Sinogram Interpolation Method for Sparse-Angle Tomography

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Applied Mathematics

ISSN Print: 2152-7385
ISSN Online: 2152-7393
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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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[13]	Non-iterative image reconstruction from sparse magnetic resonance imaging radial data without priors
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[14]	Artifact removal in sparse-angle CT based on feature fusion residual network
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[15]	Sinogram interpolation method for limited-angle tomography
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[16]	Sparse-View Reconstruction in Dental Computed Tomography by Using a Dictionary-Learning Based Method
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[17]	A convolutional neural network for fast upsampling of undersampled tomograms in X-ray CT time-series using a representative highly sampled tomogram
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[18]	Sparse-View CT Reconstruction Based on Mojette Transfrom Using Convolutional Neural Network
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[19]	Tomographic Image Reconstruction in the Case of Limited Number of X-Ray Projections Using Sinogram Inpainting
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[20]	Temporally downsampled cerebral CT perfusion image restoration using deep residual learning
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[21]	Sparse-view tomography via displacement function interpolation
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[22]	Deep-Neural-Network-Based Sinogram Synthesis for Sparse-View CT Image Reconstruction
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[23]	Directional Sinogram Inpainting for Limited Angle Tomography
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[24]	SIPID: A deep learning framework for sinogram interpolation and image denoising in low-dose CT reconstruction
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[25]	Deep-neural-network based sinogram synthesis for sparse-view CT image reconstruction
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[26]	Sparse detector configuration in SiPM digital photon counting PET: a feasibility study
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[27]	Analytic Computed Tomography Reconstruction in Sparse-Angular Sampling Using a Sinogram-Normalization Interpolation Method
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[28]	Phase-unwrapping-free and iterative reconstruction methods for propagated refractive index tomography
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[29]	Interpolation based enhancement of sparse-view projection data for low dose FBP reconstruction
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[30]	Design of an x-ray CT system with implementation of sparse-data reconstruction
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[31]	Artifact reduction based on sinogram interpolation for the 3D reconstruction of nanoparticles using electron tomography
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[32]	Image Reconstruction of Sinogram Restoration using Inpainting method in Sparse View CT
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[33]	View-interpolation of sparsely sampled sinogram using convolutional neural network
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[34]	Development of an Analytic Software Using Pencil Beam Scanning Proton Beam
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[35]	Feasibility study of sparse-angular sampling and sinogram interpolation in material decomposition with a photon-counting detector
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[36]	Limited Tomography Reconstruction via Tight Frame and Simultaneous Sinogram Extrapolation
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[37]	Limited Tomography Reconstruction via Tight Frame and Sinogram Extrapolation
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[38]	Feasibility study of sparse-angular sampling and sonogram interpolation in material decomposition with a photon-counting detector
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[39]	Improvement of Analytic Reconstruction Algorithms Using a Sinogram Interpolation Method for Sparse-angular Sampling with a Photon-counting Detector
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[40]	Interpolation method in simple computed tomography scanner
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[41]	3D image reconstruction on x-ray micro-computed tomography
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[42]	Investigation of sparse-angle view in cone beam computed tomography (CBCT) reconstruction algorithm using a sinogram interpolaton method
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[43]	TGV sinogram inpainting for limited angle tomography
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[44]	Studies of automatic dental cavity detection system as an auxiliary tool for diagnosis of dental caries in digital x-ray image
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[45]	FAST SCANNING AND QUICK SAMPLING IN COMPUTED TOMOGRAPHY
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[46]	Deep learning based sinogram interpolation applied to X-ray CT measurements of polymer additive manufacturing parts

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

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

[3]	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

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

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

[6]	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

[7]	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

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

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

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

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

[12]	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

[13]	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

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

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

[16]	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

[17]	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

[18]	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

[19]	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

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

[21]	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

[22]	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

[23]	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

[24]	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

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

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

[27]	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

[28]	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

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

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

[31]	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

[32]	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

[33]	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

[34]	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

[35]	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

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

[37]	3D image reconstruction on x-ray micro-computed tomography International Conference on Experimental Mechanics 2014, 2015 DOI:10.1117/12.2081193

[38]	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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