An ultrasensitive and broadband transparent ultrasound transducer for ultrasound and photoacoustic imaging in-vivo.

Transparent ultrasound transducers (TUTs) can seamlessly integrate optical and ultrasound components, but acoustic impedance mismatch prohibits existing TUTs from being practical substitutes for conventional opaque ultrasound transducers. Here, we propose a transparent adhesive based on a silicon dioxide-epoxy composite to fabricate matching and backing layers with acoustic impedances of 7.5 and 4-6 MRayl, respectively. By employing these layers, we develop an ultrasensitive, broadband TUT with 63% bandwidth at a single resonance frequency and high optical transparency ( > 80%), comparable to conventional opaque ultrasound transducers. Our TUT maximises both acoustic power and transfer efficiency with maximal spectrum flatness while minimising ringdowns. This enables high contrast and high-definition dual-modal ultrasound and photoacoustic imaging in live animals and humans. Both modalities reach an imaging depth of > 15 mm, with depth-to-resolution ratios exceeding 500 and 370, respectively. This development sets a new standard for TUTs, advancing the possibilities of sensor fusion.

Deep learning alignment of bidirectional raster scanning in high speed photoacoustic microscopy.

Simultaneous point-by-point raster scanning of optical and acoustic beams has been widely adapted to high-speed photoacoustic microscopy (PAM) using a water-immersible microelectromechanical system or galvanometer scanner. However, when using high-speed water-immersible scanners, the two consecutively acquired bidirectional PAM images are misaligned with each other because of unstable performance, which causes a non-uniform time interval between scanning points. Therefore, only one unidirectionally acquired image is typically used; consequently, the imaging speed is reduced by half. Here, we demonstrate a scanning framework based on a deep neural network (DNN) to correct misaligned PAM images acquired via bidirectional raster scanning. The proposed method doubles the imaging speed compared to that of conventional methods by aligning nonlinear mismatched cross-sectional B-scan photoacoustic images during bidirectional raster scanning. Our DNN-assisted raster scanning framework can further potentially be applied to other raster scanning-based biomedical imaging tools, such as optical coherence tomography, ultrasound microscopy, and confocal microscopy.

Performance comparison of high-speed photoacoustic microscopy: opto-ultrasound combiner versus ring-shaped ultrasound transducer.

Photoacoustic microscopy (PAM) embedded with a 532 nm pulse laser is widely used to visualize the microvascular structures in both small animals and humans in vivo. An opto-ultrasound combiner (OUC) is often utilized in high-speed PAM to confocally align the optical and acoustic beams to improve the system's sensitivity. However, acoustic impedance mismatch in the OUC results in little improvement in the sensitivity. Alternatively, a ring-shaped ultrasound transducer (RUT) can also accomplish the confocal configuration. Here, we compare the performance of OUC and RUT modules through ultrasound pulse-echo tests and PA imaging experiments. The signal-to-noise ratios (SNRs) of the RUT-based system were 15 dB, 12 dB, and 7 dB higher when compared to the OUC-based system for ultrasound pulse-echo test, PA phantom imaging test, and PA in-vivo imaging test, respectively. In addition, the RUT-based system could image the microvascular structures of small parts of a mouse body in a few seconds with minimal loss in SNR. Thus, with increased sensitivity, improved image details, and fast image acquisition, we believe the RUT-based systems could play a significant role in the design of future fast-PAM systems.

Deep learning acceleration of multiscale superresolution localization photoacoustic imaging.

A superresolution imaging approach that localizes very small targets, such as red blood cells or droplets of injected photoacoustic dye, has significantly improved spatial resolution in various biological and medical imaging modalities. However, this superior spatial resolution is achieved by sacrificing temporal resolution because many raw image frames, each containing the localization target, must be superimposed to form a sufficiently sampled high-density superresolution image. Here, we demonstrate a computational strategy based on deep neural networks (DNNs) to reconstruct high-density superresolution images from far fewer raw image frames. The localization strategy can be applied for both 3D label-free localization optical-resolution photoacoustic microscopy (OR-PAM) and 2D labeled localization photoacoustic computed tomography (PACT). For the former, the required number of raw volumetric frames is reduced from tens to fewer than ten. For the latter, the required number of raw 2D frames is reduced by 12 fold. Therefore, our proposed method has simultaneously improved temporal (via the DNN) and spatial (via the localization method) resolutions in both label-free microscopy and labeled tomography. Deep-learning powered localization PA imaging can potentially provide a practical tool in preclinical and clinical studies requiring fast temporal and fine spatial resolutions.

Collapse pressure measurement of single hollow glass microsphere using single-beam acoustic tweezer.

Microbubbles are widely used in medical ultrasound imaging and drug delivery. Many studies have attempted to quantify the collapse pressure of microbubbles using methods that vary depending on the type and population of bubbles and the frequency band of the ultrasound. However, accurate measurement of collapse pressure is difficult as a result of non-acoustic pressure factors generated by physical and chemical reactions such as dissolution, cavitation, and interaction between bubbles. In this study, we developed a method for accurately measuring collapse pressure using only ultrasound pulse acoustic pressure. Under the proposed method, the collapse pressure of a single hollow glass microsphere (HGM) is measured using a high-frequency (20-40 MHz) single-beam acoustic tweezer (SBAT), thereby eliminating the influence of additional factors. Based on these measurements, the collapse pressure is derived as a function of the HGM size using the microspheres' true density. We also developed a method for estimating high-frequency acoustic pressure, whose measurement using current hydrophone equipment is complicated by limitations in the size of the active aperture. By recording the transmit voltage at the moment of collapse and referencing it against the corresponding pressure, it is possible to estimate the acoustic pressure at the given transmit condition. These results of this study suggest a method for quantifying high-frequency acoustic pressure, provide a potential reference for the characterization of bubble collapse pressure, and demonstrate the potential use of acoustic tweezers as a tool for measuring the elastic properties of particles/cells.

Detection of micro inclusions in steel sheets using high-frequency ultrasound speckle analysis.

With the increasing need for steel sheet quality assurance, the detection of micro-scaled inclusions in steel sheets has become critical. Many techniques have been explored to detect inclusions, e.g., visual inspection, radiography, magnetic testing, and ultrasound. Among these methods, ultrasound (US) is the most commonly used non-destructive testing (NDT) method due to its ease of use and deep penetration depth. However, ultrasound currently cannot be used for detecting the micro-scaled inclusions due to low spatial resolution, e.g., less than 30 µm, which are the key important factors causing the cracks in the high-quality steel sheets. Here, we demonstrate a high-resolution US imaging (USI) using high-frequency US transducers to image micro inclusions in steel sheets. Our system utilizes through-transmission USI and identifies ultrasound scattering produced by the inclusions. We first ultrasonically imaged the artificial flaws induced by the laser on the steel sheet surface for validating the system. We then imaged the real inclusions in the steel sheets formed during manufacturing processes and analyzed them to derive quantitative parameters related to the number of micro-scaled inclusions. Our results confirm that inclusions less than 30 µm can be identified using our high-resolution USI modality and has the potential to be used as an effective tool for quality assurance of the steel sheets.

Thermal Ablation and High-Resolution Imaging Using a Back-to-Back (BTB) Dual-Mode Ultrasonic Transducer: In Vivo Results.

We present a back-to-back (BTB) structured, dual-mode ultrasonic device that incorporates a single-element 5.3 MHz transducer for high-intensity focused ultrasound (HIFU) treatment and a single-element 20.0 MHz transducer for high-resolution ultrasound imaging. Ultrasound image-guided surgical systems have been developed for lesion monitoring to ensure that ultrasonic treatment is correctly administered at the right locations. In this study, we developed a dual-element transducer composed of two elements that share the same housing but work independently with a BTB structure, enabling a mode change between therapy and imaging via 180-degree mechanical rotation. The optic fibers were embedded in the HIFU focal region of ex vivo chicken breasts and the temperature change was measured. Images were obtained in vivo mice before and after treatment and compared to identify the treated region. We successfully acquired B-mode and C-scan images that display the hyperechoic region indicating coagulation necrosis in the HIFU-treated volume up to a depth of 10 mm. The compact BTB dual-mode ultrasonic transducer may be used for subcutaneous thermal ablation and monitoring, minimally invasive surgery, and other clinical applications, all with ultrasound only.