Fetching the paper…
Reading the bibliography…
Cone Beam CT plays an important role in many medical fields nowadays, but the potential of this imaging modality is hampered by lower image quality compared to the conventional CT.
The power method for lp norms,
D. W. Boyd, · 1974
Earlier work this paper cites.
An Inversion Formula for Cone-Beam Reconstruction,
H. K. Tuy, · 1983
Earlier work this paper cites.
Practical cone-beam algorithm,
L. A. Feldkamp, L. C. Davis, and J. W. Kress, · 1984
Earlier work this paper cites.
Flat-panel cone-beam computed tomography for image-guided radiation therapy,
D. A. Jaffray, J. H. Siewerdsen, J. W. Wong, and M. A. A, · 2002
Earlier work this paper cites.
Parker weights revisited,
S. Wesarg, M. Ebert, and T. Bortfeld, · 2002
Earlier work this paper cites.
X-ray micro-CT with a displaced detector array,
G. Wang, · 2002
Earlier work this paper cites.
Statistical and Computational Inverse Problems
J. Kaipio and E. Somersalo, · 2005
Earlier work this paper cites.
Cone-beam-CT guided radiation therapy: technical implementation,
D. Létourneau, J. W. Wong, M. Oldham, M. Gulam, L. Watt, D. A. Jaffray, J. H. Siewerdsen, and A. A. Martinez, · 2005
Earlier work this paper cites.
Cone beam CT in dental practice,
A. Dawood, S. Patel, and J. Brown, · 2009
Earlier work this paper cites.
Comparing short scan CT reconstruction algorithms regarding cone-beam artifact performance,
C. Maaß, F. Dennerlein, F. Noo, and M. Kachelrieß, · 2010
Earlier work this paper cites.
A First-Order Primal-Dual Algorithm for Convex Problems with Applications to Imaging,
A. Chambolle and T. Pock, · 2011
Earlier work this paper cites.
C-arm cone-beam computed tomography in interventional oncology: technical aspects and clinical applications,
C. Floridi, A. Radaelli, N. Abi-Jaoudeh, M. Grass, M. Lin, M. Chiaradia, J. F. Geschwind, H. Kobeiter, E. Squillaci, G. Maleux, A. Giovagnoni, L. Brunese, B. Wood, G. Carrafiello, and A. Rotondo, · 2014
Earlier work this paper cites.
Interior micro-CT with an offset detector,
K. Sen Sharma, H. Gong, O. Ghasemalizadeh, H. Yu, G. Wang, and G. Cao, · 2014
Earlier work this paper cites.
Adam: A Method for Stochastic Optimization,
D. P. Kingma and J. Ba, · 2014
Earlier work this paper cites.
3D U-Net: Learning Dense Volumetric Segmentation from Sparse Annotation,
Ö. Çiçek, A. Abdulkadir, S. S. Lienkamp, T. Brox, and O. Ronneberger, · 2016
Earlier work this paper cites.
Fast and flexible X-ray tomography using the ASTRA toolbox,
W. van Aarle, W. J. Palenstijn, J. Cant, E. Janssens, F. Bleichrodt, A. Dabravolski, J. D. Beenhouwer, K. J. Batenburg, and J. Sijbers, · 2016
Cited alongside, same era.
Deep Convolutional Neural Network for Inverse Problems in Imaging,
K. H. Jin, M. T. McCann, E. Froustey, and M. Unser, · 2017
Cited alongside, same era.
ODL 0.6.0, 2017
J. Adler, H. Kohr, and O. Öktem, · 2017
Cited alongside, same era.
The Reversible Residual Network: Backpropagation Without Storing Activations,
A. N. Gomez, M. Ren, R. Urtasun, and R. B. Grosse, · 2017
Cited alongside, same era.
Adversarial Regularizers in Inverse Problems,
S. Lunz, O. Öktem, and C.-B. Schönlieb, · 2018
Cited alongside, same era.
Statistical Iterative CBCT Reconstruction Based on Neural Network,
MemCNN: A Python/PyTorch package for creating memory-efficient invertible neural networks,
S. C. v. Leemput, J. Teuwen, B. v. Ginneken, and R. Manniesing, · 2019
Later among the works it cites.
AirNet: Fused analytical and iterative reconstruction with deep neural network regularization for sparse-data CT,
G. Chen, X. Hong, Q. Ding, Y. Zhang, H. Chen, S. Fu, Y. Zhao, X. Zhang, H. Ji, G. Wang, Q. Huang, and H. Gao, · 2020
Later among the works it cites.
Results of the 2020 fastMRI Challenge for Machine Learning MR Image Reconstruction,
M. J. Muckley et al., · 2020
Later among the works it cites.
Multi-channel MR Reconstruction (MC-MRRec) Challenge – Comparing Accelerated MR Reconstruction Models and Assessing Their Genereralizability to Datasets Collected with Different Coils, 2020
Y. Beauferris, J. Teuwen, D. Karkalousos, N. Moriakov, M. Caan, L. Rodrigues, A. Lopes, H. Pedrini, L. Rittner, M. Dannecker, V. Studenyak, F. Gröger, D. Vyas, S. Faghih-Roohi, A. K. Jethi, J. C. Raju, M. Sivaprakasam, W. Loos, R. Frayne, and R. Souza, · 2020
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
B. Chen, K. Xiang, Z. Gong, J. Wang, and S. Tan, · 2018
Cited alongside, same era.
Learned Primal-Dual Reconstruction,
J. Adler and O. Öktem, · 2018
Cited alongside, same era.
Deep scatter estimation (DSE): feasibility of using a deep convolutional neural network for real-time x-ray scatter prediction in cone-beam CT,
J. Maier, Y. Berker, S. Sawall, and M. Kachelrieß, · 2018
Cited alongside, same era.
Adaptive Radiotherapy for Anatomical Changes,
J. J. Sonke, M. Aznar, and C. Rasch, · 2019
Cited alongside, same era.
Solving inverse problems using data-driven models,
S. Arridge, P. Maass, O. Öktem, and C.-B. Schönlieb, · 2019
Cited alongside, same era.
Computationally efficient deep neural network for computed tomography image reconstruction,
D. Wu, K. Kim, and Q. Li, · 2019
Cited alongside, same era.
Streaming convolutional neural networks for end-to-end learning with multi-megapixel images,
H. Pinckaers, B. van Ginneken, and G. Litjens, · 2019
Cited alongside, same era.
Noise2Inverse: Self-Supervised Deep Convolutional Denoising for Tomography,
A. A. Hendriksen, D. M. Pelt, and K. J. Batenburg, · 2020
Later among the works it cites.
Benchmarking MRI Reconstruction Neural Networks on Large Public Datasets,
Z. Ramzi, P. Ciuciu, and J.-L. Starck, · 2020
Later among the works it cites.
Multi-Scale Learned Iterative Reconstruction,
A. Hauptmann, J. Adler, S. Arridge, and O. Öktem, · 2020
Later among the works it cites.
CycN-Net: A Convolutional Neural Network Specialized for 4D CBCT Images Refinement,
S. Zhi, M. Kachelrieß, F. Pan, and X. Mou, · 2021
Later among the works it cites.
Deep learning reconstruction of digital breast tomosynthesis images for accurate breast density and patient-specific radiation dose estimation,
J. Teuwen, N. Moriakov, C. Fedon, M. Caballo, I. Reiser, P. Bakic, E. García, O. Diaz, K. Michielsen, and I. Sechopoulos, · 2021
Later among the works it cites.
Invertible Learned Primal-Dual, 2021
J. Rudzusika, B. Bajić, O. Öktem, C.-B. Schönlieb, and C. Etmann, · 2021
Later among the works it cites.
A more effective CT synthesizer using transformers for cone-beam CT-guided adaptive radiotherapy,
X. Chen, Y. Liu, B. Yang, J. Zhu, S. Yuan, X. Xie, Y. Liu, J. Dai, K. Men, · 2022
Closest in time.
3D helical CT reconstruction with memory efficient invertible Learned Primal-Dual method, 2022
B. Bajić, O. Öktem, and J. Rudzusika, · 2022
Closest in time.
Uformer: A General U-Shaped Transformer for Image Restoration,
Z. Wang, X. Cun, J. Bao, W. Zhou, J. Liu, and H. Li, · 2022
Closest in time.
Deep learning framework to improve the quality of cone-beam computed tomography for radiotherapy scenarios,
B. Yang, Y. Liu, J. Zhu, J. Dai, and K. Men, · 2023
Closest in time.