A flexible optoacoustic blood 'stethoscope' for noninvasive multiparametric cardiovascular monitoring.

Quantitative and multiparametric blood analysis is of great clinical importance in cardiovascular disease diagnosis. Although there are various methods to extract blood information, they often require invasive procedures, lack continuity, involve bulky instruments, or have complicated testing procedures. Flexible sensors can realize on-skin assessment of several vital signals, but generally exhibit limited function to monitor blood characteristics. Here, we report a flexible optoacoustic blood 'stethoscope' for noninvasive, multiparametric, and continuous cardiovascular monitoring, without requiring complicated procedures. The optoacoustic blood 'stethoscope' features the light delivery elements to illuminate blood and the piezoelectric acoustic elements to capture light-induced acoustic waves. We show that the optoacoustic blood 'stethoscope' can adhere to the skin for continuous and non-invasive in-situ monitoring of multiple cardiovascular biomarkers, including hypoxia, intravascular exogenous agent concentration decay, and hemodynamics, which can be further visualized with a tailored 3D algorithm. Demonstrations on both in-vivo animal trials and human subjects highlight the optoacoustic blood 'stethoscope''s potential for cardiovascular disease diagnosis and prediction.

Adaptive enhancement of acoustic resolution photoacoustic microscopy imaging via deep CNN prior.

Acoustic resolution photoacoustic microscopy (AR-PAM) is a promising medical imaging modality that can be employed for deep bio-tissue imaging. However, its relatively low imaging resolution has greatly hindered its wide applications. Previous model-based or learning-based PAM enhancement algorithms either require design of complex handcrafted prior to achieve good performance or lack the interpretability and flexibility that can adapt to different degradation models. However, the degradation model of AR-PAM imaging is subject to both imaging depth and center frequency of ultrasound transducer, which varies in different imaging conditions and cannot be handled by a single neural network model. To address this limitation, an algorithm integrating both learning-based and model-based method is proposed here so that a single framework can deal with various distortion functions adaptively. The vasculature image statistics is implicitly learned by a deep convolutional neural network, which served as plug and play (PnP) prior. The trained network can be directly plugged into the model-based optimization framework for iterative AR-PAM image enhancement, which fitted for different degradation mechanisms. Based on physical model, the point spread function (PSF) kernels for various AR-PAM imaging situations are derived and used for the enhancement of simulation and in vivo AR-PAM images, which collectively proved the effectiveness of proposed method. Quantitatively, the PSNR and SSIM values have all achieve best performance with the proposed algorithm in all three simulation scenarios; The SNR and CNR values have also significantly raised from 6.34 and 5.79 to 35.37 and 29.66 respectively in an in vivo testing result with the proposed algorithm.

A Review of Emerging Electromagnetic-Acoustic Sensing Techniques for Healthcare Monitoring.

Conventional electromagnetic (EM) sensing techniques such as radar and LiDAR are widely used for remote sensing, vehicle applications, weather monitoring, and clinical monitoring. Acoustic techniques such as sonar and ultrasound sensors are also used for consumer applications, such as ranging and in vivo medical/healthcare applications. It has been of long-term interest to doctors and clinical practitioners to realize continuous healthcare monitoring in hospitals and/or homes. Physiological and biopotential signals in real-time serve as important health indicators to predict and prevent serious illness. Emerging electromagnetic-acoustic (EMA) sensing techniques synergistically combine the merits of EM sensing with acoustic imaging to achieve comprehensive detection of physiological and biopotential signals. Further, EMA enables complementary fusion sensing for challenging healthcare settings, such as real-world long-term monitoring of treatment effects at home or in remote environments. This article reviews various examples of EMA sensing instruments, including implementation, performance, and application from the perspectives of circuits to systems. The novel and significant applications to healthcare are discussed. Three types of EMA sensors are presented: (1) Chip-based radar sensors for health status monitoring, (2) Thermo-acoustic sensing instruments for biomedical applications, and (3) Photoacoustic (PA) sensing and imaging systems, including dedicated reconstruction algorithms were reviewed from time-domain, frequency-domain, time-reversal, and model-based solutions. The future of EMA techniques for continuous healthcare with enhanced accuracy supported by artificial intelligence (AI) is also presented.

Deep and Domain Transfer Learning Aided Photoacoustic Microscopy: Acoustic Resolution to Optical Resolution.

Acoustic resolution photoacoustic micros- copy (AR-PAM) can achieve deeper imaging depth in biological tissue, with the sacrifice of imaging resolution compared with optical resolution photoacoustic microscopy (OR-PAM). Here we aim to enhance the AR-PAM image quality towards OR-PAM image, which specifically includes the enhancement of imaging resolution, restoration of micro-vasculatures, and reduction of artifacts. To address this issue, a network (MultiResU-Net) is first trained as generative model with simulated AR-OR image pairs, which are synthesized with physical transducer model. Moderate enhancement results can already be obtained when applying this model to in vivo AR imaging data. Nevertheless, the perceptual quality is unsatisfactory due to domain shift. Further, domain transfer learning technique under generative adversarial network (GAN) framework is proposed to drive the enhanced image's manifold towards that of real OR image. In this way, perceptually convincing AR to OR enhancement result is obtained, which can also be supported by quantitative analysis. Peak Signal to Noise Ratio (PSNR) and Structural Similarity Index (SSIM) values are significantly increased from 14.74 dB to 19.01 dB and from 0.1974 to 0.2937, respectively, validating the improvement of reconstruction correctness and overall perceptual quality. The proposed algorithm has also been validated across different imaging depths with experiments conducted in both shallow and deep tissue. The above AR to OR domain transfer learning with GAN (AODTL-GAN) framework has enabled the enhancement target with limited amount of matched in vivo AR-OR imaging data.

Self-assembled semiconducting polymer based hybrid nanoagents for synergistic tumor treatment.

There is an impending need for the development of carrier-free nanosystems for single laser triggered activation of phototherapy, as such approach can overcome the drawbacks associated with irradiation by two distinct laser sources for avoiding prolonged treatment time and complex treatment protocols. Herein, we developed a self-assembled nanosystem (SCP-CS) consisting of a new semiconducting polymer (SCP) and encapsulated ultrasmall CuS (CS) nanoparticles. The SCP component displays remarkable near infrared (NIR) induced photothermal ability, enhanced reactive oxygen species (ROS) generation, and incredible photoacoustic (PA) signals upon activation by 808 nm laser for phototherapy mediated cancer ablation. The CuS component improves the PA imaging ability of SCP-CS, and also enhances photo-induced chemodynamic efficacy. Attributed to promoted single laser-triggered hyperthermia and enhanced ROS generation, the SCP-CS nanosystem shows effective intracellular uptake and intratumoral accumulation, enhanced tumor suppression with reduced treatment time, and devoid of any noticeable toxicity.

A Super-Sensitivity Photoacoustic Receiver System-on-Chip Based on Coherent Detection and Tracking.

Photoacoustic (PA) imaging is becoming more attractive because it can obtain high-resolution and high-contrast images through merging the merits of optical and acoustic imaging. High sensitivity receiver is required in deep in-vivo PA imaging due to detecting weak and noisy ultrasound signal. A novel photoacoustic receiver system-on-chip (SoC) with coherent detection (CD) based on the early-and-late acquisition and tracking is developed and first fabricated. In this system, a weak PA signal with negative signal-to-noise-ratio (SNR) can be clearly extracted when the tracking loop is locked to the input. Consequently, the output SNR of the receiver is significantly improved by about 29.9 dB than input one. For the system, a high dynamic range (DR) and high sensitivity analog front-end (AFE), a multiplier, a noise shaping (NS) successive-approximation (SAR) analog-to-digital convertor (ADC), a digital-to-analog convertor (DAC) and integrated digital circuits for the proposed system are implemented on-chip. Measurement results show that the receiver achieves 0.18 µVrms sensitivity at the depth of 1 cm with 1 mJ/cm2 laser output fluence. The contrast-to-noise (CNR) of the imaging is improved by about 22.2 dB. The area of the receiver is 5.71 mm2, and the power consumption of each channel is about 28.8 mW with 1.8 V and 1 V power supply on the TSMC 65 nm CMOS process.

A Mixed-Signal Chip-Based Configurable Coherent Photoacoustic-Radar Sensing Platform for In Vivo Temperature Monitoring and Vital Signs Detection.

For precise health status monitoring and accurate disease diagnostics in the current COVID-19 pandemic, it is essential to detect various kinds of target signals robustly under high noise and strong interferences. Moreover, the health monitoring system is preferred to be realized in a small form factor for convenient mass deployments. A CMOS-integrated coherent sensing platform is proposed to achieve the goal, which synergetically leverages quadrature coherent photoacoustic (PA) detection and coherent radar sensing for achieving universal healthcare. By utilizing configurable mixed-signal quadrature coherent PA detection, high sensitivity and enhanced specificity can be achieved. In-phase (I) and quadrature (Q) templates are specifically designed to accurately sense and precisely reconstruct the target PA signals in a coherent mode. By mixed-signal implementation leveraging an FPGA to generate template waveforms adaptively, accurate tracking and precise reconstruction on the target PA signal can be attained based on the early-late tracking principle. The multiplication between the received PA signal and the templates is implemented efficiently in analog-domain by the Gilbert cell on-chip. In vivo blood temperature monitoring was realized based on the integrated PA sensing platform fabricated in a 65-nm CMOS process. With an integrated radar sensor deployed in the indoor scenario, noncontact monitoring on respiration and heartbeat rates can be attained based on electromagnetic (EM) sensing. By complementary usage of PA-EM sensing mechanisms, comprehensive health status monitoring and precise remote disease diagnostics can be achieved for the currentglobal COVID-19 pandemic and the future pervasive healthcare in the Internet of Everything (IoE) era.

Morphology-dependent resonance enhanced nonlinear photoacoustic effect in nanoparticle suspension: a temporal-spatial model.

The morphology-dependent resonances (MDRs) hotspot, ubiquity formed between the pairs of nanoparticles in close vicinity, has garnered considerable recent attention. By extending this phenomenon to pulse-laser irradiated nanoparticle suspension, we demonstrate that such collective optical/thermal enhancement can give rise to the nonlinear photoacoustic (PA) generation. In this study, a temporal-spatial analytical expression is derived to quantitatively describe the nonlinear PA signal generation from nanoparticles, incorporating the Grüneisen increase at the microscopic individual particle level and MRDs enhancement at the macroscopic suspension level. The dependence of PA nonlinearity on the critical contributors, including the laser pulse width, the particle size, and the statistical interparticle spacing, is quantitatively discussed. The theory is well validated with the finite element method (FEM) and experimentally proved with semiconducting polymer nanoparticles (SPN) suspension. This work may pave a new direction towards effective MDR based nonlinear PA contract agent design.

Evaluation of Reconstruction Methodology for Helical Scan Guided Photoacoustic Endoscopy.

Photoacoustic endoscopy (PAE), combining both advantages of optical contrast and acoustic resolution, can visualize the chemical-specific optical information of tissues inside human-body. Recently, its corresponding reconstruction methods have been extensively researched. However, most of them are limited on cylindrical scan trajectories, rather than a helical scan which is more clinically practical. On this note, this article proposes a methodology of imaging reconstruction and evaluation for helical scan guided PAE. Different from traditional reconstruction method, synthetic aperture focusing technique (SAFT), our method reconstructs image using wavefield extrapolation which significantly improves computational efficiency and even takes only 0.25 seconds for 3-D reconstructions. In addition, the proposed evaluation methodology can estimate the resolutions and deviations of reconstructed images in advance, and then can be used to optimize the PAE scan parameters. Groups of simulations as well as ex-vivo experiments with different scan parameters are provided to fully demonstrate the performance of the proposed techniques. The quantitatively measured angular resolutions and deviations agree well with our theoretical derivation results D√{rs2 +h2} / [1.25(rs rd +h2)] (rad) and -h l / (rs rd +h2) (rad), respectively D,rd, rs,h and l represent transducer diameter, radius of scan trajectory, radius of source position, unit helical pitch and the distance from targets to helical scan plane, respectively). This theoretical result also suits for circular and cylindrical scan in case of h = 0 .

Noninvasive Electromagnetic Wave Sensing of Glucose.

Diabetic patients need long-term and frequent glucose monitoring to assist in insulin intake. The current finger-prick devices are painful and costly, which places noninvasive glucose sensors in high demand. In this review paper, we list several advanced electromagnetic (EM)-wave-based technologies for noninvasive glucose measurement, including infrared (IR) spectroscopy, photoacoustic (PA) spectroscopy, Raman spectroscopy, fluorescence, optical coherence tomography (OCT), Terahertz (THz) spectroscopy, and microwave sensing. The development of each method is discussed regarding the fundamental principle, system setup, and experimental results. Despite the promising achievements that have been previously reported, no established product has obtained FDA approval or survived a marketing test. The limitations of, and prospects for, these techniques are presented at the end of this review.

Handheld Photoacoustic Imager for Theranostics in 3D.

A handheld approach to 3D photoacoustic imaging is essential in clinical applications. To this end, we develop a 3D handheld photoacoustic imager for dynamic (temporally and spatially) volumetric visualization. In this 3D imager, the optically transmitting part and the acoustically receiving part are integrated into a single handheld probe with a compact size about 160 mm ×64 mm ×40 mm. Besides, a dedicated imaging reconstruction algorithm for the heterogeneous medium is developed based on the phase-shift migration method in the frequency domain, which deals well with the stratified condition in the designed system. Dynamic 3D imaging supporting flexible handheld operation is demonstrated with needle biopsy and in vitro temperature measurement for photothermal therapy. The development of such a 3D handheld photoacoustic system paves the way for compact and handheld-operating implementations, and its further clinical exploration is promising.

Electromagnetic⁻Acoustic Sensing for Biomedical Applications.

This paper reviews the theories and applications of electromagnetic⁻acoustic (EMA) techniques (covering light-induced photoacoustic, microwave-induced thermoacoustic, magnetic-modulated thermoacoustic, and X-ray-induced thermoacoustic) belonging to the more general area of electromagnetic (EM) hybrid techniques. The theories cover excitation of high-power EM field (laser, microwave, magnetic field, and X-ray) and subsequent acoustic wave generation. The applications of EMA methods include structural imaging, blood flowmetry, thermometry, dosimetry for radiation therapy, hemoglobin oxygen saturation (SO2) sensing, fingerprint imaging and sensing, glucose sensing, pH sensing, etc. Several other EM-related acoustic methods, including magnetoacoustic, magnetomotive ultrasound, and magnetomotive photoacoustic are also described. It is believed that EMA has great potential in both pre-clinical research and medical practice.