A Fast, Scalable, and Robust Deep Learning-based Iterative Reconstruction Framework for Accelerated Industrial Cone-beam X-ray Computed Tomography | Paper Citation Record

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This paper cites Enabling rapid X-ray CT characterisation for additive manufacturing using CAD models and deep learning-based reconstruction,.

A Fast, Scalable, and Robust Deep Learning-based Iterative Reconstruction Framework for Accelerated Industrial Cone-beam X-ray Computed Tomography Enabling rapid X-ray CT characterisation for additive manufacturing using CAD models and deep learning-based reconstruction,

Reference 1

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This paper cites Geometric tolerance and manufacturing assemblability estimation of metal additive manufacturing (AM) processes,.

A Fast, Scalable, and Robust Deep Learning-based Iterative Reconstruction Framework for Accelerated Industrial Cone-beam X-ray Computed Tomography Geometric tolerance and manufacturing assemblability estimation of metal additive manufacturing (AM) processes,

Reference 2

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This paper cites Assembly analysis of titanium dental implants using X-ray computed tomography,.

A Fast, Scalable, and Robust Deep Learning-based Iterative Reconstruction Framework for Accelerated Industrial Cone-beam X-ray Computed Tomography Assembly analysis of titanium dental implants using X-ray computed tomography,

Reference 3

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This paper cites On the use of x-ray computed tomography in assessment of 3d-printed components,.

A Fast, Scalable, and Robust Deep Learning-based Iterative Reconstruction Framework for Accelerated Industrial Cone-beam X-ray Computed Tomography On the use of x-ray computed tomography in assessment of 3d-printed components,

Reference 4

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This paper cites The use of x-ray computed tomography for design and process modeling of aerospace composites: A review,.

A Fast, Scalable, and Robust Deep Learning-based Iterative Reconstruction Framework for Accelerated Industrial Cone-beam X-ray Computed Tomography The use of x-ray computed tomography for design and process modeling of aerospace composites: A review,

Reference 5

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This paper cites Practical cone-beam algorithm,.

A Fast, Scalable, and Robust Deep Learning-based Iterative Reconstruction Framework for Accelerated Industrial Cone-beam X-ray Computed Tomography Practical cone-beam algorithm,

Reference 6

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This paper cites Algorithm-driven advances for scientific ct instruments: From model-based to deep learning- based approaches,.

A Fast, Scalable, and Robust Deep Learning-based Iterative Reconstruction Framework for Accelerated Industrial Cone-beam X-ray Computed Tomography Algorithm-driven advances for scientific ct instruments: From model-based to deep learning- based approaches,

Reference 7

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This paper cites Fast model-based X-ray CT reconstruction using spatially nonhomogeneous ICD optimization,.

A Fast, Scalable, and Robust Deep Learning-based Iterative Reconstruction Framework for Accelerated Industrial Cone-beam X-ray Computed Tomography Fast model-based X-ray CT reconstruction using spatially nonhomogeneous ICD optimization,

Reference 8

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This paper cites A generalized gaussian image model for edge- preserving map estimation,.

A Fast, Scalable, and Robust Deep Learning-based Iterative Reconstruction Framework for Accelerated Industrial Cone-beam X-ray Computed Tomography A generalized gaussian image model for edge- preserving map estimation,

Reference 9

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This paper cites Bayesian estimation of transmission tomo- grams using segmentation based optimization,.

A Fast, Scalable, and Robust Deep Learning-based Iterative Reconstruction Framework for Accelerated Industrial Cone-beam X-ray Computed Tomography Bayesian estimation of transmission tomo- grams using segmentation based optimization,

Reference 10

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This paper cites A three- dimensional statistical approach to improved image quality for multislice helical CT,.

A Fast, Scalable, and Robust Deep Learning-based Iterative Reconstruction Framework for Accelerated Industrial Cone-beam X-ray Computed Tomography A three- dimensional statistical approach to improved image quality for multislice helical CT,

Reference 11

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This paper cites Deep learning based workflow for accelerated industrial X-ray Computed Tomography,.

A Fast, Scalable, and Robust Deep Learning-based Iterative Reconstruction Framework for Accelerated Industrial Cone-beam X-ray Computed Tomography Deep learning based workflow for accelerated industrial X-ray Computed Tomography,

Reference 12

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This paper cites Learning proximal operators: Using denoising networks for regularizing inverse imaging problems,.

A Fast, Scalable, and Robust Deep Learning-based Iterative Reconstruction Framework for Accelerated Industrial Cone-beam X-ray Computed Tomography Learning proximal operators: Using denoising networks for regularizing inverse imaging problems,

Reference 13

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This paper cites Denoising prior driven deep neural network for image restoration,.

A Fast, Scalable, and Robust Deep Learning-based Iterative Reconstruction Framework for Accelerated Industrial Cone-beam X-ray Computed Tomography Denoising prior driven deep neural network for image restoration,

Reference 14

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This paper cites Learning deep cnn denoiser prior for image restoration,.

A Fast, Scalable, and Robust Deep Learning-based Iterative Reconstruction Framework for Accelerated Industrial Cone-beam X-ray Computed Tomography Learning deep cnn denoiser prior for image restoration,

Reference 15

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This paper cites Deep convolutional neural network for inverse problems in imaging,.

A Fast, Scalable, and Robust Deep Learning-based Iterative Reconstruction Framework for Accelerated Industrial Cone-beam X-ray Computed Tomography Deep convolutional neural network for inverse problems in imaging,

Reference 16

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This paper cites A deep convolutional neural network using directional wavelets for low-dose X-ray CT reconstruction,.

A Fast, Scalable, and Robust Deep Learning-based Iterative Reconstruction Framework for Accelerated Industrial Cone-beam X-ray Computed Tomography A deep convolutional neural network using directional wavelets for low-dose X-ray CT reconstruction,

Reference 17

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This paper cites Low-dose CT with a residual encoder-decoder convolu- tional neural network,.

A Fast, Scalable, and Robust Deep Learning-based Iterative Reconstruction Framework for Accelerated Industrial Cone-beam X-ray Computed Tomography Low-dose CT with a residual encoder-decoder convolu- tional neural network,

Reference 18

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This paper cites Convolutional neural networks for inverse problems in imaging: A review,.

A Fast, Scalable, and Robust Deep Learning-based Iterative Reconstruction Framework for Accelerated Industrial Cone-beam X-ray Computed Tomography Convolutional neural networks for inverse problems in imaging: A review,

Reference 19

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This paper cites Using deep neural networks for inverse problems in imaging: beyond analytical methods,.

A Fast, Scalable, and Robust Deep Learning-based Iterative Reconstruction Framework for Accelerated Industrial Cone-beam X-ray Computed Tomography Using deep neural networks for inverse problems in imaging: beyond analytical methods,

Reference 20

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This paper cites U-net: Convolutional networks for biomedical image segmentation,.

A Fast, Scalable, and Robust Deep Learning-based Iterative Reconstruction Framework for Accelerated Industrial Cone-beam X-ray Computed Tomography U-net: Convolutional networks for biomedical image segmentation,

Reference 21

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A Fast, Scalable, and Robust Deep Learning-based Iterative Reconstruction Framework for Accelerated Industrial Cone-beam X-ray Computed Tomography Unresolved cited work

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This paper cites Deep convolutional neural network for inverse problems in imaging,.

A Fast, Scalable, and Robust Deep Learning-based Iterative Reconstruction Framework for Accelerated Industrial Cone-beam X-ray Computed Tomography Deep convolutional neural network for inverse problems in imaging,

Reference 23

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A Fast, Scalable, and Robust Deep Learning-based Iterative Reconstruction Framework for Accelerated Industrial Cone-beam X-ray Computed Tomography Learned primal-dual reconstruction,

Reference 24

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This paper cites End-to-end memory-efficient reconstruction for cone beam CT,.

A Fast, Scalable, and Robust Deep Learning-based Iterative Reconstruction Framework for Accelerated Industrial Cone-beam X-ray Computed Tomography End-to-end memory-efficient reconstruction for cone beam CT,

Reference 25

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A Fast, Scalable, and Robust Deep Learning-based Iterative Reconstruction Framework for Accelerated Industrial Cone-beam X-ray Computed Tomography 3D Helical CT Reconstruction with a Memory Efficient Learned Primal-Dual Architecture,

Reference 26

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A Fast, Scalable, and Robust Deep Learning-based Iterative Reconstruction Framework for Accelerated Industrial Cone-beam X-ray Computed Tomography Algorithm unrolling: Interpretable, efficient deep learning for signal and image processing,

Reference 27

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A Fast, Scalable, and Robust Deep Learning-based Iterative Reconstruction Framework for Accelerated Industrial Cone-beam X-ray Computed Tomography Zhang and B

Reference 28

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A Fast, Scalable, and Robust Deep Learning-based Iterative Reconstruction Framework for Accelerated Industrial Cone-beam X-ray Computed Tomography Deep generalization of structured low-rank algorithms (Deep-SLR),

Reference 29

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A Fast, Scalable, and Robust Deep Learning-based Iterative Reconstruction Framework for Accelerated Industrial Cone-beam X-ray Computed Tomography Plug-and-play priors for model based reconstruction,

Reference 30

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A Fast, Scalable, and Robust Deep Learning-based Iterative Reconstruction Framework for Accelerated Industrial Cone-beam X-ray Computed Tomography Plug-and-play priors for bright field electron tomog- raphy and sparse interpolation,

Reference 31

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A Fast, Scalable, and Robust Deep Learning-based Iterative Reconstruction Framework for Accelerated Industrial Cone-beam X-ray Computed Tomography Deep plug-and-play super-resolution for arbitrary blur kernels,

Reference 32

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A Fast, Scalable, and Robust Deep Learning-based Iterative Reconstruction Framework for Accelerated Industrial Cone-beam X-ray Computed Tomography Plug-and-play image restoration with deep denoiser prior,

Reference 33

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A Fast, Scalable, and Robust Deep Learning-based Iterative Reconstruction Framework for Accelerated Industrial Cone-beam X-ray Computed Tomography Plug-and-play methods for integrating physical and learned models in computational imaging: Theory, algorithms, and applications,

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A Fast, Scalable, and Robust Deep Learning-based Iterative Reconstruction Framework for Accelerated Industrial Cone-beam X-ray Computed Tomography Plug-and-play ADMM for image restoration: Fixed-point convergence and applications,

Reference 35

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A Fast, Scalable, and Robust Deep Learning-based Iterative Reconstruction Framework for Accelerated Industrial Cone-beam X-ray Computed Tomography An online plug-and-play algorithm for regularized image reconstruction,

Reference 36

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A Fast, Scalable, and Robust Deep Learning-based Iterative Reconstruction Framework for Accelerated Industrial Cone-beam X-ray Computed Tomography Plug-and-play methods provably converge with properly trained denoisers,

Reference 37

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A Fast, Scalable, and Robust Deep Learning-based Iterative Reconstruction Framework for Accelerated Industrial Cone-beam X-ray Computed Tomography Recovery analysis for plug-and-play priors using the restricted eigenvalue condition,

Reference 38

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A Fast, Scalable, and Robust Deep Learning-based Iterative Reconstruction Framework for Accelerated Industrial Cone-beam X-ray Computed Tomography The little engine that could: Regularization by denoising (RED),

Reference 39

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A Fast, Scalable, and Robust Deep Learning-based Iterative Reconstruction Framework for Accelerated Industrial Cone-beam X-ray Computed Tomography Regularization by denoising: Clarifi- cations and new interpretations,

Reference 40

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This paper cites Multi-slice fusion for sparse-view and limited-angle 4D CT reconstruc- tion,.

A Fast, Scalable, and Robust Deep Learning-based Iterative Reconstruction Framework for Accelerated Industrial Cone-beam X-ray Computed Tomography Multi-slice fusion for sparse-view and limited-angle 4D CT reconstruc- tion,

Reference 41

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This paper cites Sparse-view cone beam CT reconstruction using data-consistent supervised and adversarial learning from scarce training data,.

A Fast, Scalable, and Robust Deep Learning-based Iterative Reconstruction Framework for Accelerated Industrial Cone-beam X-ray Computed Tomography Sparse-view cone beam CT reconstruction using data-consistent supervised and adversarial learning from scarce training data,

Reference 42

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This paper cites A Restoration Network as an Implicit Prior,.

A Fast, Scalable, and Robust Deep Learning-based Iterative Reconstruction Framework for Accelerated Industrial Cone-beam X-ray Computed Tomography A Restoration Network as an Implicit Prior,

Reference 43

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A Fast, Scalable, and Robust Deep Learning-based Iterative Reconstruction Framework for Accelerated Industrial Cone-beam X-ray Computed Tomography RARE: Image reconstruction using deep priors learned without groundtruth,

Reference 44

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This paper cites Plug- and-play unplugged: Optimization-free reconstruction using consensus equilibrium,.

A Fast, Scalable, and Robust Deep Learning-based Iterative Reconstruction Framework for Accelerated Industrial Cone-beam X-ray Computed Tomography Plug- and-play unplugged: Optimization-free reconstruction using consensus equilibrium,

Reference 45

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This paper cites No-reference image quality assessment in the spatial domain,.

A Fast, Scalable, and Robust Deep Learning-based Iterative Reconstruction Framework for Accelerated Industrial Cone-beam X-ray Computed Tomography No-reference image quality assessment in the spatial domain,

Reference 46

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This paper cites Optimal short scan convolution reconstruction for fan beam ct,.

A Fast, Scalable, and Robust Deep Learning-based Iterative Reconstruction Framework for Accelerated Industrial Cone-beam X-ray Computed Tomography Optimal short scan convolution reconstruction for fan beam ct,

Reference 47

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Observation 0962317e-1c52-447e-97bf-697eb565d29e · outbound

This paper cites Model Based Iterative Reconstruction With Spatially Adaptive Sinogram Weights for Wide-Cone Cardiac CT.

A Fast, Scalable, and Robust Deep Learning-based Iterative Reconstruction Framework for Accelerated Industrial Cone-beam X-ray Computed Tomography Model Based Iterative Reconstruction With Spatially Adaptive Sinogram Weights for Wide-Cone Cardiac CT

Reference 48

Resolution: verified exact; local_arxiv, observed 2026-08-10T17:15:37.670660Z

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Observation 32d69d55-ddef-4323-b11b-89646575cd70 · outbound

This paper cites Adam: a method for stochastic optimization,.

A Fast, Scalable, and Robust Deep Learning-based Iterative Reconstruction Framework for Accelerated Industrial Cone-beam X-ray Computed Tomography Adam: a method for stochastic optimization,

Reference 49

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Observation 61bd32eb-62e3-4b0e-90f2-8b6f79e07b21 · outbound

This paper cites The ASTRA Toolbox: A platform for advanced algorithm development in electron tomography,.

A Fast, Scalable, and Robust Deep Learning-based Iterative Reconstruction Framework for Accelerated Industrial Cone-beam X-ray Computed Tomography The ASTRA Toolbox: A platform for advanced algorithm development in electron tomography,

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T17:15:37.721165Z

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Observation d5eb2100-71bc-4da2-aa59-6c103b3b2d33 · outbound

This paper cites Performance improve- ments for iterative electron tomography reconstruction using graphics processing units (gpus),.

A Fast, Scalable, and Robust Deep Learning-based Iterative Reconstruction Framework for Accelerated Industrial Cone-beam X-ray Computed Tomography Performance improve- ments for iterative electron tomography reconstruction using graphics processing units (gpus),

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T17:15:37.709948Z

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Observation 4afdd1c3-7b7b-4706-9929-ab3d0f9786ce · outbound

This paper cites Fast and flexible X-ray tomography using the ASTRA toolbox,.

A Fast, Scalable, and Robust Deep Learning-based Iterative Reconstruction Framework for Accelerated Industrial Cone-beam X-ray Computed Tomography Fast and flexible X-ray tomography using the ASTRA toolbox,

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T17:15:37.699101Z

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Observation 373a3351-9cd0-445a-ba7f-e14ab7343a4b · outbound

This paper cites Image quality assessment: from error visibility to structural similarity,.

A Fast, Scalable, and Robust Deep Learning-based Iterative Reconstruction Framework for Accelerated Industrial Cone-beam X-ray Computed Tomography Image quality assessment: from error visibility to structural similarity,

Reference 53

Resolution: unresolved; no resolver link, observed 2026-08-10T17:15:37.617975Z

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Observation 404761b9-9d5a-404e-abf0-661c04123bc5 · outbound

This paper cites an unresolved cited work.

A Fast, Scalable, and Robust Deep Learning-based Iterative Reconstruction Framework for Accelerated Industrial Cone-beam X-ray Computed Tomography Unresolved cited work

Reference 54

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