Prof. Yuxiang Xing Profile

Prof. Yuxiang Xing

Professor at Tsinghua University

SPIE Involvement:

Conference Program Committee | Author

Publications (44)

Proceedings Article | 1 April 2024 Presentation + Paper

An alternative Bayesian reconstruction of sparse-view CT by optimizing deep learning parameters

Changyu Chen, Zhiqiang Chen, Li Zhang, Yuxiang Xing

Proceedings Volume 12925, 1292518 (2024) https://doi.org/10.1117/12.3008509

KEYWORDS: Image restoration, Image quality, Deep learning, Computed tomography, Anatomy, Data modeling, CT reconstruction, Image enhancement

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Proceedings Article | 1 April 2024 Poster + Paper

Automatic tuning of CT imaging parameters with reinforcement learning

Haoyu Zhang, Le Shen, Yuxiang Xing

Proceedings Volume 12925, 1292521 (2024) https://doi.org/10.1117/12.3006018

KEYWORDS: Computed tomography, Image quality, Radiotherapy, X-ray computed tomography, Tumors, Medical imaging

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Proceedings Article | 1 April 2024 Presentation + Paper

Physics-based modeling of energy threshold induced spectral inconsistency and its adaptive correction scheme for photon-counting CT

Yuting Chen, Yuxiang Xing, Hewei Gao

Proceedings Volume 12925, 1292503 (2024) https://doi.org/10.1117/12.3007384

KEYWORDS: Calibration, Sensors, Modeling, Distortion, Photon counting, Image restoration, Spectral models, Monte Carlo methods, Image segmentation, X-ray sources

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Proceedings Article | 7 April 2023 Poster + Paper

An edge-preserving mean curvature regularization for CT reconstruction

Le Shen, Yuxiang Xing, Li Zhang, Sen Wang

Proceedings Volume 12463, 1246346 (2023) https://doi.org/10.1117/12.2655207

KEYWORDS: CT reconstruction, Reconstruction algorithms, Diffusion, Image quality, Computed tomography

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Proceedings Article | 7 April 2023 Poster

Comparison of two simulation methods in x-ray-grating-based dark-field imaging for lung tissues

Peiyuan Guo, Li Zhang, Hewei Gao, Yuxiang Xing, Zhentian Wang, Zhiqiang Chen

Proceedings Volume 12463, 124634Y (2023) https://doi.org/10.1117/12.2651557

KEYWORDS: Lung, Tissues, X-rays, X-ray imaging, Diffusion, Visibility, Imaging systems, Wavefronts, Wave propagation, Projection systems

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Proceedings Article | 18 October 2022 Open Access

An analytical Prj2CR covariance estimation method for iterative CT reconstruction algorithms

Xiaoyue Guo, Li Zhang, Yuxiang Xing

Proceedings Volume 12304, 123041J (2022) https://doi.org/10.1117/12.2647146

KEYWORDS: Signal to noise ratio, Statistical analysis, Image analysis, CT reconstruction, Image quality

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Image mean and covariance required for a model observer are usually calculated by the statistical method using image samples, which is hard to acquire in reality. Although some analytical methods are proposed to estimate image covariance from a single projection, these methods are of high computational cost for large-dimensional images (e.g., 512×512), and images of large dimension are commonly required. Considering the covariance used for a model observer is the covariance of the channel response, whose dimension is much smaller than the image covariance, we aim to obtain the covariance of small-dimensional channel response directly from its projection. Channel filters are applied to the analytical projection to image (Prj2Img) covariance estimation method to derive the analytical projection to channel response (Prj2CR) covariance estimation method, which successfully reduces the computational cost and connects the covariance of projection and channel response. In addition, a transition matrix is introduced in Prj2CR method to stabilize the connections. The transition matrix mainly depends on channel filters, not the system, phantom, and reconstruction algorithm, which means it can be calibrated by small-dimensional reconstructions and then applied to any situation with a same channel filter. We validate the feasibility and utility of the proposed Prj2CR method by simulations. 128×128 reconstructions from qGGMRF-WLS are adopted for calibration, while 512×512 reconstructions are used for validation. SNR of CHO is chosen as the figure of merit for performance evaluation, and the covariance estimated by 290 image samples are used as the reference. Results show that the SNR by the Prj2CR method is within 95% confidence interval of the SNR* by 290 image samples, indicating that the proposed method accords with statistical method. The Prj2CR method may be beneficial for subjective image quality assessment since it only needs a single sample of projection and has low computational cost.

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Proceedings Article | 15 February 2021 Presentation + Paper

Training a low-dose CT denoising network with only low-dose CT dataset: comparison of DDLN and Noise2Void

Kaichao Liang, Li Zhang, Yuxiang Xing

Proceedings Volume 11595, 115950I (2021) https://doi.org/10.1117/12.2581922

KEYWORDS: Denoising, X-rays, Visualization, Statistical analysis, Machine learning, Image visualization, Computed tomography

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Proceedings Article | 15 February 2021 Poster + Presentation + Paper

3D image reconstruction for symmetric-geometry CT with linearly distributed source and detector in a stationary configuration

Tao Zhang, Hewei Gao, Li Zhang, Yuxiang Xing, Zhiqiang Chen

Proceedings Volume 11595, 115953S (2021) https://doi.org/10.1117/12.2581070

KEYWORDS: Sensors, Computed tomography, 3D image processing, Image restoration, Tomography, Reconstruction algorithms, X-ray sources, X-ray computed tomography, Temporal resolution, Manufacturing

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Proceedings Article | 16 March 2020 Presentation + Paper

A deep learning reconstruction method for sparsely spaced multidetector helical CT using cascaded dual-domain CNN

Ao Zheng, Hewei Gao, Li Zhang, Yuxiang Xing

Proceedings Volume 11312, 1131208 (2020) https://doi.org/10.1117/12.2548774

KEYWORDS: Sensors, Image quality, Computed tomography, Lung, Fluctuations and noise, Reconstruction algorithms, Network architectures, Photons

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Proceedings Article | 16 March 2020 Paper

A stopping criterion for iterative reconstruction of x-ray computed tomography

Yirong Yang, Kaichao Liang, Yuxiang Xing

Proceedings Volume 11312, 113123M (2020) https://doi.org/10.1117/12.2549379

KEYWORDS: Image quality, X-ray computed tomography, Reconstruction algorithms, Data modeling, X-rays, Signal to noise ratio

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Proceedings Article | 31 July 2019 Paper

Second glance framework (secG): enhanced ulcer detection with deep learning on a large wireless capsule endoscopy dataset

Sen Wang, Yuxiang Xing, Li Zhang, Hewei Gao, Hao Zhang

Proceedings Volume 11198, 111980V (2019) https://doi.org/10.1117/12.2540456

KEYWORDS: Endoscopy, Machine learning, Computer aided diagnosis and therapy, Image classification, Machine vision

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Proceedings Article | 28 May 2019 Paper

Contrast-medium anisotropy-aware tensor total variation model for robust cerebral perfusion CT reconstruction with weak radiation: a preliminary study

Yuanke Zhang, Dong Zeng, Sui Li, Yuting Liao, Zhaoying Bian, Yongbo Wang, Ji He, Xiao Jia, Yuxiang Xing, Deyu Meng, Hongbing Lu, Jianhua Ma

Proceedings Volume 11072, 110720A (2019) https://doi.org/10.1117/12.2534360

KEYWORDS: Reconstruction algorithms, Algorithm development, Computed tomography, CT reconstruction, Matrices, Optimization (mathematics), Biomedical engineering, Mathematical modeling

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Proceedings Article | 28 May 2019 Paper

Inverse-geometry CT with linearly distributed source and detector: stationary configuration and direct filtered-backprojection reconstruction

Tao Zhang, Yuxiang Xing, Hewei Gao, Zhiqiang Chen, Li Zhang

Proceedings Volume 11072, 110722T (2019) https://doi.org/10.1117/12.2534817

KEYWORDS: Sensors, Reconstruction algorithms, X-ray computed tomography, Detection and tracking algorithms, Detector arrays, Linear filtering, Computed tomography, Algorithm development, Imaging systems, Head

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Proceedings Article | 28 May 2019 Paper

Synthesize monochromatic images in spectral CT by dual-domain deep learning

Chuqing Feng, Zhiqiang Chen, Kejun Kang, Yuxiang Xing

Proceedings Volume 11072, 1107229 (2019) https://doi.org/10.1117/12.2534921

KEYWORDS: Neural networks, X-ray computed tomography, Calibration, Computed tomography, Photon counting, CT reconstruction

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Proceedings Article | 28 May 2019 Paper

Information retrieval in x-ray imaging with grating interferometry using convolution neural network

Chengpeng Wu, Yuxiang Xing, Hewei Gao, Li Zhang, Xinbin Li, Shengping Wang, Weijun Peng

Proceedings Volume 11072, 1107231 (2019) https://doi.org/10.1117/12.2534270

KEYWORDS: Convolution, Neural networks, X-ray imaging, Interferometry, Phase contrast, X-rays, Deconvolution, Breast, Image retrieval

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Proceedings Article | 1 March 2019 Paper

Dental and maxillofacial cone beam computed tomography absorbed dose distribution calculation by GEANT4

Shumei Jia, Hewei Gao, Li Zhang, YuXiang Xing, JunZheng Zheng

Proceedings Volume 10948, 109483K (2019) https://doi.org/10.1117/12.2512967

KEYWORDS: X-rays, Computed tomography, X-ray computed tomography, X-ray sources, Radiology

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SPIE Journal Paper | 22 October 2018

Fully connected neural network for virtual monochromatic imaging in spectral computed tomography

Chuqing Feng, Kejun Kang, Yuxiang Xing

JMI, Vol. 6, Issue 01, 011006, (October 2018) https://doi.org/10.1117/12.10.1117/1.JMI.6.1.011006

KEYWORDS: Neural networks, Signal attenuation, Sensors, Sodium, Systems modeling, Spectral computed tomography, CT reconstruction, Computed tomography, Neurons, Calibration

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Proceedings Article | 12 March 2018 Paper

Low dose CT reconstruction with nonlocal means-based prior predicted from normal-dose CT database

Junyan Rong, Yuanke Zhang, Tianshuai Liu, Peng Gao, Yuxiang Xing, Zhengrong Liang, Hongbing Lu

Proceedings Volume 10578, 1057821 (2018) https://doi.org/10.1117/12.2294051

KEYWORDS: Databases, Computed tomography, Image quality, Neodymium, CT reconstruction, Reconstruction algorithms, Image restoration, Biomedical engineering, Data modeling

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Proceedings Article | 9 March 2018 Presentation + Paper

Improve angular resolution for sparse-view CT with residual convolutional neural network

Kaichao Liang, Hongkai Yang, Kejun Kang, Yuxiang Xing

Proceedings Volume 10573, 105731K (2018) https://doi.org/10.1117/12.2293319

KEYWORDS: X-ray computed tomography, Convolutional neural networks, CT reconstruction, Reconstruction algorithms, Computed tomography, Medical imaging, Neural networks

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Proceedings Article | 9 March 2018 Paper

Low-dose CT reconstruction with MRF prior predicted from patch samples of normal-dose CT database

Junyan Rong, Yuanke Zhang, Yuxiang Xing, Peng Gao, Tianshuai Liu, Zhengrong Liang, Hongbing Lu

Proceedings Volume 10573, 1057334 (2018) https://doi.org/10.1117/12.2293446

KEYWORDS: Databases, Reconstruction algorithms, Computed tomography, Statistical modeling, CT reconstruction, Tissues, Data modeling

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Proceedings Article | 9 March 2018 Paper

A multi-energy material decomposition method for spectral CT using neural network

Chuqing Feng, Kejun Kang, Yuxiang Xing

Proceedings Volume 10573, 105734J (2018) https://doi.org/10.1117/12.2294611

KEYWORDS: Neural networks, CT reconstruction, Image processing, Image denoising

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Proceedings Article | 9 March 2018 Paper

A feasibility study of extracting tissue textures from a previous normal-dose CT database as prior for Bayesian reconstruction of current ultra-low-dose CT images

Yongfeng Gao, Zhengrong Liang, Yuxiang Xing, Hao Zhang, Jianhua Ma, Hongbing Lu, Lihong Li, Bo Chen, Marc Pomeroy, William Moore

Proceedings Volume 10573, 105733C (2018) https://doi.org/10.1117/12.2293854

KEYWORDS: Magnetorheological finishing, Tissues, X-ray computed tomography, Lung, Image segmentation, Databases, X-rays, Reconstruction algorithms, Computed tomography, CT reconstruction

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Tremendous research efforts have been devoted to lower the X-ray radiation exposure to the patient in order to expand the utility of computed tomography (CT), particularly to pediatric imaging and population-based screening. When the exposure dosage goes down, both the X-ray quanta fluctuation and the system electronic background noise become significant factors affecting the image quality. Conventional edge-preserving noise smoothing would sacrifice tissue textures and compromise the clinical tasks. To relieve these challenges, this work models the noise problem by pre-log shifted Poisson statistics and extracts tissue textures from previous normal-dose CT scans as prior knowledge for texturepreserving Bayesian reconstruction of current ultralow-dose CT images. The pre-log shift Poisson model considers accurately both the X-ray quanta fluctuation and the system electronic noise while the prior knowledge of tissue textures removes the limitation of the conventional edge-preserving noise smoothing. The Bayesian reconstruction was tested by experimental studies. One patient chest scan was selected from a database of 133 patients’ scans at 100mAs/120kVp normal-dose level. From the selected patient scan, ultralow-dose data was simulated at 5mAs/120kVp level. The other 132 normal-dose scans were grouped according to how close their lung tissue texture patterns are from that of the selected patient scan. The tissue textures of each group were used to reconstruct the ultralow-dose scan by the Bayesian algorithm. The closest group to the selected patient produced almost identical results to the reconstruction when the tissue textures of the selected patient’s normal-dose scan were used, indicating the feasibility of extracting tissue textures from a previous normal-dose database to reconstruct any current ultralow-dose CT image. Since the Bayesian reconstruction can be time consuming, this work further investigates a strategy to efficiently store the projection matrix rather than computing the line integrals on-flight. This strategy accelerated the computing speed by more than 18 times.

Proceedings Article | 5 March 2018 Paper

CT artifact reduction via U-net CNN

C. Zhang, Y. Xing

Proceedings Volume 10574, 105741R (2018) https://doi.org/10.1117/12.2293903

KEYWORDS: Convolutional neural networks, Computed tomography, Network architectures, X-ray computed tomography, Neural networks

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Purpose: Our preliminary study showed us the capability of a deep learning neural network (DLNN) based method to eliminate a specific type of artifact in CT images. This work is to comprehensively study the applicability of a U-net CNN architecture in improving the image quality of CT reconstructions by respectively testing its performance in various artifact removal tasks. Methods: A U-net architecture is trained by a big dataset of contaminated and expected image pairs. The expected images known as reference images are acquired from groundtruths or using superior imaging system. A proper initialization of network parameters, a careful normalization of original data and a residual learning objective are incoorprated into the framework to boost training convergence. Both numerical and real data studies are conducted to validate this method. Results: In numerical studies, we found that the DLNN-based artifact reduction is powerful and can work well in reducing nearly all type artifacts and recover detailed structrual information in low-quliaty images (e.g. plain FBP reconstructions) if the network is trained with groundtruths provided. In real situations where groundtruth is not available, the proposed method can characterize the discrepancy between contaminated data and higher-quality reference labels produced by other techniques, mimicking their capability of reducing artifacts. Generalization to disjointed data is also examined using testing data. All results show that the DLNN framework can be applied to various artifact reduction tasks and outperforms conventional methods with shorter runtime. Conclusion: This work gained promising results of the U-net network architecture successfully characterizing both global and local artifact patterns. By forward propagating contaminated images through the trained network, undesired artifacts can be greatly reduced while structrual information maintained for an input of CT image. It should be noted that the proposed deep network should be trained independently for each specific case.

Proceedings Article | 7 September 2017 Presentation + Paper

A multi-purpose readout electronics for CdTe and CZT detectors for x-ray imaging applications

X. B. Yue, Z. Deng, Y. X. Xing, Y. N. Liu

Proceedings Volume 10393, 103930E (2017) https://doi.org/10.1117/12.2273857

KEYWORDS: Digital signal processing, Sensors, Electronics, X-ray imaging, Signal processing, X-ray detectors, Inspection, Analog electronics, Semiconductors, X-rays

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Proceedings Article | 9 March 2017 Paper

A TV-constrained decomposition method for spectral CT

Xiaoyue Guo, Li Zhang, Yuxiang Xing

Proceedings Volume 10132, 101323F (2017) https://doi.org/10.1117/12.2254593

KEYWORDS: Computed tomography, Computer simulations, Signal to noise ratio, Image quality, Medicine, Nondestructive evaluation, Inspection, Imaging systems, Dual energy imaging, Visualization, Bone, Tissues, Distributed interactive simulations, Calcium, Denoising

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Proceedings Article | 30 March 2016 Paper

Reconstruction of limited-angle dual-energy CT using mutual learning and cross-estimation (MLCE)

Huayu Zhang, Yuxiang Xing

Proceedings Volume 9783, 978344 (2016) https://doi.org/10.1117/12.2211224

KEYWORDS: Signal attenuation, Artificial neural networks, X-ray computed tomography, Dual energy imaging, X-ray sources, Sensors, Data acquisition, Cones, Image restoration, Single photon emission computed tomography, Neural networks, X-rays, Reconstruction algorithms, Associative arrays, Chromium, Rhodium

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Proceedings Article | 22 March 2016 Paper

Limited-angle multi-energy CT using joint clustering prior and sparsity regularization

Huayu Zhang, Yuxiang Xing

Proceedings Volume 9783, 97830D (2016) https://doi.org/10.1117/12.2214312

KEYWORDS: Compressed sensing, X-ray computed tomography, Reconstruction algorithms, Coherence (optics), Numerical simulations, X-rays, Dual energy imaging, Tissues, Teeth, Sensors, Data acquisition

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Proceedings Article | 17 October 2012 Paper

Anisotropic total variation minimization method for limited-angle CT reconstruction

Xin Jin, Liang Li, Zhiqiang Chen, Li Zhang, Yuxiang Xing

Proceedings Volume 8506, 85061C (2012) https://doi.org/10.1117/12.930339

KEYWORDS: CT reconstruction, X-ray computed tomography, Image processing, Compressed sensing, Computed tomography, Reconstruction algorithms, X-rays, Radon transform, Rutherfordium, Lithium

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Proceedings Article | 17 October 2012 Paper

Self-calibration for a multi-segment straight-line trajectory CT using an invariant moment

Xiyun Li, Zhiqiang Chen, Xiaolei Jiang, Yuxiang Xing

Proceedings Volume 8506, 85061K (2012) https://doi.org/10.1117/12.928355

KEYWORDS: Calibration, Image segmentation, Computed tomography, X-ray computed tomography, Sensors, X-ray sources, Signal attenuation, Reconstruction algorithms, X-rays, CT reconstruction

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Proceedings Article | 17 October 2012 Paper

A noise variance estimation approach for CT

Le Shen, Xin Jin, Yuxiang Xing

Proceedings Volume 8506, 85061M (2012) https://doi.org/10.1117/12.928828

KEYWORDS: Sensors, Reconstruction algorithms, Denoising, X-rays, Signal detection, Bone, Expectation maximization algorithms, Algorithm development, Head, Optical spheres

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Proceedings Article | 24 February 2010 Paper

Optimization of detector thickness for single slice helical CT with ROC study

Cheng Shi, Yuxiang Xing

Proceedings Volume 7627, 76271D (2010) https://doi.org/10.1117/12.844611

KEYWORDS: Sensors, Signal to noise ratio, Computed tomography, X-ray computed tomography, Numerical simulations, 3D image processing, 3D displays, X-rays, Diagnostics, Performance modeling

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Proceedings Article | 18 March 2008 Paper

MLSD-OSEM reconstruction algorithm for cosmic ray muon radiography

Yuanyuan Liu, Ziran Zhao, Zhiqiang Chen, Li Zhang, Yuxiang Xing

Proceedings Volume 6913, 691331 (2008) https://doi.org/10.1117/12.769803

KEYWORDS: Reconstruction algorithms, Muons, Scattering, Radiography, Optical spheres, Monte Carlo methods, Atmospheric particles, Numerical simulations, Particles, Expectation maximization algorithms

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Proceedings Article | 18 March 2008 Paper

Accelerate helical cone-beam CT with graphics hardware

Wenyuan Bi, Zhiqiang Chen, Li Zhang, Yuxiang Xing

Proceedings Volume 6913, 69132T (2008) https://doi.org/10.1117/12.770121

KEYWORDS: Reconstruction algorithms, Visualization, X-ray computed tomography, Sensors, Lithium, Convolution, Detection and tracking algorithms, Physics, Volume rendering, 3D image reconstruction

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Proceedings Article | 18 March 2008 Paper

A study on projection distribution of few-view reconstruction with total variation constraint

Li Zhang, Xinhui Duan, Yuxiang Xing, Zhiqiang Chen, Jianping Cheng

Proceedings Volume 6913, 69132W (2008) https://doi.org/10.1117/12.769779

KEYWORDS: Reconstruction algorithms, Tomography, Fluctuations and noise, Imaging systems, Computed tomography, Image quality, Image processing, Image restoration, Lithium, Physics

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Proceedings Article | 18 March 2008 Paper

PDE regularization for Bayesian reconstruction of emission tomography

Zhentian Wang, Li Zhang, Yuxiang Xing, Ziran Zhao

Proceedings Volume 6913, 69132X (2008) https://doi.org/10.1117/12.769742

KEYWORDS: Image segmentation, Reconstruction algorithms, Tomography, Image processing algorithms and systems, Image processing, Anisotropic diffusion, Denoising, Partial differential equations, Image restoration, Signal attenuation

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SPIE Journal Paper | 1 July 2007

Volumetric imaging from a multisegment straight-line trajectory and a practical reconstruction algorithm

Hewei Gao, Li Zhang, Yuxiang Xing, Zhiqiang Chen, Jian Zhang, Jianping Cheng

OE, Vol. 46, Issue 07, 077004, (July 2007) https://doi.org/10.1117/12.10.1117/1.2756823

KEYWORDS: Reconstruction algorithms, Sensors, Image segmentation, Optical engineering, Inspection, X-ray computed tomography, Scanners, Computed tomography, Imaging systems, Detection and tracking algorithms

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SPIE Journal Paper | 1 May 2007

Direct filtered-backprojection-type reconstruction from a straight-line trajectory

Hewei Gao, Li Zhang, Zhiqiang Chen, Yuxiang Xing, Jianping Cheng, Zhihua Qi

OE, Vol. 46, Issue 05, 057003, (May 2007) https://doi.org/10.1117/12.10.1117/1.2739624

KEYWORDS: Sensors, Reconstruction algorithms, Detection and tracking algorithms, Optical filters, Optical engineering, Computed tomography, Spatial resolution, Image quality, Inspection, Image filtering

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Proceedings Article | 17 March 2006 Paper

Metal artifacts reduction in CT images through Euler’s elastica and curvature based sinogram inpainting

Jianwei Gu, Li Zhang, Guoqiang Yu, Yuxiang Xing, Zhiqiang Chen

Proceedings Volume 6144, 614465 (2006) https://doi.org/10.1117/12.650137

KEYWORDS: Metals, X-ray computed tomography, Lithium, Reconstruction algorithms, X-rays, Numerical simulations, Signal attenuation, X-ray imaging, Image interpolation, Image quality

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Proceedings Article | 2 March 2006 Paper

A backprojection filtered image reconstruction algorithm for circular cone-beam CT

Liang Li, Zhiqiang Chen, Li Zhang, Yuxiang Xing, Kejun Kang

Proceedings Volume 6142, 61424N (2006) https://doi.org/10.1117/12.652520

KEYWORDS: Reconstruction algorithms, Detection and tracking algorithms, Sensors, Image segmentation, Image filtering, X-rays, X-ray computed tomography, X-ray sources, Image processing algorithms and systems, Image restoration

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Proceedings Article | 2 March 2006 Paper

Accelerating 3D cone beam T-FDK algorithm on commodity PC graphics hardware

Zhisheng Dai, Zhiqiang Chen, Yuxiang Xing, Li Zhang

Proceedings Volume 6142, 61424P (2006) https://doi.org/10.1117/12.654121

KEYWORDS: Reconstruction algorithms, Volume rendering, Visualization, Image quality, Detection and tracking algorithms, Sensors, Algorithm development, Data processing, 3D image processing, 3D image reconstruction

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Proceedings Article | 17 February 2006 Paper

Variational segmentation of x-ray image with overlapped objects

Guoqiang Yu, Li Zhang, Jin Zhang, Yuxiang Xing, Hewei Gao

Proceedings Volume 6064, 60640W (2006) https://doi.org/10.1117/12.650449

KEYWORDS: Image segmentation, X-rays, X-ray imaging, Visual process modeling, Image processing, Image processing algorithms and systems, Image filtering, Machine vision, Computer vision technology, Chemical species

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Proceedings Article | 2 February 2006 Paper

Cupping artifacts analysis and correction for a FPD-based cone-beam CT

Li Zhang, Hewei Gao, Shuanglei Li, Zhiqiang Chen, Yuxiang Xing

Proceedings Volume 6065, 60650Z (2006) https://doi.org/10.1117/12.650503

KEYWORDS: X-rays, Sensors, Convolution, Iron, Lead, X-ray detectors, Image quality, X-ray computed tomography, CT reconstruction, X-ray sources

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Proceedings Article | 16 January 2006 Paper

Robust feature extraction for character recognition based on binary images

Lijun Wang, Li Zhang, Yuxiang Xing, Zhiming Wang, Hewei Gao

Proceedings Volume 6067, 606708 (2006) https://doi.org/10.1117/12.650386

KEYWORDS: Detection and tracking algorithms, Feature extraction, Curium, Optical character recognition, Binary data, Image processing, Databases, Neurons, Solids, Optical pattern recognition

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Proceedings Article | 5 June 2003 Paper

Adaptive noise reduction toward low-dose computed tomography

Hongbing Lu, Xiang Li, Lihong Li, Dongqing Chen, Yuxiang Xing, Jiang Hsieh, Zhengrong Liang

Proceedings Volume 5030, (2003) https://doi.org/10.1117/12.480374

KEYWORDS: Image filtering, Computed tomography, Signal to noise ratio, Digital filtering, Denoising, X-ray computed tomography, Nonlinear filtering, Smoothing, Gaussian filters, Image restoration

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