Sinogram Interpolation Method for Sparse-Angle Tomography

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Applied Mathematics

ISSN Print: 2152-7385
ISSN Online: 2152-7393
www.scirp.org/journal/am
E-mail: am@scirp.org

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"Sinogram Interpolation Method for Sparse-Angle Tomography"
written by Martti Kalke, Samuli Siltanen,
published by Applied Mathematics, Vol.5 No.3, 2014

has been cited by the following article(s):

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[1]	Sparse-view cone-beam CT reconstruction by bar-by-bar neural FDK algorithm
	… Testing and Evaluation, 2024

[2]	Machine learning based sinogram interpolation for X-ray computed tomography validated on experimental data
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[3]	Sparse-view tomographic reconstruction using residual U-Net with attention gates
	Medical Imaging 2024: Image Processing, 2024

[4]	Measurement of the transverse phase space based on machine learning
	强激光与粒子束, 2023

[5]	Circular LSTM for Low-Dose Sinograms Inpainting
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[6]	Sinogram Interpolation Inspired by Single-Image Super Resolution
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[7]	基于机器学习的束团横向相空间测量
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[8]	PIE-ARNet: Prior image enhanced artifact removal network for limited-angle DECT
	IEEE Transactions …, 2022

[9]	Difference between Dual and Triple Detector in Off-set Acquisition for SPECT
	2022 IEEE Nuclear …, 2022

[10]	Practical part-specific trajectory optimization for robot-guided inspection via computed tomography
	Journal of Nondestructive …, 2022

[11]	PGNet: Projection generative network for sparse‐view reconstruction of projection‐based magnetic particle imaging
	Medical …, 2022

[12]	Learning CT projection denoising from adjacent views
	Medical Physics, 2022

[13]	Hepatic dual-contrast CT imaging: slow triple kVp switching CT with CNN-based sinogram completion and material decomposition
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[14]	Inverse Problems and Dynamical Systems in Tomography and Optics
	2022

[15]	Deep learning for tomographic reconstruction with limited data
	2022

[16]	Trajectory Optimization for Sparsely Sampled Computed Tomography
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[17]	Deep learning based sinogram interpolation applied to X-ray CT measurements of polymer additive manufacturing parts
	2021

[18]	Novel FBP based sparse-view CT reconstruction scheme using self-shaping spatial filter based morphological operations and scaled reprojections
	2021

[19]	Tomographic system for transparent objects using visible light as a source of radiation
	2021

[20]	Deep denoising for multi-dimensional synchrotron X-ray tomography without high-quality reference data
	2021

[21]	Dense U-Net for Limited Angle Tomography of Sound Pressure Fields
	2021

[22]	3D Multimodal X-ray Imaging of a Microchip: Correlating Structure, Composition and Orientation at the Nanoscale
	2020

[23]	Non-iterative image reconstruction from sparse magnetic resonance imaging radial data without priors
	2020

[24]	Artifact removal in sparse-angle CT based on feature fusion residual network
	2020

[25]	A machine learning supported sinogram interpolation method for X-ray computed tomography
	2020

[26]	Sinogram interpolation method for limited-angle tomography
	2019

[27]	Sparse-View Reconstruction in Dental Computed Tomography by Using a Dictionary-Learning Based Method
	2019

[28]	A convolutional neural network for fast upsampling of undersampled tomograms in X-ray CT time-series using a representative highly sampled tomogram
	2019

[29]	Sparse-View CT Reconstruction Based on Mojette Transfrom Using Convolutional Neural Network
	2019

[30]	Tomographic Image Reconstruction in the Case of Limited Number of X-Ray Projections Using Sinogram Inpainting
	2019

[31]	Temporally downsampled cerebral CT perfusion image restoration using deep residual learning
	2019

[32]	Sparse-view tomography via displacement function interpolation
	2019

[33]	Deep-Neural-Network-Based Sinogram Synthesis for Sparse-View CT Image Reconstruction
	2018

[34]	Directional Sinogram Inpainting for Limited Angle Tomography
	2018

[35]	SIPID: A deep learning framework for sinogram interpolation and image denoising in low-dose CT reconstruction
	2018

[36]	Deep-neural-network based sinogram synthesis for sparse-view CT image reconstruction
	2018

[37]	Sparse detector configuration in SiPM digital photon counting PET: a feasibility study
	Molecular Imaging and Biology, 2018

[38]	Analytic Computed Tomography Reconstruction in Sparse-Angular Sampling Using a Sinogram-Normalization Interpolation Method
	Journal of the Korean Physical Society, 2018

[39]	Phase-unwrapping-free and iterative reconstruction methods for propagated refractive index tomography
	Japanese Journal of Applied Physics, 2018

[40]	Interpolation based enhancement of sparse-view projection data for low dose FBP reconstruction
	2017

[41]	Design of an x-ray CT system with implementation of sparse-data reconstruction
	2017

[42]	Artifact reduction based on sinogram interpolation for the 3D reconstruction of nanoparticles using electron tomography
	Particle & Particle Systems Characterization, 2017

[43]	Image Reconstruction of Sinogram Restoration using Inpainting method in Sparse View CT
	2017

[44]	View-interpolation of sparsely sampled sinogram using convolutional neural network
	2017

[45]	Development of an Analytic Software Using Pencil Beam Scanning Proton Beam
	Progress in Medical Physics, 2017

[46]	Feasibility study of sparse-angular sampling and sinogram interpolation in material decomposition with a photon-counting detector
	2016

[47]	Limited Tomography Reconstruction via Tight Frame and Simultaneous Sinogram Extrapolation
	Journal of Computational Mathematics, 2016

[48]	Limited Tomography Reconstruction via Tight Frame and Sinogram Extrapolation
	arXiv preprint arXiv:1602.07049, 2016

[49]	Feasibility study of sparse-angular sampling and sonogram interpolation in material decomposition with a photon-counting detector
	2016

[50]	Improvement of Analytic Reconstruction Algorithms Using a Sinogram Interpolation Method for Sparse-angular Sampling with a Photon-counting Detector
	Progress in Medical Physics, 2016

[51]	Interpolation method in simple computed tomography scanner
	International Conference on Experimental Mechanics 2014, 2015

[52]	3D image reconstruction on x-ray micro-computed tomography
	International Conference on Experimental Mechanics 2014, 2015

[53]	Investigation of sparse-angle view in cone beam computed tomography (CBCT) reconstruction algorithm using a sinogram interpolaton method
	World Congress on Medical Physics and Biomedical Engineering, June 7-12, 2015, Toronto, Canada, 2015

[54]	TGV sinogram inpainting for limited angle tomography
	2015

[55]	Studies of automatic dental cavity detection system as an auxiliary tool for diagnosis of dental caries in digital x-ray image
	2015

[56]	FAST SCANNING AND QUICK SAMPLING IN COMPUTED TOMOGRAPHY
	2014

[1]	Sparse-View Cone-Beam CT Reconstruction by Bar-by-Bar Neural FDK Algorithm Nondestructive Testing and Evaluation, 2024 DOI:10.1080/10589759.2023.2195646

[2]	Machine learning based sinogram interpolation for X-ray computed tomography validated on experimental data Precision Engineering, 2024 DOI:10.1016/j.precisioneng.2024.02.020

[3]	Dual-Domain Collaborative Diffusion Sampling for Multi-Source Stationary Computed Tomography Reconstruction IEEE Transactions on Medical Imaging, 2024 DOI:10.1109/TMI.2024.3420411

[4]	Projection Image Synthesis Using Adversarial Learning Based Spatial Transformer Network For Sparse Angle Sampling CT 2024 46th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 2024 DOI:10.1109/EMBC53108.2024.10782486

[5]	Circular LSTM for Low-Dose Sinograms Inpainting IEEE Access, 2023 DOI:10.1109/ACCESS.2023.3295246

[6]	Circular LSTM for Low-Dose Sinograms Inpainting IEEE Access, 2023 DOI:10.1109/ACCESS.2023.3295246

[7]	SPEAR-Net: Self-Prior Enhanced Artifact Removal Network for Limited-Angle DECT IEEE Transactions on Instrumentation and Measurement, 2023 DOI:10.1109/TIM.2023.3260280

[8]	Learning CT projection denoising from adjacent views Medical Physics, 2023 DOI:10.1002/mp.16115

[9]	Sparse-View Cone-Beam CT Reconstruction by Bar-by-Bar Neural FDK Algorithm Nondestructive Testing and Evaluation, 2023 DOI:10.1080/10589759.2023.2195646

[10]	SPEAR-Net: Self-Prior Enhanced Artifact Removal Network for Limited-Angle DECT IEEE Transactions on Instrumentation and Measurement, 2023 DOI:10.1109/TIM.2023.3260280

[11]	Circular LSTM for Low-Dose Sinograms Inpainting IEEE Access, 2023 DOI:10.1109/ACCESS.2023.3295246

[12]	Learning CT projection denoising from adjacent views Medical Physics, 2023 DOI:10.1002/mp.16115

[13]	Practical Part-Specific Trajectory Optimization for Robot-Guided Inspection via Computed Tomography Journal of Nondestructive Evaluation, 2022 DOI:10.1007/s10921-022-00888-9

[14]	PGNet: Projection generative network for sparse‐view reconstruction of projection‐based magnetic particle imaging Medical Physics, 2022 DOI:10.1002/mp.16048

[15]	Learning CT projection denoising from adjacent views Medical Physics, 2022 DOI:10.1002/mp.16115

[16]	Difference between Dual and Triple Detector in Off-set Acquisition for SPECT 2022 IEEE Nuclear Science Symposium and Medical Imaging Conference (NSS/MIC), 2022 DOI:10.1109/NSS/MIC44845.2022.10399314

[17]	Dense U-Net for Limited Angle Tomography of Sound Pressure Fields Applied Sciences, 2021 DOI:10.3390/app11104570

[18]	Deep denoising for multi-dimensional synchrotron X-ray tomography without high-quality reference data Scientific Reports, 2021 DOI:10.1038/s41598-021-91084-8

[19]	Novel FBP based sparse-view CT reconstruction scheme using self-shaping spatial filter based morphological operations and scaled reprojections Biomedical Signal Processing and Control, 2021 DOI:10.1016/j.bspc.2020.102323

[20]	Artifact Removal in Sparse-Angle CT Based on Feature Fusion Residual Network IEEE Transactions on Radiation and Plasma Medical Sciences, 2021 DOI:10.1109/TRPMS.2020.3000789

[21]	Dense U-Net for Limited Angle Tomography of Sound Pressure Fields Applied Sciences, 2021 DOI:10.3390/app11104570

[22]	Deep denoising for multi-dimensional synchrotron X-ray tomography without high-quality reference data Scientific Reports, 2021 DOI:10.1038/s41598-021-91084-8

[23]	Tomographic system for transparent objects using visible light as a source of radiation Journal of Physics: Conference Series, 2021 DOI:10.1088/1742-6596/1943/1/012045

[24]	Non-iterative image reconstruction from sparse magnetic resonance imaging radial data without priors Visual Computing for Industry, Biomedicine, and Art, 2020 DOI:10.1186/s42492-020-00044-y

[25]	Deep-Neural-Network-Based Sinogram Synthesis for Sparse-View CT Image Reconstruction IEEE Transactions on Radiation and Plasma Medical Sciences, 2019 DOI:10.1109/TRPMS.2018.2867611

[26]	Sparse-View Reconstruction in Dental Computed Tomography by Using a Dictionary-Learning Based Method Journal of the Korean Physical Society, 2019 DOI:10.3938/jkps.74.57

[27]	Directional sinogram inpainting for limited angle tomography Inverse Problems, 2019 DOI:10.1088/1361-6420/aaf2fe

[28]	A convolutional neural network for fast upsampling of undersampled tomograms in X-ray CT time-series using a representative highly sampled tomogram Journal of Synchrotron Radiation, 2019 DOI:10.1107/S1600577519003448

[29]	Sparse-View CT Reconstruction Based on Mojette Transfrom Using Convolutional Neural Network 2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019), 2019 DOI:10.1109/ISBI.2019.8759546

[30]	Tomographic Image Reconstruction in the Case of Limited Number of X-Ray Projections Using Sinogram Inpainting Russian Journal of Nondestructive Testing, 2019 DOI:10.1134/S1061830919070027

[31]	Temporally downsampled cerebral CT perfusion image restoration using deep residual learning International Journal of Computer Assisted Radiology and Surgery, 2019 DOI:10.1007/s11548-019-02082-1

[32]	Sparse-view tomography via displacement function interpolation Visual Computing for Industry, Biomedicine, and Art, 2019 DOI:10.1186/s42492-019-0024-7

[33]	Phase-unwrapping-free and iterative reconstruction methods for propagated refractive index tomography Japanese Journal of Applied Physics, 2018 DOI:10.7567/JJAP.57.09SB03

[34]	SIPID: A deep learning framework for sinogram interpolation and image denoising in low-dose CT reconstruction 2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018), 2018 DOI:10.1109/ISBI.2018.8363862

[35]	Analytic Computed Tomography Reconstruction in Sparse-Angular Sampling Using a Sinogram-Normalization Interpolation Method Journal of the Korean Physical Society, 2018 DOI:10.3938/jkps.73.361

[36]	Sparse Detector Configuration in SiPM Digital Photon Counting PET: a Feasibility Study Molecular Imaging and Biology, 2018 DOI:10.1007/s11307-018-1250-7

[37]	View-interpolation of sparsely sampled sinogram using convolutional neural network Medical Imaging 2017: Image Processing, 2017 DOI:10.1117/12.2254244

[38]	Artifact Reduction Based on Sinogram Interpolation for the 3D Reconstruction of Nanoparticles Using Electron Tomography Particle & Particle Systems Characterization, 2017 DOI:10.1002/ppsc.201700287

[39]	Interpolation based enhancement of sparse-view projection data for low dose FBP reconstruction 2017 International Conference on Innovations in Electrical Engineering and Computational Technologies (ICIEECT), 2017 DOI:10.1109/ICIEECT.2017.7916534

[40]	Artifact Reduction Based on Sinogram Interpolation for the 3D Reconstruction of Nanoparticles Using Electron Tomography Particle & Particle Systems Characterization, 2017 DOI:10.1002/ppsc.201700287

[41]	Improvement of Analytic Reconstruction Algorithms Using a Sinogram Interpolation Method for Sparse-angular Sampling with a Photon-counting Detector Progress in Medical Physics, 2016 DOI:10.14316/pmp.2016.27.3.105

[42]	Feasibility study of sparse-angular sampling and sinogram interpolation in material decomposition with a photon-counting detector Medical Imaging 2016: Physics of Medical Imaging, 2016 DOI:10.1117/12.2216634

[43]	Fast scanning and quick sampling in computed tomography International Conference on Experimental Mechanics 2014, 2015 DOI:10.1117/12.2081194

[44]	3D image reconstruction on x-ray micro-computed tomography International Conference on Experimental Mechanics 2014, 2015 DOI:10.1117/12.2081193

[45]	Interpolation method in simple computed tomography scanner International Conference on Experimental Mechanics 2014, 2015 DOI:10.1117/12.2076910

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	customer@scirp.org
	+86 18163351462(WhatsApp)
	1655362766

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