Groell R, Willfurth P, Schaffler GJ, Mayer R, Schmidt F, Uggowitzer MM, et al. Contrast-enhanced spiral CT of the head and neck: comparison of contrast material injection rates. AJNR Am J Neuroradiol. 1999;20(9):1732–6.
CAS PubMed PubMed Central Google Scholar
Lakhal K, Ehrmann S, Robert-Edan V. Iodinated contrast medium: is there a re(n)al problem? a clinical vignette-based review. Crit Care. 2020;24(1):641. https://doi.org/10.1186/s13054-020-03365-9.
Article PubMed PubMed Central Google Scholar
Nielsen Y, Thomsen H. Optimal management of acute nonrenal adverse reactions to iodine-based contrast media Reports Med. Imaging. 2013;6:49e55.
Morzycki A, Bhatia A, Murphy KJ. Adverse reactions to contrast material: a canadian update. Can Assoc Radiol J. 2017;68(2):187–93. https://doi.org/10.1016/j.carj.2016.05.006.
Goodfellow J, Pougetbadie M, Mirza B, Xu D, Warde Farley S, Ozair A. Generative adversarial nets. In: Ghahramani Z, Welling M, Cortes C, Lawrence ND, Weinberger KQ, editors. Advances in Neural Information Processing Systems. New York, NY: ACM; 2014.
Zhu JY, Park T, Isola P, Efros AA. Unpaired image-to-image translation using cycle-consistent adversarial networks. In Proceedings of the IEEE International Conference on Computer Vision, Venice, Italy. 2017;22–29:2223–32.
Wang J, Zhao Y, Noble JH, Dawant BM. Conditional generative adversarial networks for metal artifact reduction in CT images of the ear. Med Image Comput Comput Assist Interv. 2018;11070:3–11. https://doi.org/10.1007/978-3-030-00928-1_1.
Article PubMed PubMed Central Google Scholar
Tang C, Li J, Wang L, Li Z, Jiang L, Cai A, et al. Unpaired low-dose CT denoising network based on cycle-consistent generative adversarial network with prior image information. Comput Math Methods Med. 2019;7(2019):8639825. https://doi.org/10.1155/2019/8639825.
Wang J, Noble JH, Dawant BM. Metal artifact reduction for the segmentation of the intra cochlear anatomy in CT images of the ear with 3D-conditional GANs. Med Image Anal. 2019;58: 101553. https://doi.org/10.1016/j.media.2019.101553.
Article PubMed PubMed Central Google Scholar
Kida S, Kaji S, Nawa K, Imae T, Nakamoto T, Ozaki S, et al. Visual enhancement of cone-beam CT by use of CycleGAN. Med Phys. 2020;47(3):998–1010. https://doi.org/10.1002/mp.13963.
Kearney V, Ziemer BP, Perry A, Wang T, Chan JW, Ma L, et al. Attention-aware discrimination for MR-to-CT image translation using cycle-consistent generative adversarial networks. Radiol Artif Intell. 2020;2(2): e190027. https://doi.org/10.1148/ryai.2020190027.
Article PubMed PubMed Central Google Scholar
Li Z, Shi W, Xing Q, Miao Y, He W, Yang H, et al. Low-dose CT image denoising with improving WGAN and hybrid loss function. Comput Math Methods Med. 2021;26(2021):2973108. https://doi.org/10.1155/2021/2973108.
Haubold J, Hosch R, Umutlu L, Wetter A, Haubold P, Radbruch A, et al. Contrast agent dose reduction in computed tomography with deep learning using a conditional generative adversarial network. Eur Radiol. 2021;31(8):6087–95. https://doi.org/10.1007/s00330-021-07714-2.
Article CAS PubMed PubMed Central Google Scholar
Choi JW, Cho YJ, Ha JY, Lee SB, Lee S, Choi YH, et al. Generating synthetic contrast enhancement from non-contrast chest computed tomography using a generative adversarial network. Sci Rep. 2021;11(1):20403. https://doi.org/10.1038/s41598-021-00058-3.
Article CAS PubMed PubMed Central Google Scholar
Denck J, Guehring J, Maier A, Rothgang E. MR-contrast-aware image-to-image translations with generative adversarial networks. Int J Comput Assist Radiol Surg. 2021;16(12):2069–78. https://doi.org/10.1007/s11548-021-02433-x.
Article PubMed PubMed Central Google Scholar
Gomi T, Kijima Y, Kobayashi T, Koibuchi Y. Evaluation of a generative adversarial network to improve image quality and reduce radiation-dose during digital breast tomosynthesis. Diagnostics (Basel). 2022;12(2):495. https://doi.org/10.3390/diagnostics12020495.
Huang Z, Zhang G, Lin J, Pang Y, Wang H, Bai T, et al. Multi-modal feature-fusion for CT metal artifact reduction using edge-enhanced generative adversarial networks. Comput Methods Programs Biomed. 2022;217: 106700. https://doi.org/10.1016/j.cmpb.2022.106700.
Wang X, Yu Z, Wang L, Zheng P. An enhanced priori knowledge GAN for CT images generation of early lung nodules with small-size labelled samples. Oxid Med Cell Longev. 2022;14(2022):2129303. https://doi.org/10.1155/2022/2129303.
Li J, Qu Z, Yang Y, Zhang F, Li M, Hu S. TCGAN: a transformer-enhanced GAN for PET synthetic CT. Biomed Opt Express. 2022;13(11):6003–18. https://doi.org/10.1364/BOE.467683.
Article PubMed PubMed Central Google Scholar
Wang Z, Bovik AC, Sheikh HR, Simoncelli EP. Image quality assessment: from error visibility to structural similarity. IEEE Trans Image Process. 2004;13:600–12.
Fleiss JL. Measuring nominal scale agreement among many raters. Psychol Bull. 1971;76:378–82.
Landis JR, Koch GG. The measurement of observer agreement for categorical data. Biometrics. 1977;33:159–74.
Article CAS PubMed Google Scholar
Hosmer DW Jr, Lemeshow S, Sturdivant RX. Applied logistic regression. 3rd ed. New York: Wiley; 2013. p. 177.
Power M, Fell G, Wright M. Principles for high-quality, high-value testing. Evidence Based Medicine. 2013;18(1):5–10.
Article PubMed PubMed Central Google Scholar
Kim HS, Ha EG, Lee A, Choi YJ, Jeon KJ, Han SS, et al. Refinement of image quality in panoramic radiography using a generative adversarial network. Dentomaxillofac Radiol. 2023;52:20230007.
Comments (0)