Prof. Bin Yan Profile

Prof. Bin Yan

at National Digital Switching System Engineering

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Author

Publications (22)

Proceedings Article | 8 November 2023 Paper

Scanning coherent diffraction imaging using cascaded residual network

Mengnan Liu, Yu Han, Xiaoqi Xi, Linlin Zhu, Qi Zhong, Ningning Liang, Lei Li, Bin Yan

Proceedings Volume 12923, 1292328 (2023) https://doi.org/10.1117/12.3011412

KEYWORDS: Reconstruction algorithms, Image restoration, Algorithm development, Phase retrieval, Network architectures

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Proceedings Article | 10 August 2023 Paper

A simulation study on photon-counting denoising based on subspace decomposition

Junru Ren, Ailong Cai, Ningning Liang, Yizhong Wang, Xinrui Zhang, Lei Li, Bin Yan

Proceedings Volume 12748, 127480E (2023) https://doi.org/10.1117/12.2689447

KEYWORDS: Image processing, Data processing, Denoising, Relative intensity noise, Data transmission, X-rays, Signal to noise ratio, Signal attenuation, Medical imaging

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Proceedings Article | 12 October 2022 Paper

LSTM-SENet: category decoding model with an attention mechanism

Penghui Ding, Chi Zhang, Linyuan Wang, Guoen Hu, Bin Yan, Li Tong

Proceedings Volume 12342, 123422M (2022) https://doi.org/10.1117/12.2644595

KEYWORDS: Visualization, Brain, Information visualization, Functional magnetic resonance imaging, Performance modeling, Neural networks, Visual cortex, Visual analytics

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Proceedings Article | 2 March 2022 Paper

Visual analysis of image features in CT reconstruction based on convolutional neural network

Linlin Zhu, Yu Han, Lei Li, Xiaoqi Xi, Bin Yan

Proceedings Volume 12158, 1215806 (2022) https://doi.org/10.1117/12.2626905

KEYWORDS: CT reconstruction, Image processing, Reconstruction algorithms, X-ray computed tomography, Visualization, Convolution, Computed tomography, Image analysis

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Proceedings Article | 12 March 2021 Paper

Projection drift alignment by acquisition time division for x-ray nanotomography

Xiaoqi Xi, Yu Han, Junzheng Zhang, Lei Li, Siyu Tan, Zhaole Yin, Zhaoying Sun, Bin Yan

Proceedings Volume 11763, 11763A1 (2021) https://doi.org/10.1117/12.2588343

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Proceedings Article | 5 November 2020 Paper

CT local reconstruction method based on truncated data extrapolation network

Yuying Du, Linlin Zhu, Xiaoqi Xi, Yu Han, Lei Li, Bin Yan

Proceedings Volume 11565, 1156512 (2020) https://doi.org/10.1117/12.2580364

KEYWORDS: Reconstruction algorithms, Head, Neural networks, Computed tomography, Abdomen, Image quality, X-ray computed tomography, Tomography, Image filtering

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Proceedings Article | 5 November 2020 Paper

Low dose x-ray CT image denoising via U-net in projection domain

Xiaofu Song, Linlin Zhu, Xiaoqi Xi, Yu Han, Lei Li, Zhiwei Feng, Mingwan Zhu, Guanyu Kang, Bin Yan

Proceedings Volume 11565, 1156513 (2020) https://doi.org/10.1117/12.2580369

KEYWORDS: X-ray computed tomography, Image quality, Convolution, Image processing, Computer programming, Computed tomography, Image denoising, Denoising, X-rays

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Proceedings Article | 18 December 2019 Paper

Fully convolutional networks based sinogram correction for metal artifact reduction

Linlin Zhu, Yu Han, Ziheng Li, Xiaoqi Xi, Lei Li, Bin Yan, Mingwan Zhu

Proceedings Volume 11342, 1134208 (2019) https://doi.org/10.1117/12.2542909

KEYWORDS: Metals, X-ray computed tomography, Convolution, Computed tomography, Image processing, Medical imaging, Nondestructive evaluation, Medicine

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Proceedings Article | 10 September 2019 Paper

Effect of tube setting variables on image quality in industrial x-ray computed tomography

Xiaoqi Xi, Zhaoying Sun, Yu Han, Lei Li, Bin Yan, Yongchao Li

Proceedings Volume 11113, 111131U (2019) https://doi.org/10.1117/12.2531796

KEYWORDS: Signal to noise ratio, Image quality, X-ray computed tomography, Modulation transfer functions, Spatial resolution, Computed tomography, CT reconstruction

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Proceedings Article | 28 May 2019 Paper

A sinogram inpainting method based on generative adversarial network for limited-angle computed tomography

Ziheng Li, Wenkun Zhang, Linyuan Wang, Ailong Cai, Ningning Liang, Bin Yan, Lei Li

Proceedings Volume 11072, 1107220 (2019) https://doi.org/10.1117/12.2533757

KEYWORDS: Computed tomography, Image restoration, Reconstruction algorithms, CT reconstruction, Gallium nitride, Image segmentation, Computer programming, Lithium, Image quality, Switching

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Proceedings Article | 28 May 2019 Paper

Projection super-resolution based on convolutional neural network for computed tomography

Chao Tang, Wenkun Zhang, Ziheng Li, Ailong Cai, Linyuan Wang, Lei Li, Ningning Liang, Bin Yan

Proceedings Volume 11072, 1107233 (2019) https://doi.org/10.1117/12.2533766

KEYWORDS: Super resolution, Computed tomography, X-ray computed tomography, Convolution, Convolutional neural networks, Image resolution, CT reconstruction, Image quality, 3D image processing

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Proceedings Article | 10 May 2019 Paper

Metal artifact reduction based on fully convolutional networks in CT image domain

Linlin Zhu, Yu Han, Lei Li, Yifu Xu, Xiaoqi Xi, Bin Yan, Kai Xiao

Proceedings Volume 11068, 110681U (2019) https://doi.org/10.1117/12.2524635

KEYWORDS: Metals, X-ray computed tomography, Computed tomography, Image processing, Convolution, Deconvolution, X-rays

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Proceedings Article | 12 December 2018 Paper

Effect of tube setting on image quality in industrial x-ray computed tomography

Zhaoying Sun, Lei Li, Xiaoqi Xi, Yu Han, Bin Yan, Lihui Rong, Sangsang Zhou

Proceedings Volume 10845, 108450A (2018) https://doi.org/10.1117/12.2504429

KEYWORDS: Image quality, Signal to noise ratio, X-ray computed tomography, X-ray imaging, X-rays, CT reconstruction, 3D image reconstruction, Image enhancement, Sun, Nondestructive evaluation

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Proceedings Article | 9 August 2018 Paper

Convolutional neural network for automated histopathological grading of breast cancer on digital mammograms

Jinjin Hai, Hongna Tan, Lei Zeng, Minghui Wu, Kai Qiao, Jingbo Xu, Dapeng Shi, Bin Yan

Proceedings Volume 10806, 108065I (2018) https://doi.org/10.1117/12.2503019

KEYWORDS: Breast cancer, Mammography, Tumors, Convolutional neural networks, Cancer, Feature extraction, Breast

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Proceedings Article | 24 October 2017 Paper

An optimized acquisition approach exploiting geometrical calibration in x-ray cone-beam computed tomography

Kai Xiao, Yu Han, Xiaoqi Xi, Haibing Bu, Lei Li, Bin Yan, Yanmin Sun, Jingyu Wang, Jianbang Liu

Proceedings Volume 10462, 104624Q (2017) https://doi.org/10.1117/12.2285578

KEYWORDS: Calibration, X-ray computed tomography, X-ray imaging, Image restoration

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X-ray cone-beam computed tomography, featuring high precision and fast-imaging speed, has been widely used in industrial non-destructive testing applications for the three dimensional visualization of internal structures. Due to mechanical imperfections, geometric calibrations are imperative to high quality image reconstruction. Currently, the twoball phantom-based calibration procedures exploiting the projection trajectories of the phantoms are the most commonly used approach for the estimation of the geometrical parameters and the calibration of CT system. However, an additional scan needs to be performed, even after each object acquisition when lack of system reproducibility, leading to multiplied calibration times. The emphasis of this paper is to optimize the process of acquisition in cone-beam CT imaging with minimal time, based on the understanding of the determination of the ball position in typical phantom-based geometric calibration algorithms. An applicable condition of the calibration algorithm for simultaneously scanning objects and calibration phantoms is proposed and demonstrated, which is that the minimum projection value of the scanned object needs to be at least 100 counts higher than those of the calibration phantom, with consideration of the system noise. The CT experiments are based on a laboratory industrial cone-beam CT system with a micro-focus x-ray tube (Thales Hawkeye 130) and a flat panel detector (Thales Pixium RF4343). Objects imaged are chosen with a wide projection value range, from low-Z watermelon seeds and high-Z materials, including a standard Micro CT Bar Pattern Phantom (QRM) for image quality assessment. In these experiments, objects, as well as two-ball phantoms, both placed in the field of view without overlapping in the vertical direction, are projected over 360 degrees, instead of scanning the calibration phantoms separately. Hence, the true geometrical relationship is resolved utilizing the two-ball algorithm. Both simulation and experimental results confirm that the calculated geometrical parameters will not be affected by the objects as long as their projection value difference meeting the requirements above. And the reconstruction image quality is almost the same with those by an independent calibration. Compared to the traditional application of the phantombased geometrical calibration method, the novel approach presented in this paper has obvious advantages from an imaging perspective, saving acquisition time and eliminating the undesired influence from the operation staff for the same cost.

Proceedings Article | 21 July 2017 Paper

Object detection via eye tracking and fringe restraint

Fei Pan, Hanming Zhang, Ying Zeng, Li Tong, Bin Yan

Proceedings Volume 10420, 104200R (2017) https://doi.org/10.1117/12.2282087

KEYWORDS: Eye, Object recognition, Eye tracking

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Proceedings Article | 25 October 2016 Paper

Implementation on GPU-based acceleration of the m-line reconstruction algorithm for circle-plus-line trajectory computed tomography

Zengguang Li, Xiaoqi Xi, Yu Han, Bin Yan, Lei Li

Proceedings Volume 10156, 101561Z (2016) https://doi.org/10.1117/12.2247555

KEYWORDS: Reconstruction algorithms, Computed tomography, Cones

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The circle-plus-line trajectory satisfies the exact reconstruction data sufficiency condition, which can be applied in C-arm X-ray Computed Tomography (CT) system to increase reconstruction image quality in a large cone angle. The m-line reconstruction algorithm is adopted for this trajectory. The selection of the direction of m-lines is quite flexible and the m-line algorithm needs less data for accurate reconstruction compared with FDK-type algorithms. However, the computation complexity of the algorithm is very large to obtain efficient serial processing calculations. The reconstruction speed has become an important issue which limits its practical applications. Therefore, the acceleration of the algorithm has great meanings. Compared with other hardware accelerations, the graphics processing unit (GPU) has become the mainstream in the CT image reconstruction. GPU acceleration has achieved a better acceleration effect in FDK-type algorithms. But the implementation of the m-line algorithm’s acceleration for the circle-plus-line trajectory is different from the FDK algorithm. The parallelism of the circular-plus-line algorithm needs to be analyzed to design the appropriate acceleration strategy. The implementation can be divided into the following steps. First, selecting m-lines to cover the entire object to be rebuilt; second, calculating differentiated back projection of the point on the m-lines; third, performing Hilbert filtering along the m-line direction; finally, the m-line reconstruction results need to be three-dimensional-resembled and then obtain the Cartesian coordinate reconstruction results. In this paper, we design the reasonable GPU acceleration strategies for each step to improve the reconstruction speed as much as possible. The main contribution is to design an appropriate acceleration strategy for the circle-plus-line trajectory m-line reconstruction algorithm. Sheep-Logan phantom is used to simulate the experiment on a single K20 GPU. The development environment trajectory, using CPU and the paper’s GPU acceleration strategy, respectively. The experimental results show considerable reconstruction image quality, and the reconstruction acceleration ratio can reach 620 times.

Proceedings Article | 19 October 2016 Paper

Model-based x-ray energy spectrum estimation algorithm from CT scanning data with spectrum filter

Lei Li, Lin-Yuan Wang, Bin Yan

Proceedings Volume 10155, 101553S (2016) https://doi.org/10.1117/12.2247466

KEYWORDS: Data modeling, Model-based design, X-ray computed tomography, Optical filters, Computed tomography

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Proceedings Article | 19 October 2016 Paper

Beam hardening correction for interior tomography based on exponential formed model and radon inversion transform

Siyu Chen, Hanming Zhang, Lei Li, Xiaoqi Xi, Yu Han, Bin Yan

Proceedings Volume 10155, 101553D (2016) https://doi.org/10.1117/12.2247372

KEYWORDS: Tomography, Radon, Radon transform

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Proceedings Article | 29 August 2016 Paper

CT reconstruction from sparse projections based on extrapolation in Fourier domain

Zhao Jin, Lei Li, Linyuan Wang, Zengguang Li, Hanming Zhang, Bin Yan

Proceedings Volume 10033, 100334I (2016) https://doi.org/10.1117/12.2244034

KEYWORDS: CT reconstruction, Fourier transforms, Reconstruction algorithms, X-ray computed tomography, Detection and tracking algorithms, Image quality, Image restoration, Algorithm development, Expectation maximization algorithms, Sensors

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Proceedings Article | 29 August 2016 Paper

A filter design method for beam hardening correction in middle-energy x-ray computed tomography

Siyu Chen, Xiaoqi Xi, Lei Li, Leifei Luo, Yu Han, Jingyu Wang, Bin Yan

Proceedings Volume 10033, 1003352 (2016) https://doi.org/10.1117/12.2245152

KEYWORDS: X-ray computed tomography, X-rays, Optical filters, Signal attenuation, Photons, Metals, Monte Carlo methods, Aluminum, Mass attenuation coefficient, Copper

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Proceedings Article | 29 August 2016 Paper

A filter design method for beam hardening correction in middle-energy x-ray computed tomography

Siyu Chen, Xiaoqi Xi, Lei Li, Leifei Luo, Yu Han, Jingyu Wang, Bin Yan

Proceedings Volume 10033, 1003352 (2016) https://doi.org/10.1117/12.2245152

KEYWORDS: X-ray computed tomography, X-rays, Optical filters, Signal attenuation, Photons, Metals, Monte Carlo methods, Aluminum, Mass attenuation coefficient, Copper

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Showing 5 of 22 publications

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