Photoacoustic Tomography (PAT) combines the advantages of optical imaging and ultrasound imaging, playing an indispensable role in biomedical research and clinical investigations. However, current advanced PAT systems are large in size and expensive, limiting their widespread adoption. Despite attempts to reduce costs by using low-cost light sources, research on sensor driving and data acquisition optimization remains lacking. Therefore, we present a low-cost and high-speed PAT system consisting of a 20Hz pulse frequency PhotoSonus-YAG laser, ultrasound array transducer, a multi-channel high-speed data acquisition system, and computer.This study designs a multi-channel high-speed data acquisition system (DAS) based on FPGA. The system, with FPGA as the core, DDR III SDRAM as the storage device, and a 14-bit high-performance ADC as the core analogto- digital conversion chip, utilizes a USB-based high-speed data acquisition card solution. To meet the demand for synchronous processing of multi-channel signals, the system employs high-end FPGA chips from Xilinx’s ZYNQ7000 series and Texas Instruments’ AFE5816 chips from the ultrasonic AFE series. These components are interconnected via Low Voltage Differential Signaling (LVDS) interfaces to ensure high-speed and highly reliable digital signal transmission. The designed high-speed data acquisition system achieves a collection of 65MSPS × 14 Bit × 16 channels, with a maximum data acquisition speed of 1000 frames per second. This design not only significantly reduces the volume and cost of the PAT system but also ensures the quality of image preservation through real-time data acquisition and processing.
With the advancement of technology and increasing security demands, the exploration and extraction of new internal fingertip features have become a significant trend. In traditional fingerprint recognition systems, enhancing anti-spoofing capabilities is crucial. Conventional fingerprints are typically obtained through surface imaging, making their texture features easily susceptible to theft. Optical coherence tomography (OCT) technology offers non-invasive, high-resolution, and live tissue detection advantages, providing micron-level resolution images of biological tissues within a millimeter depth range. This enables the capture of more secure and stable internal biometric features such as internal fingerprints, sweat pores, and sweat glands. Subcutaneous fingerprints are stable, difficult to alter, and possess strong anti-spoofing characteristics. Consequently, subcutaneous fingerprint recognition promises higher security and reliability, addressing the shortcomings of currently prevalent fingerprint recognition systems. This paper presents a subcutaneous fingerprint recognition scheme based on an embedded system. The scheme utilizes Xilinx's Zynq, an all-programmable System on Chip (SoC), and employs OCT technology for fingerprint capture to meet the reliability demands of fingerprint recognition. It addresses issues in traditional OCT capture systems, such as large size, high power consumption, and poor scalability. By using image processing algorithms such as Gray level Co-occurrence Matrix(GLCM), the system extracts features from subcutaneous fingerprint images, achieving low-cost, real-time subcutaneous fingerprint image capture and recognition.
KEYWORDS: Optical coherence tomography, Image processing, Data processing, Data acquisition, Data conversion, Spectral data processing, Computing systems, Signal processing, Image restoration
Optical Coherence Tomography(OCT) system is a non-contact imaging modality based on low-coherence optical interferometry, used for imaging turbid scattering media. They excel in rendering depth-resolved images of internal structures with micrometer-scale resolution. Previous OCT systems have some defects in image reconstruction, which are limited by complex signal processing and mathematical computation, slow image processing speed, difficult to realize real-time imaging, and high equipment cost. This paper proposes an OCT image reconstruction algorithm acceleration scheme based on the combination of FPGA (Field-Programmable Gate Array) and Python, aiming at accelerating and simplifying the image acquisition and processing of the OCT system through Python, so as to enhance the efficiency of medical diagnosis and biological research. Using Python as the upper computer control software, provide user-friendly graphical interface, output spectral waveform and then realize the Fourier transform, de-direct current and autocorrelation terms and other algorithmic steps to generate OCT images, to realize the real-time data transmission and processing. Python not only has a powerful data visualization ability, but also has the advantages of simple operation, easy to develop the program to ensure that the system operates efficiently.
In this study, a detection system based on Polarization-Sensitive Optical Coherence Tomography (PS-OCT) using Mueller Matrix Optical Coherence Tomography (MM-OCT) was developed. By employing PS-OCT technology, the system was able to fully detect all sixteen elements of the Mueller matrix. By comparing the intensity element M00 among the sixteen elements of the Mueller matrix, the texture structure of the pearl layers could be observed. This allowed for differentiation between freshwater and saltwater pearls, identification of genuine and fake pearls, detection of internal flaws in pearls, and differentiation between nucleated and non-nucleated pearls. The study also involved the labeling of connected regions in binary images, where pixels within the same connected region were assigned the same label. The labeled images were displayed to facilitate more intuitive qualitative analysis, and quantitative analysis was performed using gray-level co-occurrence matrices. Subsequently, pearl layer pixels were extracted from multiple angles in the images, and the thickness of the pearl layer was calculated using the extracted pixels and axial resolution. Finally, detection and classification of unknown pearls were conducted, yielding results consistent with the actual outcomes. The measured thickness results after sectioning matched the calculated results, providing evidence for the feasibility of the experimental method proposed in this study.
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