1. Monga M. Computed tomography - An increasing source of radiation exposure: Editorial comment. Int Braz J Urol. 2007;33(6):855.
2. Oppelt A. Noise in Computed Tomography. In: Aktiengesselschaft S, editor. Imaging Systems for Medical Diagnostics [Internet]. 2nd ed. Publicis Corporate Publishing; 2005. p. 996.
3. Manduca A, Yu L, Trzasko JD, Khaylova N, Kofler JM, McCollough CM, et al. Projection space denoising with bilateral filtering and CT noise modeling for dose reduction in CT. Med Phys. 2009;36(11):4911–9.
4. Manhart M, Fahrig R, Hornegger J, Doerfler A, Maier A. Guided Noise Reduction for Spectral CT with Energy-Selective Photon Counting Detectors. In: Proceedings of the Third CT Meeting [Internet]. 2014. p. 91–4.
5. Ramirez-Giraldo JC, Grant KL, Raupach R. ADMIRE : Advanced Modeled Iterative Reconstruction [Internet]. Forchheim, Germany; 2015. Available from: https://www.siemens-healthineers.com/computed-tomography/technologies-innovations/admire
6. Angel E. AIDR 3D Iterative Reconstruction : Integrated, Automated and Adaptive Dose Reduction. 2012.
7. Maier A, Fahrig R. GPU Denoising for Computed Tomography. In: Xun J, Jiang S, editors. Graphics Processing Unit-Based High Performance Computing in Radiation Therapy. 1st ed. Boca Raton, Florida, USA: CRC Press; 2015. p. 113–28.
8. Zhang Y, Wang G, Zhang W, Li K, Chen H, Liao P, et al. Low-dose CT via convolutional neural network. Biomed Opt Express. 2017;8(2):679–94.
9. Wolterink JM, Leiner T, Viergever MA, Isgum I. Generative Adversarial Networks for Noise Reduction in Low-Dose CT. IEEE Trancations Med Imaging. 2017;36(12):2536–45.
10. Yang Q, Yan P, Zhang Y, Yu H, Shi Y, Mou X, et al. Low-Dose CT Image Denoising Using a Generative Adversarial Network With Wasserstein Distance and Perceptual Loss. IEEE Trans Med Imaging. 2018;37(6):1348–57.
11. Chen H, Zhang Y, Kalra MK, Lin F, Chen Y, Liao P, et al. Low-Dose CT with a Residual Encoder-Decoder Convolutional Neural Network (RED-CNN). IEEE Trans Med Imaging. 2017;36(12):2524–35.
12. Patwari M, Gutjahr R, Raupach R, Maier A. Measuring CT Reconstruction Quality with Deep Convolutional Neural Networks. In: Knoll F, Maier A, Rueckert D, Ye J, editors. Proceedings of the International Workshop on Machine Learning in Medical Image Reconstruction. Shenzhen: Springer; 2019. p. 113–24.
13. Stierstorfer K, Rauscher A, Boese J, Bruder H, Schaller S, Flohr T. Weighted FBP - A simple approximated 3D FBP algorithm for multislice spiral CT with good dose usage for arbitrary pitch. Phys Med Biol. 2004;49(11):2209–18.
14. Shan H, Zhang Y, Yang Q, Kruger U, Kalra MK, Sun L, et al. 3D Convolutional Encoder-Decoder Network for Low-Dose CT via Transfer Learning from a 2D Trained Network. IEEE Trans Med Imaging. 2018;37(6):1522–34.
15. Kingma DP, Bai JL. Adam: A Method for Stochastic Optimization. In: International Conference on Learning Representations. 2015.