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9th World Conference on Neutron Radiography (WCNR-9)

作者： Guo, J. Buecherl, T. Zou, Y. Guo, Z. Peking Univ State Key Lab Nucl Phys & Technol Beijing 100871 Peoples R China Peking Univ Sch Phys Beijing 100871 Peoples R China Tech Univ Munich Lehrstuhl Radiochem D-80748 Garching Germany

Investigations on the fast neutron beam geometry for the NECTAR facility are presented. The results of MCNP simulations and experimental measurements of the beam distributions at NECTAR are compared. Boltzmann functions are used to describe the beam profile in the detection plane assuming the area source to be set up of large number of single neutron point sources. An iterative algebraic reconstruction algorithm is developed, realized and verified by both simulated and measured projection data. The feasibility for improved reconstruction in fast neutron computerized tomography at the NECTAR facility is demonstrated. (C) 2011 Elsevier B.V. All rights reserved.

关键词： Beam geometry image reconstruction algorithm Fast neutron Neutron computerized tomography Neutron imaging

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Development and study of image reconstruction algorithm for electrical capacitance tomography

Development and study of image reconstruction algorithm for ...

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2nd IEEE Conference on Industrial Electronics and Applications (ICIEA 2007)

作者： Zhang, Yonggao Gao, Yanli Xu, Qing Zhou, Feng E China Jiao Tong Univ Nanchang 330013 Peoples R China

ISBN: (纸本)9781424407361

In order to realize measurement on line for multi-phase flow parameter using electrical capacitance tomography (ECT), it is the key to improve quality and rate of image reconstruction, which also is hot spot for researcher. In this paper, ECT image reconstruction algorithms are reviewed by their principle and recently researches on image reconstruction are introduced. The most commonly used ECT image reconstruction algorithms, LBP, Landweber iteration, MLRR were evaluated using simulations. Future possibilities on ECT image reconstruction algorithms are also discussed.

关键词： ECT image reconstruction algorithm simulation

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Tissue contrast enhancement: image reconstruction algorithm and selection of TI in inversion recovery MRI

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Magnetic Resonance Imaging 1986年第3期4卷 229-235页

作者： Moran, P.R. Kumar, N.G. Karstaedt, N. Jackels, S.C. Department of Radiology Bowman Gray School of Medicine Wake Forest University Winston-Salem NC 27103 300 S. Hawthorne Road United States

It is clearly demonstrated that the proper application of the inversion recovery imaging pulse sequence is dependent on the method of image reconstruction and the selection of TI for optimum tissue contrast. There are two methods of 2DFT image reconstruction of IR sequence time-domain raw data. The first is a modulus-image reconstruction algorithm (contrast-obliterating option), and the second is a phase-correction routine for reconstructing "phase-sensitive" true IR-images. The second option generates proper "in-phase" images, retains proper scale of contrast, but can invert the algebraic sign of image-values under certain conditions. A series of "phase-sensitive" and "modulus" reconstructed brain images, obtained with conventional and optimized new IR pulse sequences, are shown to demonstrate these effects. They illustrate the considerable advantages gained, in practical clinical situations, if one generates "phase-sensitive" true IR-images from IR-sequence raw data at optimum TI for tissue contrast enhancement. © 1986.

关键词： image reconstruction algorithm Inversion recovery MRI MRI Optimized new IR pulse sequences Tissue contrast enhancement

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image reconstruction algorithm for Electrical Capacitance Tomography Based on Multi-dimensional Support Vector Regression

Image Reconstruction Algorithm for Electrical Capacitance To...

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The 2010 International Conference on Computer Application and System Modeling(2010计算机应用与系统建模国际会议 ICCASM 2010)

作者： Yang xiaoguang Li Jianwei Province-Ministry Joint Key Laboratory of Electromagnetic Field and Electrical Apparatus Reliability School of Computer Science and Software Hebei University of Technology Tianjin China

ISBN: (纸本)9781424472352

A new method based on multi-dimensional support vector regression (MSVR) is presented to solve the ill-posed image reconstruction problem in electrical capacitance tomography (ECT). The MSVR with a hyper-spherical insensitive zone and IRWLS algorithm is firstly introduced to solve this problem. The neural networks have been reported to be applied to this kind of inverse problem. However, this method is known for serious over-fitting. MSVR has been proven to have all the advantages of neural networks, and can overcome the over-fitting problem. The proposed MSVR method in this paper is verified through typical flow patters image reconstruction. The results show that this method is an effective approach to solve image reconstruction for ECT, which is faster compared with the iterative methods and more accurate compared with the neural networks.

关键词： electrical capacitance tomography two-phase flow image reconstruction algorithm multi-dimensional support vector regression similar iterative re-weight least square

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New image reconstruction algorithm for CCERT: LBP plus Gaussian mixture model (GMM) clustering

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MEASUREMENT SCIENCE AND TECHNOLOGY 2021年第2期32卷

作者： Wang, Yuxin He, Xuekai Jiang, Yandan Wang, Baoliang Ji, Haifeng Huang, Zhiyao Zhejiang Univ Coll Control Sci & Engn State Key Lab Ind Control Technol Hangzhou 310027 Peoples R China

This work focuses on the study of the image reconstruction algorithm of capacitively coupled electrical resistance tomography (CCERT). With the combination of a linear back projection (LBP) algorithm and an unsupervised Gaussian mixture model (GMM) algorithm, a new image reconstruction algorithm for CCERT is proposed. The LBP algorithm is used to implement the initial image reconstruction. The GMM algorithm is adopted to acquire the gray level threshold which will be used for the establishment of the gray level threshold filter. The final reconstructed image can be obtained with the thresholding operation. With a developed 12-electrode CCERT prototype system, the new image reconstruction algorithm is tested in image reconstruction experiments. The experimental results show that the proposed new image reconstruction algorithm is effective. The image reconstruction results are satisfactory. Compared with the conventional image reconstruction algorithms, the new image reconstruction algorithm (LBP + GMM) can obtain better reconstructed images with smaller relative image errors. It can obtain the reconstructed images with fewer empirical preset parameters and less manual intervention. In addition, with the introduction of the GMM algorithm, a relatively more suitable and reasonable gray level threshold can be obtained because the GMM algorithm implements the clustering process by utilizing both mean and variance information on the gray level distribution. Thus, better image reconstruction results can be obtained.

关键词： process tomography electrical tomography image reconstruction algorithm Gaussian mixture model (GMM) capacitively coupled electrical resistance tomography (CCERT)

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A deep neural networks-based image reconstruction algorithm for a reduced sensor model in large-scale tomography system

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FLOW MEASUREMENT AND INSTRUMENTATION 2022年第0期88卷

作者： Lee, Chau Ching Rahiman, Mohd Hafiz Fazalul Leow, Pei Ling Rahim, Ruzairi Abdul Saad, Fathinul Syahir Ahmad Univ Malaysia Perlis Fac Elect Engn & Technol Pauh Putra Campus Arau 02600 Perlis Malaysia Univ Malaysia Perlis Ctr Excellence Adv Sensor Technol CEASTech Arau Perlis Malaysia Univ Teknol Malaysia Fac Engn Sch Elect Engn Skudai 81310 Johor Malaysia

image reconstruction for soft-field tomography is a highly nonlinear and ill-posed inverse problem. Owing to the highly complicated nature of soft-field, the reconstructed images are always poor in quality. One of the factors that affect image quality is the number of sensors in a tomography system. It is commonly assumed that increasing the number of sensors in a tomography system will improve the ill-posed condition in image recon-struction and hence improve image quality. However, as the number of sensors increases, challenges such as more complicated and expensive hardware, slower data acquisition rates, longer image reconstruction times, and larger sensitivity matrices will arise, resulting in a greater ill-posed condition. Since deep learning (DL) is capable of expressing complex nonlinear functions, the majority of research efforts have been directed toward developing a robust DL-based inverse solver for image reconstruction. However, no study has been conducted to solve the inverse problem and improve the quality of the reconstructed image using a reduced sensor model for a large-scale tomography system. This paper proposed an image reconstruction algorithm based on Deep Neural Net-works (DNN) to investigate its feasibility in solving the ill-posed inverse problem caused by the reduced sensor model for a large-scale tomography system. The proposed DNN model is based on a supervised, feed-forward, fully connected, backpropagation network. It comprises an input layer, three hidden layers and an output layer. Also, it was trained using large data samples obtained from COMSOL simulation. The relationship between the scattered electromagnetic field measurement and the corresponding true electromagnetic field distribution vector is determined. During the image reconstruction process, the untrained scattered electromagnetic field measurement samples are used as inputs to the trained DNN model, and the model output is an estimate of the electromagnetic field distrib

关键词： Inverse problem image reconstruction algorithm Reduced sensor model Large-scale tomography Deep learning

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Improved PSO ERT image reconstruction algorithm for Human Lung Based on Prior Knowledge and Clustering

Improved PSO ERT Image Reconstruction Algorithm for Human Lu...

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第30届中国控制与决策会议

作者： Liqing Xiao Qiufang Ma Huaxiang Wang School of Mechanical and Electrical Engineering Huainan Normal University School of Precision Instrument & Optoelectronics Engineering Tianjin University College of International Electronic Commerce Qingdao Huanghai University School of Electrical and Information Engineering Tianjin University

Aiming to improve the resolution of the reconstructed image in electrical resistance tomography(ERT) technique for human lung, an improved particle swarm optimization(PSO) image reconstruction algorithm is proposed based on prior knowledge and clustering, according to the characteristics of ERT technique for human lung. In the initial iteration of the novel algorithm, the modified Newton-Raphson algorithm is adopted to solve the inverse problem of ERT for human lung. According to the image reconstruction result of the modified Newton-Raphson algorithm, the finite elements in the sensitivity field are clustered according to the resistivity, based on the prior knowledge. On that basis, the improved PSO image reconstruction algorithm is adopted to the reconstruction of the image. Simulation results demonstrate that, compared to the modified Newton-Raphson algorithm and the PSO image reconstruction algorithm based on prior knowledge, the proposed algorithm can improve the precision of image reconstruction effectively.

关键词： electrical resistance tomography human lung particle swarm optimization image reconstruction algorithm clustering inverse problem

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Assessment of CT-to-physical density table for multiple image reconstruction functions with a large-bore scanner for radiotherapy treatment planning

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PHYSICA MEDICA-EUROPEAN JOURNAL OF MEDICAL PHYSICS 2025年 133卷 104970-104970页

作者： Okumura, Takuro Koganezawa, Akito S. Nakashima, Takeo Ochi, Yusuke Tsubouchi, Kento Murakami, Yuji Hiroshima Univ Hosp Dept Clin Practice & Support Hiroshima 7348551 Japan Teikyo Univ Fac Sci & Engn Dept Informat & Elect Engn 1-1 Toyosatodai Utsunomiya Tochigi 3208551 Japan Hiroshima Univ Grad Sch Biomed & Hlth Sci Dept Radiat Oncol Hiroshima 7348551 Japan

Purpose: To evaluate the performance of the Aquilion Exceed LB computed tomography (CT) scanner for radiotherapy treatment planning, this study examined the effect of different combinations of the image reconstruction function (IRF) (AiCE and AIDR) and scan parameters on the CT-to-physical density (CT-PD) table and radiation dose in the phantom, and the effect of different object positions on CT values. Methods: To investigate IRF's influence on each material, we calculated CT values by varying tube current, pitch, field of view (FOV), and phantom position for each IRF, comparing them with reference values using filtered back projection (FBP). Furthermore, we evaluated changes in depth dose values due to IRF differences using a solid phantom. Results: In the combinations of changes in IRF and scan parameters the change in CT value (Delta HU) of each material was within +/- 10 HU, except for most conditions. The change in physical density (Delta PD) was within +/- 0.02 g/cm3 for all combinations. For changes in phantom position, Delta HU was within +/- 25 HU for changes within the scan FOV, except for Bone 200 mg/cc and 1250 mg/cc. In areas outside the scan FOV with an expanded FOV, Delta HU was significantly larger than within the scan FOV. Variations in depth dose for different IRFs using solid phantoms were within +/- 0.5 %, except at material boundaries. Conclusion: Our evaluations of the CT values and dose calculations suggested no need to change the CT-PD table, even with multiple IRFs.

关键词： CT number Dose calculation image reconstruction algorithm Large-bore CT Treatment planning

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Efficient discrete cosine transform model-based algorithm for photoacoustic image reconstruction

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JOURNAL OF BIOMEDICAL OPTICS 2013年第6期18卷 1-9页

作者： Zhang, Yan Wang, Yuanyuan Zhang, Chen Fudan Univ Dept Elect Engn Shanghai 200437 Peoples R China

The model-based algorithm is an effective reconstruction method for photoacoustic imaging (PAI). Compared with the analytical reconstruction algorithms, the model-based algorithm is able to provide a more accurate and high-resolution reconstructed image. However, the relatively heavy computational complexity and huge memory storage requirement often impose restrictions on its applications. We incorporate the discrete cosine transform (DCT) in PAI reconstruction and establish a new photoacoustic model. With this new model, an efficient algorithm is proposed for PAI reconstruction. Relatively significant DCT coefficients of the measured signals are used to reconstruct the image. As a result, the calculation can be saved. The theoretical computation complexity of the proposed algorithm is figured out and it is proved that the proposed method is efficient in calculation. The proposed algorithm is also verified through the numerical simulations and in vitro experiments. Compared with former developed model-based methods, the proposed algorithm is able to provide an equivalent reconstruction with the cost of much less time. From the theoretical analysis and the experiment results, it would be concluded that the model-based PAI reconstruction can be accelerated by using the proposed algorithm, so that the practical applicability of PAI may be enhanced. (C) 2013 Society of Photo-Optical Instrumentation Engineers (SPIE)

关键词： photoacoustic imaging image reconstruction algorithm model based discrete cosine transform

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IoT-Integrated Bio-Impedance Monitoring for Enhanced Agricultural Applications

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JOURNAL OF THE ELECTROCHEMICAL SOCIETY 2025年第4期172卷

作者： Kumar, Ramesh Ratnesh, Ratneshwar Kumar Gupta, Prateek Singla, Manish Kumar Pandey, Pawan Kumar Chitkara Univ Inst Engn & Technol Dept Interdisciplinary Courses Engn Chandigarh 140401 Punjab India Meerut Inst Engn & Technol Dept Elect & Commun Engn Meerut 250005 Uttar Pradesh India Bennett Univ Sch Comp Sci Engn & Technol Greater Noida 201310 India Appl Sci Private Univ Appl Sci Res Ctr Amman Jordan Gyan Ganga Inst Technol & Sci Dept Elect Engn Jabalpur 482003 MP India

A non-invasive bio-impedance technique provides a quick response to small changes in the electrical impedance of a phantom or object, making it suitable for agriculture-based applications. This method generates high-frequency, low-current signals that vary with impedance changes in the phantom (e.g., papaya) detected through paired electrodes. The electrodes, positioned at either end of the cylindrical phantom, measure electrical impedance based on voltage changes in response to constant current insertion. reconstruction algorithms designed in MATLAB generate electrical impedance images using initial conductivity and measured potentials. Electrical Impedance Tomography applies forward and inverse solutions to estimate conductivity distribution within an object, leveraging finite element meshes with triangular elements for computational accuracy. The forward problem involves determining current magnitude in a homogeneous conducting medium. This study developed a GUI-based reconstruction algorithm for the agricultural phantom model using MATLAB. Data acquisition integrates Internet of Things technology, connecting sensors to the GUI system and further to remote monitoring systems. This enables real-time parameter monitoring for agricultural phantom applications. The IoT-based approach demonstrates versatility for agriculture and medical applications, offering efficient, remote-access monitoring of critical parameters. Develop an IoT-enabled data acquisition system for Electrical Impedance Tomography (EIT)Developed MATLAB enable system for *** real-time parameter monitoring for agricultural *** positioned at either end of a cylindrical agricultural phantom.

关键词： image reconstruction algorithm internet of things (IoT) finite element method electrical impedance tomography graphical user interface (GUI)

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