Photoacoustics最新文献

We introduce a physics-based computational reconstruction framework for non-invasive photoacoustic tomography through a thick aberrating layer. Our wave-based approach leverages an analytic formulation of diffraction to beamform a photoacoustic image, when the aberrating layer profile is known. When the profile of the aberrating layer is unknown, the same analytical formulation serves as the basis for an automatic-differentiation regularized optimization algorithm that simultaneously reconstructs both the profile of the aberrating layer and the optically absorbing targets. Results from numerical studies and proof-of-concept experiments show promise for fast beamforming that takes into account diffraction effects occurring in the propagation through thick, highly-aberrating layers.

We report on a photoacoustic sensor system based on a differential photoacoustic cell to detect the concentration of CO impurities in hydrogen. A DFB-QCL laser with a central wavelength of 4.61 µm was employed as an exciting source with an optical power of 21 mW. Different concentrations of CO gas mixed with pure hydrogen were injected into the photoacoustic cell to test the linear response of the photoacoustic signal to the CO concentration. The stability of the long-term operation was verified by Allan-Werle deviation analysis. The minimum detection limit (MDL, SNR=1) results 8 ppb at 1 s and reaches a sub-ppb level at 100 s of integration time. Dynamic response of the system is linear and has been tested up to the concentration of 6 ppm. Saturation conditions are expected to be reached for CO concentration larger than 100 ppm.

The X-ray free-electron laser (XFEL) has remarkably advanced X-ray imaging technology and enabled important scientific achievements. The XFEL’s extremely high power, short pulse width, low emittance, and high coherence make possible such diverse imaging techniques as absorption/emission spectroscopy, diffraction imaging, and scattering imaging. Here, we demonstrate a novel XFEL-based imaging modality that uses the X-ray induced acoustic (XA) effect, which we call X-ray free-electron laser induced acoustic microscopy (XFELAM). Initially, we verified the XA effect by detecting XA signals from various materials, then we validated the experimental results with simulation outcomes. Next, in resolution experiments, we successfully imaged a patterned tungsten target with drilled various-sized circles at a spatial resolution of 7.8 ± 5.1 µm, which is the first micron-scale resolution achieved by XA imaging. Our results suggest that the novel XFELAM can expand the usability of XFEL in various areas of fundamental scientific research.

A high sensitivity and ultra-low concentration range photoacoustic spectroscopy (PAS) gas detection system, which was based on a novel trapezoid compound ellipsoid resonant photoacoustic cell (TCER-PAC) and partial least square (PLS), was proposed to detect acetylene (C₂H₂) gas. In the concentration range of 0.5 ppm ∼ 10.0 ppm, the limit of detection (LOD) values of TCER-PAC-based PAS system without data processing was 66.4 ppb, which was lower than that of the traditional trapezoid compound cylindrical resonant photoacoustic cell (TCCR-PAC). The experimental results indicated that the TCER-PAC had higher sensitivity than of TCCR-PAC. Within the concentration range of 12.5 ppb ∼ 125.0 ppb, the LOD and limit of quantification (LOQ) of TCER-PAC-based PAS system combined with PLS regression algorithm were 1.1 ppb and 3.7 ppb, respectively. The results showed that higher detection sensitivity and lower LOD were obtained by PAS system with TCER-PAC and PLS than that of TCCR-PAC-based PAS system.

Optoacoustic (photoacoustic) mesoscopy bridges the gap between optoacoustic microscopy and macroscopy and enables high-resolution visualization deeper than optical microscopy. Nevertheless, as images may be affected by motion and noise, it is critical to develop methodologies that offer standardization and quality control to ensure that high-quality datasets are reproducibly obtained from patient scans. Such development is particularly important for ensuring reliability in applying machine learning methods or for reliably measuring disease biomarkers. We propose herein a quality control scheme to assess the quality of data collected. A reference scan of a suture phantom is performed to characterize the system noise level before each raster-scan optoacoustic mesoscopy (RSOM) measurement. Using the recorded RSOM data, we develop a method that estimates the amount of motion in the raw data. These motion metrics are employed to classify the quality of raw data collected and derive a quality assessment index (QASIN) for each raw measurement. Using simulations, we propose a selection criterion of images with sufficient QASIN, leading to the compilation of RSOM datasets with consistent quality. Using 160 RSOM measurements from healthy volunteers, we show that RSOM images that were selected using QASIN were of higher quality and fidelity compared to non-selected images. We discuss how this quality control scheme can enable the standardization of RSOM images for clinical and biomedical applications.

In this paper, we present a novel on-demand modular robotic photoacoustic tomography (PAT) probe integrated into an endoscopic device, potentially for deep intragastric sensing. The proposed solution offers a plug-and-play approach through the use of meso-scale steerable endoscopy and a new ‘snap-on’ 3D robotic PAT probe that can reconfigure the geometry of the intracorporeal light delivery, inspired by an umbrella structure. Specifically, using the limited esophageal access, steerable endoscopy allows navigation and advancement of a distally mounted robotic add-on for PAT that is folded until it reaches the deep-seated gastric lesion. Once the tip is positioned near the lesion site in the gastric cavity, there is ample working space for the robotic probe to adjust its umbrella-like unfolded shape. This allows fine-tuning of the laser delivery orientation of the fiber bundles to achieve the lesion-specific light delivery scheme. This design allows volumetric imaging of the intragastric PAT with enhanced sensitivity. To evaluate the performance of the modular robotic PAT probe, we performed a simulation analysis of the light intensity and ultrasound field distribution. The simulation results show that the robotic probe is feasible for intracorporeal PAT imaging. In addition, we printed a 3D model of a human stomach containing a simulated gastric tumour. Both the phantom and ex vivo experimental results validate the feasibility of the proposed robotic PAT probe.

Folded-optics-based quartz-enhanced photoacoustic and photothermal hybrid spectroscopy (FO-QEPA-PTS) is reported for the first time. In FO-QEPA-PTS, the detection of the photoacoustic and photothermal hybrid signal is achieved through the use of a custom quartz tuning fork (QTF), thereby mitigating the issue of resonant frequency mismatch typically encountered in quartz-enhanced photoacoustic-photothermal spectroscopy employing multiple QTFs. A multi-laser beam, created by a multi-pass cell (MPC) with a designed single-line spot pattern, partially strikes the inner edge of the QTF and partially passes through the prong of the QTF, thereby generating photoacoustic and photothermal hybrid signals. To assess the performance of FO-QEPA-PTS, 1 % acetylene is selected as the analyte gas and the 2f signals produced by the photoacoustic, the photothermal, and their hybrid effects are measured. Comparative analysis against QEPAS and QEPTS reveals signal gain factors of ∼ 79 and ∼ 14, respectively, when these laser beams created by MPC excite the QTF operating at fundamental resonance mode in phase. In the FO-QEPA-PTS signal, the proportions of the photoacoustic and the photothermal effects induced by the multiple beams are ∼7 % and 93 %, respectively.

Peripheral arterial disease (PAD) leads to chronic vascular occlusion and results in end organ damage in critically perfused limbs. There are currently no clinical methods available to determine the muscular damage induced by chronic mal-perfusion. This monocentric prospective cross-sectional study investigated n = 193 adults, healthy to severe PAD, in order to quantify the degree of calf muscle degeneration caused by PAD using a non-invasive hybrid ultrasound and single wavelength optoacoustic imaging (US/SWL-OAI) approach. While US provides morphologic information, SWL-OAI visualizes the absorption of pulsed laser light and the resulting sound waves from molecules undergoing thermoelastic expansion. US/SWL-OAI was compared to multispectral data, clinical disease severity, angiographic findings, phantom experiments, and histological examinations from calf muscle biopsies. We were able to show that synergistic use of US/SWL-OAI is most likely to map clinical degeneration of the muscle and progressive PAD.

Accurate needle guidance is crucial for safe and effective clinical diagnosis and treatment procedures. Conventional ultrasound (US)-guided needle insertion often encounters challenges in consistency and precisely visualizing the needle, necessitating the development of reliable methods to track the needle. As a powerful tool in image processing, deep learning has shown promise for enhancing needle visibility in US images, although its dependence on manual annotation or simulated data as ground truth can lead to potential bias or difficulties in generalizing to real US images. Photoacoustic (PA) imaging has demonstrated its capability for high-contrast needle visualization. In this study, we explore the potential of PA imaging as a reliable ground truth for deep learning network training without the need for expert annotation. Our network (UIU-Net), trained on ex vivo tissue image datasets, has shown remarkable precision in localizing needles within US images. The evaluation of needle segmentation performance extends across previously unseen ex vivo data and in vivo human data (collected from an open-source data repository). Specifically, for human data, the Modified Hausdorff Distance (MHD) value stands at approximately 3.73, and the targeting error value is around 2.03, indicating the strong similarity and small needle orientation deviation between the predicted needle and actual needle location. A key advantage of our method is its applicability beyond US images captured from specific imaging systems, extending to images from other US imaging systems.