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Estimation-free spatial-domain image reconstruction of structured illumination microscopy

Journal of Innovative Optical Health Sciences 2024年第2期17卷 45-58页

作者： Xiaoyan Li Shijie Tu Yile Sun Yubing Han Xiang Hao Cuifang kuang Xu Liu State Key Laboratory of Extreme Photonics and Instrumentation College of Optical Science and Engineering Zhejiang University Hangzhou 310027P.R.China ZJU-Hangzhou Global Scientific and Technological Innovation Center Hangzhou 311200 P.R.China Collaborative Innovation Center of Extreme Optics Shanxi University TaiyuanShanxi 030006P.R.China A dvanced Biomedical Imaging Facility-Wuhan National Laboratory for Optoelectronics Huazhong University of Science and Technology WuhanHubei 430074P.R.China

Structured illumination microscopy(SIM)achieves super-resolution(SR)by modulating the high-frequency information of the sample into the passband of the optical system and subsequent image reconstruction.The traditional Wiener-filtering-based reconstruction algorithm operates in the Fourier domain,it requires prior knowledge of the sinusoidal illumination patterns which makes the time-consuming procedure of parameter estimation to raw datasets necessary,besides,the parameter estimation is sensitive to noise or aberration-induced pattern distortion which leads to reconstruction artifacts.Here,we propose a spatial-domain image reconstruction method that does not require parameter estimation but calculates patterns from raw datasets,and a reconstructed image can be obtained just by calculating the spatial covariance of differential calculated patterns and differential filtered datasets(the notch filtering operation is performed to the raw datasets for attenuating and compensating the optical transfer function(OTF)).Experiments on reconstructing raw datasets including nonbiological,biological,and simulated samples demonstrate that our method has SR capability,high reconstruction speed,and high robustness to aberration and noise.

关键词： Structured illumination microscopy image reconstruction spatial domain digital micromirror device(DMD)

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Triple-path feature transform network for ring-array photoacoustic tomography image reconstruction

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Journal of Innovative Optical Health Sciences 2024年第3期17卷 23-40页

作者： Lingyu Ma Zezheng Qin Yiming Ma Mingjian Sun School of Astronautics Harbin Institute of Technology HarbinHeilongjiang 150000P.R.China

Photoacoustic imaging(PAI)is a noninvasive emerging imaging method based on the photoacoustic effect,which provides necessary assistance for medical diagnosis.It has the characteristics of large imaging depth and high contrast.However,limited by the equipment cost and reconstruction time requirements,the existing PAI systems distributed with annular array transducers are difficult to take into account both the image quality and the imaging speed.In this paper,a triple-path feature transform network(TFT-Net)for ring-array photoacoustic tomography is proposed to enhance the imaging quality from limited-view and sparse measurement data.Specifically,the network combines the raw photoacoustic pressure signals and conventional linear reconstruction images as input data,and takes the photoacoustic physical model as a prior information to guide the reconstruction process.In addition,to enhance the ability of extracting signal features,the residual block and squeeze and excitation block are introduced into the TFT-Net.For further efficient reconstruction,the final output of photoacoustic signals uses‘filter-then-upsample’operation with a pixel-shuffle multiplexer and a max out module.Experiment results on simulated and in-vivo data demonstrate that the constructed TFT-Net can restore the target boundary clearly,reduce background noise,and realize fast and high-quality photoacoustic image reconstruction of limited view with sparse sampling.

关键词： Deep learning feature transformation image reconstruction limited-view measurement photoacoustic tomography.

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image reconstruction for the coded aperture system in nuclear safety and security using a Monte Carlo-based system matrix

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辐射探测技术与方法(英文) 2023年第2期7卷 263-270页

作者： Yue Yu Xiaoli Sun Zhiming Zhang Shuangquan Liu Xiuzuo Liang Daowu Li Lei Shuai Tingting Hu Long Wei Beijing Engineering Research Center of Radiographic Techniques and Equipment Institute of High Energy PhysicsChinese Academy of SciencesBeijing100049China School of Nuclear Science and Technology University of Chinese Academy of SciencesBeijing100049China Jinan Laboratory of Applied Nuclear Science Jinan250131China

Purpose Accurate localization of radioactive materials is critical to nuclear safety and nuclear security.A coded aperture imaging system provides a visualization solution.However,the correlation method has poor reconstruction performance for sources with low counts and for extended sources.Methods In this study,a Monte Carlo optimization-based MLEM algorithm(MC-MLEM)is proposed.The system matrix was obtained by accurate Monte Carlo simulation,so the physical effects such as mask penetration that affect the imaging process were taken into account in the MLEM algorithm.In the simulation process,the normalization of the system matrix was realized by controlling the source at different position of the source plane to have the same activity and emission angle.Results The experimental results showed that compared with the correlation method,the MC-MLEM algorithm could improve the signal-to-noise ratio and angular resolution and locate the source position quickly and accurately under low count conditions.Furthermore,the MC-MLEM algorithm could reconstruct the shape of the extended source and the expected activity ratio of cold-hot sources with large activity differences.Conclusion The MC-MLEM algorithm improved the imaging results and enhanced the reconstruction performance.

关键词： Coded aperture System matrix image reconstruction Low count imaging Extended sources

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Research on Multi-View image reconstruction Technology Based on Auto-Encoding Learning

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Computers, Materials & Continua 2022年第9期72卷 4603-4614页

作者： Tao Zhang Shaokui Gu Jinxing Niu Yi Cao School of Mechanical Engineering North China University of Water Conservancy and Hydroelectric PowerZhengzhou450045China Department of Electrical and Computer Engineering University of WindsorWindsorN9B 3P4ONCanada

Traditional three-dimensional(3D)image reconstruction method,which highly dependent on the environment and has poor reconstruction effect,is easy to lead to mismatch and poor real-time performance.The accuracy of feature extraction from multiple images affects the reliability and real-time performance of 3D reconstruction technology.To solve the problem,a multi-view image 3D reconstruction algorithm based on self-encoding convolutional neural network is proposed in this paper.The algorithm first extracts the feature information of multiple two-dimensional(2D)images based on scale and rotation invariance parameters of Scale-invariant feature transform(SIFT)operator.Secondly,self-encoding learning neural network is introduced into the feature refinement process to take full advantage of its feature extraction ability.Then,Fish-Net is used to replace the U-Net structure inside the self-encoding network to improve gradient propagation between U-Net structures,and Generative Adversarial Networks(GAN)loss function is used to replace mean square error(MSE)to better express image features,discarding useless features to obtain effective image features.Finally,an incremental structure from motion(SFM)algorithm is performed to calculate rotation matrix and translation vector of the camera,and the feature points are triangulated to obtain a sparse spatial point cloud,and meshlab software is used to display the results.Simulation experiments show that compared with the traditional method,the image feature extraction method proposed in this paper can significantly improve the rendering effect of 3D point cloud,with an accuracy rate of 92.5%and a reconstruction complete rate of 83.6%.

关键词： Multi-view image reconstruction self-encoding feature extraction

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Survey of EIT image reconstruction Algorithms

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Journal of Shanghai Jiaotong university(Science) 2022年第2期27卷 211-218页

作者：张明珠马艺馨黄宁宁葛浩 School of Electronic Information and Electrical Engineering Shanghai Jiao Tong UniversityShanghai200240China Institute of Medical Robotics Shanghai Jiao Tong UniversityShanghai200240China

With the recent promotion of clinical applications of electrical impedance tomography(EIT)technology,more scholars have begun studying EIT technology.Although the principle of EIT technology seems simple,EIT image reconstruction is a non-linear and ill-posed problem that is quite difficult to solve because of its soft field characteristics and the inhomogeneous distribution of its sensitive field.What’s more,the EIT reconstruction algorithm requires further improvements in robustness,clarity,etc.The image-reconstruction algorithm and image quality are among the key challenges in the application of EIT technology;thus,more research is urgently needed to improve the performance of EIT technology and use it to solve a larger variety of clinical problems.In this paper,we pay special attention to the latest advances in the study of EIT image-reconstruction algorithms to provide a convenient reference for EIT beginners and researchers who are newly involved in research on EIT image reconstruction.

关键词： electrical impedance tomography(EIT) image reconstruction inverse problem

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Artificial Intelligence-Based image reconstruction for Computed Tomography: A Survey

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Intelligent Automation & Soft Computing 2023年第6期36卷 2545-2558页

作者： Quan Yan Yunfan Ye Jing Xia Zhiping Cai Zhilin Wang Qiang Ni College of Computer National University of Defense TechnologyChangsha410073China Technical Service Center for Vocational Education National University of Defense TechnologyChangsha410073China School of Computing and Communications Lancaster UniversityLancasterLA14WAUK

Computed tomography has made significant advances since its intro-duction in the early 1970s,where researchers have mainly focused on the quality of image reconstruction in the early stage.However,radiation exposure poses a health risk,prompting the demand of the lowest possible dose when carrying out CT examinations.To acquire high-quality reconstruction images with low dose radiation,CT reconstruction techniques have evolved from conventional reconstruction such as analytical and iterative reconstruction,to reconstruction methods based on artificial intelligence(AI).All these efforts are devoted to con-structing high-quality images using only low doses with fast reconstruction speed.In particular,conventional reconstruction methods usually optimize one aspect,while AI-based reconstruction has finally managed to attain all goals in one shot.However,there are limitations such as the requirements on large datasets,unstable performance,and weak generalizability in AI-based reconstruction methods.This work presents the review and discussion on the classification,the commercial use,the advantages,and the limitations of AI-based image reconstruction methods in CT.

关键词： Computed tomography image reconstruction artificial intelligence

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A variable threshold visual sensing and image reconstruction method based on pulse sequence

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Science China(Information Sciences) 2022年第2期65卷 257-258页

作者： Jiangtao XU Peng LIN Zhiyuan GAO Kaiming NIE Liang XU School of Microelectronics Tianjin University Tianjin Key Laboratory of Imaging and Sensing Microelectronic Technology

Dear editor,Pulsed-based CMOS image sensor (CIS) outperforms classic CIS in terms of data rate,and is able to achieve ultra-highspeed imaging[1].A pulsed-based CIS which is able to capture the movement of the hard disk rotating at 6000 r/min,was designed in our previous work[2,3].The main problems of the pulse-based sensing mechanism are nonlinearity and readout noise.Nonlinearity.As light intensity is inversely proportional to the pulse interval[4],reconstructed images will involve loss of gray levels or lag in various light conditions[5].

关键词： pulse sequence variable threshold image reconstruction visual sensing adaptive video reconstruction

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image reconstruction for cone-beam computed tomography using total p-variation plus Kullback-Leibler data divergence

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Chinese Physics B 2017年第7期26卷 461-473页

作者：蔡爱龙李磊王林元闫镔郑治中张瀚铭胡国恩 National Digital Switching System Engineering & Technological Research Centre

Accurate reconstruction from a reduced data set is highly essential for computed tomography in fast and/or low dose imaging applications. Conventional total variation（TV）-based algorithms apply the L1 norm-based penalties, which are not as efficient as Lp（0〈p〈1） quasi-norm-based penalties. TV with a p-th power-based norm can serve as a feasible alternative of the conventional TV, which is referred to as total p-variation（TpV）. This paper proposes a TpV-based reconstruction model and develops an efficient algorithm. The total p-variation and Kullback-Leibler（KL） data divergence, which has better noise suppression capability compared with the often-used quadratic term, are combined to build the reconstruction model. The proposed algorithm is derived by the alternating direction method（ADM） which offers a stable, efficient, and easily coded implementation. We apply the proposed method in the reconstructions from very few views of projections（7 views evenly acquired within 180°）. The images reconstructed by the new method show clearer edges and higher numerical accuracy than the conventional TV method. Both the simulations and real CT data experiments indicate that the proposed method may be promising for practical applications.

关键词： image reconstruction total p-variation minimization Kullback-Leibler data divergence p-shrinkage mapping

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image reconstruction based on total-variation minimization and alternating direction method in linear scan computed tomography

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Chinese Physics B 2013年第7期22卷 582-589页

作者：张瀚铭王林元闫镔李磊席晓琦陆利忠 National Digital Switching System Engineering and Technological Research Center

Linear scan computed tomography （LCT） is of great benefit to online industrial scanning and security inspection due to its characteristics of straight-line source trajectory and high scanning speed. However, in practical applications of LCT, there are challenges to image reconstruction due to limited-angle and insufficient data. In this paper, a new reconstruction algorithm based on total-variation （TV） minimization is developed to reconstruct images from limited-angle and insufficient data in LCT. The main idea of our approach is to reformulate a TV problem as a linear equality constrained problem where the objective function is separable, and then minimize its augmented Lagrangian function by using alternating direction method （ADM） to solve subproblems. The proposed method is robust and efficient in the task of reconstruction by showing the convergence of ADM. The numerical simulations and real data reconstructions show that the proposed reconstruction method brings reasonable performance and outperforms some previous ones when applied to an LCT imaging problem.

关键词： linear scan CT image reconstruction total variation alternating direction method

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image reconstruction of Ghost Imaging Based on Improved Generative Adversarial Networks

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Journal of Applied Mathematics and Physics 2022年第4期10卷 1098-1104页

作者： Xu Chen College of Science University of Shanghai for Science and Technology Shanghai China

In this paper, we improve traditional generative adversarial networks (GAN) with reference to residual networks and convolutional neural networks to facilitate the reconstruction of complex objects that cannot be reconstructed by traditional associative imaging methods. Unlike traditional ghost imaging to reconstruct objects from bucket signals, our proposed method can use simple objects (such as EMNIST) as a training set for GAN, and then recognize objects (such as faces) of completely different complexity than the training set. We use traditional ghost imaging and neural network to reconstruct target objects respectively. According to the research results in this paper, the method based on neural network can reconstruct complex objects very well, but the method based on traditional ghost imaging cannot reconstruct complex objects. The research scheme in this paper is of great significance for the reconstruction of complex object-related imaging under low sampling conditions.

关键词： Generative Adversarial Networks Ghost Imaging image reconstruction

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