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SIRT‑TV 3D image reconstruction for simulated muon tomography of the QinShiHuang tomb 认领 引用
1
作者 Ning Su Yuan‑Yuan Liu +6 位作者 Li Wang Jian‑Jie Zhang Zhi Zhou Xin Zhao Wen‑Wan Ding Peng Zheng Jian‑Ping Cheng 《Nuclear Science and Techniques》 SCIE EI CAS CSCD 2026年第8期95-106,共12页
Cosmic-ray muons are suitable for the nondestructive imaging of large-scale objects.However,owing to the low statistics of cosmic-ray muons and the complicated surroundings of transmission muography experiments,the ap... Cosmic-ray muons are suitable for the nondestructive imaging of large-scale objects.However,owing to the low statistics of cosmic-ray muons and the complicated surroundings of transmission muography experiments,the applicability of transmission muography is limited by challenges such as noise and incomplete data,which hinder 3D image reconstruction.In this study,we applied the SIRT-TV algorithm to muon tomography to perform 3D image reconstruction simulations based on the QinShiHuang tomb phantom.The results indicate that compared with the conventional SIRT algorithm,the SIRT-TV algorithm can effectively suppress artifacts in the reconstructed image,allowing for a more accurate reconstruction of the underground palace.The geometric similarity of the walls reconstructed by SIRT-TV is nearly three times that of the results reconstructed by SIRT.This paper further discusses the impact of the TV-minimization algorithm parameters,measurement configurations,and durations on the quality of the reconstructed image,providing a reference point for future experiments. 展开更多
关键词 Muography Monte Carlo simulation Image reconstruction
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A lightweight physics-conditioned diffusion multi-model for medical image reconstruction 认领 引用 被引量：1
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作者 Raja Vavekanand Ganesh Kumar Shakhlokhon Kurbanova 《Biomedical Engineering Communications》 CAS 2026年第2期50-59,共10页
Background:Medical imaging advancements are constrained by fundamental trade-offs between acquisition speed,radiation dose,and image quality,forcing clinicians to work with noisy,incomplete data.Existing reconstructio... Background:Medical imaging advancements are constrained by fundamental trade-offs between acquisition speed,radiation dose,and image quality,forcing clinicians to work with noisy,incomplete data.Existing reconstruction methods either compromise on accuracy with iterative algorithms or suffer from limited generalizability with task-specific deep learning approaches.Methods:We present LDM-PIR,a lightweight physics-conditioned diffusion multi-model for medical image reconstruction that addresses key challenges in magnetic resonance imaging(MRI),CT,and low-photon imaging.Unlike traditional iterative methods,which are computationally expensive,or task-specific deep learning approaches lacking generalizability,integrates three innovations.A physics-conditioned diffusion framework that embeds acquisition operators(Fourier/Radon transforms)and noise models directly into the reconstruction process.A multi-model architecture that unifies denoising,inpainting,and super-resolution via shared weight conditioning.A lightweight design(2.1M parameters)enabling rapid inference(0.8s/image on GPU).Through self-supervised fine-tuning with measurement consistency losses adapts to new imaging modalities using fewer annotated samples.Results:Achieves state-of-the-art performance on fastMRI(peak signal-to-noise ratio(PSNR):34.04 for single-coil/31.50 for multi-coil)and Lung Image Database Consortium and Image Database Resource Initiative(28.83 PSNR under Poisson noise).Clinical evaluations demonstrate superior preservation of anatomical structures,with SSIM improvements of 8.8%for single-coil and 4.36%for multi-coil MRI over uDPIR.Conclusion:It offers a flexible,efficient,and scalable solution for medical image reconstruction,addressing the challenges of noise,undersampling,and modality generalization.The model’s lightweight design allows for rapid inference,while its self-supervised fine-tuning capability minimizes reliance on large annotated datasets,making it suitable for real-world clinical applications. 展开更多
关键词 medical image reconstruction physics-conditioned diffusion multi-task learning self-supervised fine-tuning multimodal fusion lightweight neural networks
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Neural-field-based image reconstruction for bioluminescence tomography 认领 引用
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作者 Xuanxuan Zhang Xu Cao +2 位作者 Jiulou Zhang Lin Zhang Guanglei Zhang 《Journal of Innovative Optical Health Sciences》 SCIE EI CSCD 2025年第1期165-179,共15页
Deep learning(DL)-based image reconstruction methods have garnered increasing interest in the last few years.Numerous studies demonstrate that DL-based reconstruction methods function admirably in optical tomographic ... Deep learning(DL)-based image reconstruction methods have garnered increasing interest in the last few years.Numerous studies demonstrate that DL-based reconstruction methods function admirably in optical tomographic imaging techniques,such as bioluminescence tomography(BLT).Nevertheless,nearly every existing DL-based method utilizes an explicit neural representation for the reconstruction problem,which either consumes much memory space or requires various complicated computations.In this paper,we present a neural field(NF)-based image reconstruction scheme for BLT that uses an implicit neural representation.The proposed NFbased method establishes a transformation between the coordinate of an arbitrary spatial point and the source value of the point with a relatively light-weight multilayer perceptron,which has remarkable computational efficiency.Another simple neural network composed of two fully connected layers and a 1D convolutional layer is used to generate the neural features.Results of simulations and experiments show that the proposed NF-based method has similar performance to the photon density complement network and the two-stage network,while consuming fewer floating point operations with fewer model parameters. 展开更多
关键词 Bioluminescence tomography image reconstruction neural field
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Frequency-Quantized Variational Autoencoder Based on 2D-FFT for Enhanced Image Reconstruction and Generation 认领 引用
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作者 Jianxin Feng Xiaoyao Liu 《Computers, Materials & Continua》 SCIE EI 2025年第5期2087-2107,共21页
As a form of discrete representation learning,Vector Quantized Variational Autoencoders(VQ-VAE)have increasingly been applied to generative and multimodal tasks due to their ease of embedding and representative capaci... As a form of discrete representation learning,Vector Quantized Variational Autoencoders(VQ-VAE)have increasingly been applied to generative and multimodal tasks due to their ease of embedding and representative capacity.However,existing VQ-VAEs often perform quantization in the spatial domain,ignoring global structural information and potentially suffering from codebook collapse and information coupling issues.This paper proposes a frequency quantized variational autoencoder(FQ-VAE)to address these issues.The proposed method transforms image features into linear combinations in the frequency domain using a 2D fast Fourier transform(2D-FFT)and performs adaptive quantization on these frequency components to preserve image’s global relationships.The codebook is dynamically optimized to avoid collapse and information coupling issue by considering the usage frequency and dependency of code vectors.Furthermore,we introduce a post-processing module based on graph convolutional networks to further improve reconstruction quality.Experimental results on four public datasets demonstrate that the proposed method outperforms state-of-the-art approaches in terms of Structural Similarity Index(SSIM),Learned Perceptual Image Patch Similarity(LPIPS),and Reconstruction Fréchet Inception Distance(rFID).In the experiments on the CIFAR-10 dataset,compared to the baselinemethod VQ-VAE,the proposedmethod improves the abovemetrics by 4.9%,36.4%,and 52.8%,respectively. 展开更多
关键词 VAE 2D-FFT image reconstruction image generation
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Image reconstruction based on total-variation minimization and alternating direction method in linear scan computed tomography 认领 引用 被引量：20
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作者 张瀚铭 王林元 +3 位作者 闫镔 李磊 席晓琦 陆利忠 《Chinese Physics B》 SCIE EI CAS CSCD 2013年第7期582-589,共8页
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 prac... 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. 展开更多
关键词 linear scan CT image reconstruction total variation alternating direction method
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An algorithm for computed tomography image reconstruction from limited-view projections 认领 引用 被引量：20
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作者 王林元 李磊 +3 位作者 闫镔 江成顺 王浩宇 包尚联 《Chinese Physics B》 SCIE EI CAS 2010年第8期642-647,共6页
With the development of the compressive sensing theory, the image reconstruction from the projections viewed in limited angles is one of the hot problems in the research of computed tomography technology. This paper d... With the development of the compressive sensing theory, the image reconstruction from the projections viewed in limited angles is one of the hot problems in the research of computed tomography technology. This paper develops an iterative algorithm for image reconstruction, which can fit the most cases. This method gives an image reconstruction flow with the difference image vector, which is based on the concept that the difference image vector between the reconstructed and the reference image is sparse enough. Then the l1-norm minimization method is used to reconstruct the difference vector to recover the image for flat subjects in limited angles. The algorithm has been tested with a thin planar phantom and a real object in limited-view projection data. Moreover, all the studies showed the satisfactory results in accuracy at a rather high reconstruction speed. 展开更多
关键词 limited-view problem computed tomography image reconstruction algorithms reconstruction-reference difference algorithm adaptive steepest descent-projection onto convex sets algorithm
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Method of lateral image reconstruction in structured illumination microscopy with super resolution 认领 引用 被引量：2
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作者 Qiang Yang Liangcai Cao +2 位作者 Hua Zhang Hao Zhang Guofan Jin 《Journal of Innovative Optical Health Sciences》 SCIE EI 2016年第3期4-18,共15页
The image reconstruction process in super-resolution structured illumination microscopy(SIM)is investigated.The structured pattern is generated by the interference of two Gaussian beams to encode undetectable spectra ... The image reconstruction process in super-resolution structured illumination microscopy(SIM)is investigated.The structured pattern is generated by the interference of two Gaussian beams to encode undetectable spectra into detectable region of microscope.After parameters estimation of the structured pattern,the encoded spectra are computationally decoded and recombined in Fourier domain to equivalently increase the cut-off frequency of microscope,resulting in the extension of detectable spectra and a reconstructed image with about two-fold enhanced resolution.Three di®erent methods to estimate the initial phase of structured pattern are compared,verifying the auto-correlation algorithm a®ords the fast,most precise and robust measurement.The artifacts sources and detailed reconstruction°owchart for both linear and nonlinear SIM are also presented. 展开更多
关键词 Microscopy structured illumination super-resolution image reconstruction
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Accelerating SAGE algorithm in PET image reconstruction by rescaled block-iterative method 认领 引用 被引量：3
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作者 朱宏擎 舒华忠 +1 位作者 周健 罗立民 《Journal of Southeast University(English Edition)》 EI CAS 2005年第2期207-210,共4页
A new method to accelerate the convergent rate of the space-alternatinggeneralized expectation-maximization (SAGE) algorithm is proposed. The new rescaled block-iterativeSAGE (RBI-SAGE) algorithm combines the RBI algo... A new method to accelerate the convergent rate of the space-alternatinggeneralized expectation-maximization (SAGE) algorithm is proposed. The new rescaled block-iterativeSAGE (RBI-SAGE) algorithm combines the RBI algorithm with the SAGE algorithm for PET imagereconstruction. In the new approach, the projection data is partitioned into disjoint blocks; eachiteration step involves only one of these blocks. SAGE updates the parameters sequentially in eachblock. In experiments, the RBI-SAGE algorithm and classical SAGE algorithm are compared in theapplication on positron emission tomography (PET) image reconstruction. Simulation results show thatRBI-SAGE has better performance than SAGE in both convergence and image quality. 展开更多
关键词 positron emission tomography space-alternating generalizedexpectation-maximization image reconstruction rescaled block-iterative maximum likelihood
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Super-resolution image reconstruction based on three-step-training neural networks 认领 引用 被引量：2
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作者 Fuzhen Zhu Jinzong Li +1 位作者 Bing Zhu Dongdong Ma 《Journal of Systems Engineering and Electronics》 SCIE EI 2010年第6期934-940,共7页
A new method of super-resolution image reconstruction is proposed,which uses a three-step-training error backpropagation neural network(BPNN)to realize the super-resolution reconstruction(SRR)of satellite image.The me... A new method of super-resolution image reconstruction is proposed,which uses a three-step-training error backpropagation neural network(BPNN)to realize the super-resolution reconstruction(SRR)of satellite image.The method is based on BPNN.First,three groups learning samples with different resolutions are obtained according to image observation model,and then vector mappings are respectively used to those three group learning samples to speed up the convergence of BPNN,at last,three times consecutive training are carried on the BPNN.Training samples used in each step are of higher resolution than those used in the previous steps,so the increasing weights store a great amount of information for SRR,and network performance and generalization ability are improved greatly.Simulation and generalization tests are carried on the well-trained three-step-training NN respectively,and the reconstruction results with higher resolution images verify the effectiveness and validity of this method. 展开更多
关键词 image reconstruction super-resolution three-steptraining neural network BP algorithm vector mapping.
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Accelerating spectral digital image correlation computation with Taylor series image reconstruction 认领 引用 被引量：1
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作者 Shihao Han Yuming He +2 位作者 Yiyu Hu Jian Lei Yongbo Yang 《Acta Mechanica Sinica》 SCIE EI CAS CSCD 2024年第6期78-86,共9页
In this paper,we introduce an accelerating algorithm based on the Taylor series for reconstructing target images in the spectral digital image correlation method(SDIC).The Taylor series image reconstruction method is ... In this paper,we introduce an accelerating algorithm based on the Taylor series for reconstructing target images in the spectral digital image correlation method(SDIC).The Taylor series image reconstruction method is employed instead of the previous direct Fourier transform(DFT)image reconstruction method,which consumes the majority of the computational time for target image reconstruction.The partial derivatives in the Taylor series are computed using the fast Fourier transform(FFT)of the entire image,following the principles of Fourier transform theory.To examine the impact of different orders of Taylor series expansion on accuracy and efficiency,we employ third-and fourth-order Taylor series image reconstruction methods and compare them with the DFT image reconstruction method through simulated experiments.As a result of these enhancements,the computational efficiency using the third-and fourth-order Taylor series improves by factors of 57 and 46,respectively,compared to the previous method.In terms of analysis accuracy,within a strain range of 0–0.1 and without the addition of image noise,the accuracy of the proposed method increases with higher expansion orders,surpassing that of the DFT image reconstruction method when the fourth order is utilized.However,when different levels of Gaussian noise are applied to simulated images individually,the accuracy of the third-or fourth-order Taylor series expansion method is superior to that of the DFT reconstruction method.Finally,we present the analyzed experimental results of a silicone rubber plate specimen with bilateral cracks under uniaxial tension. 展开更多
关键词 Digital image correlation Taylor series expansion Image reconstruction Strain measurement
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Image reconstruction for cone-beam computed tomography using total p-variation plus Kullback-Leibler data divergence 认领 引用 被引量：1
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作者 蔡爱龙 李磊 +4 位作者 王林元 闫镔 郑治中 张瀚铭 胡国恩 《Chinese Physics B》 SCIE EI CAS CSCD 2017年第7期461-473,共13页
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 pen... 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. 展开更多
关键词 image reconstruction total p-variation minimization Kullback-Leibler data divergence p-shrinkage mapping
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Image Reconstruction for Invasive ERT in Vertical Oil Well Logging 认领 引用 被引量：1
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作者 周海力 徐立军 +2 位作者 曹章 胡金海 刘兴斌 《Chinese Journal of Chemical Engineering》 SCIE EI CAS CSCD 2012年第2期319-328,共10页
An invasive electrical resistance tomographic sensor was proposed for production logging in vertical oil well.The sensor consists of 24 electrodes that are fixed to the logging tool,which can move in the pipeline to a... An invasive electrical resistance tomographic sensor was proposed for production logging in vertical oil well.The sensor consists of 24 electrodes that are fixed to the logging tool,which can move in the pipeline to acquire data on the conductivity distribution of oil/water mixture flow at different depths.A sensitivity-based algorithm was introduced to reconstruct the cross-sectional images.Analysis on the sensitivity of the sensor to the distribution of oil/water mixture flow was carried out to optimize the position of the imaging cross-section.The imaging results obtained using various boundary conditions at the pipe wall and the logging tool were compared.Eight typical models with various conductivity distributions were created and the measurement data were obtained by solving the forward problem of the sensor system.Image reconstruction was then implemented by using the simulation data for each model.Comparisons between the models and the reconstructed images show that the number and spatial distribution of the oil bubbles can be clearly identified. 展开更多
关键词 image reconstruction electrical resistance tomography invasive sensor production logging vertical well
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Estimation-free spatial-domain image reconstruction of structured illumination microscopy 认领 引用 被引量：1
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作者 Xiaoyan Li Shijie Tu +4 位作者 Yile Sun Yubing Han Xiang Hao Cuifang kuang Xu Liu 《Journal of Innovative Optical Health Sciences》 SCIE EI CSCD 2024年第2期45-58,共14页
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 traditiona... 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. 展开更多
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 认领 引用 被引量：1
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作者 Lingyu Ma Zezheng Qin +1 位作者 Yiming Ma Mingjian Sun 《Journal of Innovative Optical Health Sciences》 SCIE EI CSCD 2024年第3期23-40,共18页
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... 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. 展开更多
关键词 Deep learning feature transformation image reconstruction limited-view measurement photoacoustic tomography.
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Optimization-based image reconstruction in x-ray computed tomography by sparsity exploitation of local continuity and nonlocal spatial self-similarity 认领 引用 被引量：2
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作者 张瀚铭 王林元 +3 位作者 李磊 闫镔 蔡爱龙 胡国恩 《Chinese Physics B》 SCIE EI CAS CSCD 2016年第7期557-565,共9页
The additional sparse prior of images has been the subject of much research in problems of sparse-view computed tomography（CT） reconstruction. A method employing the image gradient sparsity is often used to reduce t... The additional sparse prior of images has been the subject of much research in problems of sparse-view computed tomography（CT） reconstruction. A method employing the image gradient sparsity is often used to reduce the sampling rate and is shown to remove the unwanted artifacts while preserve sharp edges, but may cause blocky or patchy artifacts.To eliminate this drawback, we propose a novel sparsity exploitation-based model for CT image reconstruction. In the presented model, the sparse representation and sparsity exploitation of both gradient and nonlocal gradient are investigated.The new model is shown to offer the potential for better results by introducing a similarity prior information of the image structure. Then, an effective alternating direction minimization algorithm is developed to optimize the objective function with a robust convergence result. Qualitative and quantitative evaluations have been carried out both on the simulation and real data in terms of accuracy and resolution properties. The results indicate that the proposed method can be applied for achieving better image-quality potential with the theoretically expected detailed feature preservation. 展开更多
关键词 computed tomography image reconstruction sparsity exploitation nonlocal gradient
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SDOCT IMAGE RECONSTRUCTION BY INTERFEROMETRIC SYNTHETIC APERTURE MICROSCOPY 认领 引用
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作者 XIAODONG CHEN QIAO LI +2 位作者 YONG LEI YI WANG DAOYIN YU 《Journal of Innovative Optical Health Sciences》 SCIE EI 2010年第1期17-23,共7页
Spectral domain optical coherence tomography(SDOCT)is a noninvasive,cross-sectional imaging technique that measures depth resolved reflectance of tissue by Fourier transforming the spectral interferogram with the scan... Spectral domain optical coherence tomography(SDOCT)is a noninvasive,cross-sectional imaging technique that measures depth resolved reflectance of tissue by Fourier transforming the spectral interferogram with the scanning of the reference avoided.Interferometric synthetic aperture microscopy(ISAM)is an optical microscopy computed-imaging technique for measuring the optical properties of biological tissues,which can overcome the compromise between depth of focus and transverse resolution.This paper describes the principle of SDOCT and ISAM,which multiplexes raw acquisitions to provide quantitatively meaningful data with reliable spatially invariant resolution at all depths.A mathematical model for a coherent microscope with a planar scanning geometry and spectral detection was described.The two-dimensional fast Fourier transform(FFT)of spectral data in the transverse directions was calculated.Then the nonuniform ISAM resampling and filtering was implemented to yield the scattering potential within the scalar model.Inverse FFT was used to obtain the ISAM reconstruction.One scatterer,multiple scatterers,and noisy simulations were implemented by use of ISAM to catch spatially invariant resolution.ISAM images were compared to those obtained using standard optical coherence tomography(OCT)methods.The high quality of the results validates the rationality of the founded model and that diffraction limited resolution can be achieved outside the focal plane. 展开更多
关键词 Optical coherence tomography(OCT) spectral domain OCT(SDOCT) interferometric synthetic aperture microscopy(ISAM) resolution image reconstruction
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Investigation of prior image constrained compressed sensing-based spectral X-ray CT image reconstruction 认领 引用
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作者 周正东 余子丽 +1 位作者 张雯雯 管绍林 《Journal of Southeast University(English Edition)》 EI CAS 2016年第4期420-425,共6页
To improve spectral X-ray CT reconstructed image quality, the energy-weighted reconstructed image xbins^W and the separable paraboloidal surrogates（SPS） algorithm are proposed for the prior image constrained compres... To improve spectral X-ray CT reconstructed image quality, the energy-weighted reconstructed image xbins^W and the separable paraboloidal surrogates（SPS） algorithm are proposed for the prior image constrained compressed sensing（PICCS）-based spectral X-ray CT image reconstruction. The PICCS-based image reconstruction takes advantage of the compressed sensing theory, a prior image and an optimization algorithm to improve the image quality of CT reconstructions.To evaluate the performance of the proposed method, three optimization algorithms and three prior images are employed and compared in terms of reconstruction accuracy and noise characteristics of the reconstructed images in each energy bin.The experimental simulation results show that the image xbins^W is the best as the prior image in general with respect to the three optimization algorithms; and the SPS algorithm offers the best performance for the simulated phantom with respect to the three prior images. Compared with filtered back-projection（FBP）, the PICCS via the SPS algorithm and xbins^W as the prior image can offer the noise reduction in the reconstructed images up to 80. 46%, 82. 51%, 88. 08% in each energy bin,respectively. M eanwhile, the root-mean-squared error in each energy bin is decreased by 15. 02%, 18. 15%, 34. 11% and the correlation coefficient is increased by 9. 98%, 11. 38%,15. 94%, respectively. 展开更多
关键词 spectral X-ray CT prior image compressed sensing optimization algorithm image reconstruction
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Rising role of artificial intelligence in image reconstruction for biomedical imaging 认领 引用 被引量：1
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作者 Xue-Li Chen Tian-Yu Yan +1 位作者 Nan Wang Karen M von Deneen 《Artificial Intelligence in Medical Imaging》 2020年第1期1-5,共5页
In this editorial,we review recent progress on the applications of artificial intelligence(AI)in image reconstruction for biomedical imaging.Because it abandons prior information of traditional artificial design and a... In this editorial,we review recent progress on the applications of artificial intelligence(AI)in image reconstruction for biomedical imaging.Because it abandons prior information of traditional artificial design and adopts a completely data-driven mode to obtain deeper prior information via learning,AI technology plays an increasingly important role in biomedical image reconstruction.The combination of AI technology and the biomedical image reconstruction method has become a hotspot in the field.Favoring AI,the performance of biomedical image reconstruction has been improved in terms of accuracy,resolution,imaging speed,etc.We specifically focus on how to use AI technology to improve the performance of biomedical image reconstruction,and propose possible future directions in this field. 展开更多
关键词 Biomedical imaging Image reconstruction Artificial intelligence Machine learning Deep learning Tomography
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Multi-channel fast super-resolution image reconstruction based on matrix observation model 认领 引用
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作者 刘洪臣 冯勇 李林静 《Journal of Harbin Institute of Technology(New Series)》 EI CAS 2010年第2期239-246,共8页
A multi-channel fast super-resolution image reconstruction algorithm based on matrix observation model is proposed in the paper,which consists of three steps to avoid the computational complexity: a single image SR re... A multi-channel fast super-resolution image reconstruction algorithm based on matrix observation model is proposed in the paper,which consists of three steps to avoid the computational complexity: a single image SR reconstruction step,a registration step and a wavelet-based image fusion. This algorithm decomposes two large matrixes to the tensor product of two little matrixes and uses the natural isomorphism between matrix space and vector space to transform cost function based on matrix-vector products model to matrix form. Furthermore,we prove that the regularization part can be transformed to the matrix formed. The conjugate-gradient method is used to solve this new model. Finally,the wavelet fusion is used to integrate all the registered highresolution images obtained from the single image SR reconstruction step. The proposed algorithm reduces the storage requirement and the calculating complexity,and can be applied to large-dimension low-resolution images. 展开更多
关键词 super-resolution image reconstruction tensor product wavelet fusion
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Survey of EIT Image Reconstruction Algorithms 认领 引用
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作者 Zhang Mingzhu Ma Yixin +1 位作者 Huang Ningning Ge Hao 《Journal of Shanghai Jiaotong university(Science)》 EI 2022年第2期211-218,共8页
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 rec... 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. 展开更多
关键词 electrical impedance tomography(EIT) image reconstruction inverse problem
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