Conventional phase-shift method can obtain the topography information of the object surface by projecting phase-shift fringe sequence and capturing deformed pictures. However, overexposure on shiny surfaces causes the loss of fringe information received by the camera, thus reducing the measurement accuracy and integrity. To solve this problem, an adaptive fringe projection method based on speckle-assisted fringes and mask fusion is proposed. Firstly, the optimal projection intensities are obtained by fusing multiple mask images. Then, the pixel-by-pixel mapping relationship between the camera and projector is established by using speckle-assisted phase shift method under unsaturated condition. Finally, the adaptive intensity fringe is generated and projected by the constraint of the mapping relationship. The 3D reconstruction is performed by the speckle-assisted phase shift method. Experimental results show that the RMS phase error of the proposed method is reduced by 94%, 33% and 12% compared with the contrast methods. The proposed method can accurately match the phase information of the overexposed surface and reconstruct objects with large surface fluctuations, thereby effectively solving the problem of 3D reconstruction of high reflective objects.
Coronary artery disease (CAD) is a cardiovascular disease characterized by coronary stenosis or occlusion due to atherosclerosis, which may result in a number of symptoms, including myocardial ischemia, angina and heart failure. Coronary computed tomography angiography (CCTA) is a diagnostic assessment for CAD. Radiology encompasses a vast amount of quantitative, high-dimensional features and transform medical images into a rich dataset that can be explored for insights. This study introduces an approach leveraging radiology features for the automated detection of coronary artery stenosis. We extract curved planar reconstruction (CPR) images along with the segmentation of the coronary arteries from three-dimensional CCTA images and extract radiomic features from the segmented regions of interest. Considering the high-dimensional nature of radiology features, we utilize techniques like LASSO regression to reduce the dimensionality of these features. We construct a graph convolutional network (GCN) block to fuse radiomic features and deep features embed this block within an encoder-decoder network. In the visualization analysis of coronary radiology features, there is a qualitative distinction between lipid and calcification regions, demonstrating the diagnostic value of radiology in coronary stenosis detection.
In this study, we developed a nasal endoscopy simulation system based on Unity to be used for training novice doctors. The nasal cavity and internal structures were segmented from high-resolution CT images of patients, and mesh models were generated. To address model penetration between surgical tools and the nasal cavity model, we proposed a collision detection method based on motion trend prediction. By predicting potential collisions based on motion trends and performing corresponding preprocessing, we were able to prevent model penetration. The deformation of soft tissue structures within the nasal cavity was achieved by using a voxel-based PBD model. By voxelizing the mesh model and adding PBD constraints to the voxelized model, we achieved synchronous deformation between the mesh and voxelized models through coupling. For haptic rendering, two Phantom Omni devices were used to operate endoscopes and other surgical tools while providing force feedback. Experimental results showed that our system can provide realistic nasal endoscopy scenarios and effectively assist doctors in surgical training.
As the primary method for real-time image processing, a field-programmable gate array (FPGA) is widely used in binocular vision systems. Distortion correction is an important component of binocular stereo vision systems. When implementing a real-time image distortion correction algorithm on FPGA, problems, such as insufficient on-chip storage space and high complexity of coordinate correction calculation methods, occur. These problems are analyzed in detail in this study. On the basis of the reverse mapping method, a distortion correction algorithm that uses a lookup table (LUT) is proposed. A compression with restoration method is established for this LUT to reduce space occupation. The corresponding cache method of LUT and the image data are designed. The algorithm is verified on our binocular stereo vision system based on Xilinx Zynq-7020. The experiments show that the proposed algorithm can achieve real-time and high precision gray image distortion correction effect and significantly reduce the consumption of on-chip resources. Enough to meet the requirements of accurate binocular stereo vision system.
Photon upconversion with the transformation of low-energy photons to high-energy photons is of significant interest for broad applications. Here, we present self-powered, micrometer-scale optoelectronic devices based on III-V materials for high-performance near-infrared to visible upconversion. By taking advantage of its unique photon – “free electron” – photon processes, these thin-film, ultra-miniaturized devices realize fast upconversion that is linearly dependent on incoherent, low-power excitation, with a quantum yield of ~1.5%. By exploiting the advanced manufacturing method, encapsulated, freestanding devices are transferred onto heterogeneous substrates and show desirable biocompatibilities within biological fluids and tissues. These devices as the microscale light sources are implanted in behaving animals, with in vitro and in vivo experiments demonstrating their utility for optogenetic neuromodulation. These results provide routes for high-performance upconversion materials and devices and their unprecedented potential as optical biointerfaces.
Photon upconversion with the transformation of low-energy photons to high-energy photons is of significant interest for broad applications in biomedicine for stimulation, sensing, and imaging. Conventional upconversion materials rely on non-linear light-matter interactions, exhibit incidence dependent efficiencies and require high power excitation. Here, we present self-powered, micrometer-scale optoelectronic devices for high-performance near-infrared (~810 nm) to visible (630 nm red or 590 nm yellow) photon upconversion. Thanks to its unique photon–electron conversion process, these thin-film, ultra-miniaturized devices realize fast upconversion that is linearly dependent on incoherent, low-power excitation, with a quantum yield of ~1.5%. Encapsulated, freestanding devices are transferred onto heterogeneous flexible substrates and show desirable biocompatibilities within biological fluids and tissues. Demonstrations of optogenetic stimulation with upconversion devices as implantable light sources have successfully performed in vitro and in vivo scenarios. This approach provides a versatile route to achieve upconversion throughout the entire visible spectral range at lower power and higher efficiency than has previously been possible.
Over the last few years, major breakthroughs were achieved in the application of deep learning in many computer vision tasks, such as image classification and segmentation. The automatic liver segmentation from CT images has become an important area in clinical research, including radiotherapy, liver volume measurement, and liver transplant surgery. This paper proposes a novel convolutional neural network for liver segmentation (CNN-LivSeg) algorithm that involves three convolutional (each convolutional layer followed by max-pooling layer) and two fully connected layers with a final 2- way softmax is used for liver discrimination. The weight initialization is based on a random Gaussian, which performed a distance preserving-embedding of the data. To avoid using the fully 3D CNN network which is computationally expensive and time-consuming, 2D patches were extracted and processed for segmentation. Experiments were performed on MICCAI-SLiver07 as a benchmark dataset. The mean ratios of Dice similarity coefficient, Jaccard similarity index, accuracy, specificity, and sensitivity were 0.9541, 0.9122, 0.9725, 0.9904, and 0.9652, respectively, thereby suggesting that the proposed method performed well on the test images.
Vascular anastomosis, connecting two vessel ends together, is the foundation of plastic and reconstructive surgery. Every living tissue transplantation requires vascular anastomosis. While optical coherence tomography (OCT) imaging can provide objective information for intraoperative evaluation, currently it suffers from limited imaging penetration depth for relative large vessels, which will result in information loss at the bottom region of vessel. In this work, we designed a multi-view scanning scheme for the vessel imaging to increase the FOV effectively. To push the clinical translation, we used MEMS mirror to steer the beam and made a miniature handheld probe for testing. Preliminary results on IR sensing card, multilayer scotch tape, and plastic tube imaging showed the performance of our probe.
KEYWORDS: Stars, Star sensors, Computer simulations, Device simulation, Monte Carlo methods, Data processing, Image sensors, Space operations, LCDs, Computing systems
A designed star sensor must be extensively tested before launching. Testing star sensor requires complicated process
with much time and resources input. Even observing sky on the ground is a challenging and time-consuming job,
requiring complicated and expensive equipments, suitable time and location, and prone to be interfered by weather. And
moreover, not all stars distributed on the sky can be observed by this testing method. Semi-physical simulation in
laboratory reduces the testing cost and helps to debug, analyze and evaluate the star sensor system while developing the
model. The test system is composed of optical platform, star field simulator, star field simulator computer, star sensor
and the central data processing computer. The test system simulates the starlight with high accuracy and good
parallelism, and creates static or dynamic image in FOV (Field of View). The conditions of the test are close to
observing real sky. With this system, the test of a micro star tracker designed by Beijing University of Aeronautics and
Astronautics has been performed successfully. Some indices including full-sky autonomous star identification time,
attitude update frequency and attitude precision etc. meet design requirement of the star sensor. Error source of the
testing system is also analyzed. It is concluded that the testing system is cost-saving, efficient, and contributes to
optimizing the embed arithmetic, shortening the development cycle and improving engineering design processes.
KEYWORDS: Arteries, Angiography, 3D modeling, X-rays, Image processing, Image segmentation, 3D image processing, Visualization, Detection and tracking algorithms, Imaging systems
In this paper, we have developed a model-based approach to match two X-ray angiograms from different views. Under
the guidance of the prior knowledge of anatomic structure of human coronary vessels, this method can build a node
attribute table and assign unique anatomic labels to coronary arteries in X-ray angiograms automatically by the
father-son relationship of the nodes, which is essential in reconstruction of vessels.
We constructed a computer controlled high stability wide tunable parametric amplifier, and reported on a (beta) - borium borate (BBO) optical parametric amplifier of injection-seeded with a narrow linewidth pulsed Ti:sapphire laser. The pump source of Ti:sapphire laser is the residual second harmonic (532 nm) in frequency tripling (355 nm) of the Nd:YAG laser. We obtained the linewidth interferograph of Ti:sapphire laser is less than 0.003 nm, and tested six times curves of the output energy as a function of tunable wavelengths in injection seeding more than that of no injection. The narrow linewidth (<0.1 nm) continuous tunable range of 570 - 670 nm is achieved.
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