA images. Phantom measurements showed that the system could achieve high-resolution images (down to 90 µm for HFU and 150 µm for PAI) and a large DOF of >8 mm. Volume renderings of a mouse embryo showed that the scanner allowed for visualizing morphologically precise anatomy of the entire embryo along with corresponding co-registered vasculature. Major head vessels, such as the superior sagittal sinus or rostral vein, were clearly identified as well as limb bud vasculature.

Influence of shell properties on high-frequency ultrasound imaging and drug delivery using polymer-shelled microbubbles.

This two-part study investigated shell rupture of ultrasound contrast agents (UCAs) under static overpressure conditions and the subharmonic component from UCAs subjected to 20-MHz tonebursts. Five different polylactide-shelled UCAs with shell-thickness-to-radius ratios (STRRs) of 7.5, 30, 40, 65, and 100 nm/¿m were subjected to static overpressure in a glycerol-filled test chamber. A video microscope imaged the UCAs as pressure varied from 2 to 330 kPa over 90 min. Images were postprocessed to obtain the pressure threshold for rupture and the diameter of individual microbubbles. Backscatter from individual UCAs was investigated by flowing a dilute UCA solution through a wall-less flow phantom placed at the geometric focus of a 20-MHz transducer. UCAs were subjected to 10- and 20-cycle tonebursts of acoustic pressures ranging from 0.3 to 2.3 MPa. A method based on singular-value decomposition (SVD) was employed to obtain a cumulative subharmonic score (SHS). Different UCA types exhibited distinctly different rupture thresholds that were linearly related to their STRR, but uncorrelated with UCA size. The rupture threshold for the UCAs with an STRR = 100 nm/µm was more than 4 times greater than the UCAs with an STRR = 7.5 nm/µm. The polymer-shelled UCAs produced substantial subharmonic response but the subharmonic response to 20- MHz excitation did not correlate with STRRs or UCA-rupture pressures. The 20-cycle excitation resulted in an SHS that was 2 to 3 times that of UCAs excited with 10-cycle tonebursts.