Kulathike, K.A.S.H., Abdullah, N.A., Sabri, A.Q.M.S., Lai, K.W.: A review on Deep Learning Approaches for Low-Dose Computed Tomography Restoration. Complex & Intelligent Systems (2021) https://doi.org/10.1007/s40747-021-00405-x

Article  Google Scholar 

Trindade, H., Barreira, M., Xavier, C.: Head and abdomen CT exposures in Portugal. Journal of radiological protection 42(2), 21514 (2022)

Article  Google Scholar 

Hauptmann, M., Byrnes, G., Cardis, E., Bernier, M.-O., Blettner, M., Dabin, J., Engels, H., Istad, T.S., Johansen, C., Kaijser, M., Kjaerheim, K., Journy, N., Meulepas, J.M., Moissonnier, M., Ronckers, C., Thierry-Chef, I., Le Cornet, L., Jahnen, A., Pokora, R., Basea, M., Figuerola, J., Maccia, C., Nordenskjold, A., Harbron, R.W., Lee, C., Simon, S.L., Gonzalez, A., Schüz, J., Kesminiene, A.: Brain cancer after radiation exposure from CT examinations of children and young adults: results from the EPI-CT cohort study. The lancet oncology 24(1), 45–53 (2023)

Article  PubMed  Google Scholar 

Smith-Bindman, R., Kwan, M.L., Marlow, E.C., Theis, M.K., Bolch, W., Cheng, S.Y., Bowles, E.J.A., Duncan, J.R., Greenlee, R.T., Kushi, L.H., Pole, J.D., Rahm, A.K., Stout, N.K., Weinmann, S., Miglioretti, D.L.: Trends in Use of Medical Imaging in US Health Care Systems and in Ontario, Canada, 2000-2016. JAMA 322(9), 843–856 (2019) https://doi.org/10.1001/jama.2019.11456

Article  PubMed  PubMed Central  Google Scholar 

McCollough, C.H., Chen, B., Holmes, D., III Duan, X., Yu, Z., Yu, L., Leng, S., Fletcher, J.: Data from Low Dose CT Image and Projection Data [Data Set]. The Cancer Imaging Archive. Dataset (2021). https://doi.org/10.7937/9npb-2637

Article  Google Scholar 

Inoue, Y.: Radiation Dose Modulation of Computed Tomography Component in Positron Emission Tomography/Computed Tomography. Seminars in nuclear medicine 52(2), 157–166 (2022)

Article  PubMed  Google Scholar 

Dhawan, A.P.: Medical Imaging Modalities: X-Ray Imaging, 79–97 (2011) https://doi.org/10.1002/9780470918548.ch4

Yang, L., Li, Z., Ge, R., Zhao, J., Si, H., Zhang, D.: Low-dose ct denoising via sinogram inner-structure transformer. IEEE Transactions on Medical Imaging, 1–1 (2022) https://doi.org/10.1109/TMI.2022.3219856

Liu, J., Zhang, Y., Zhao, Q., Lv, T., Wu, W., Cai, N., Quan, G., Yang, W., Chen, Y., Luo, L., Shu, H., Coatrieux, J.-L.: Deep iterative reconstruction estimation (DIRE): approximate iterative reconstruction estimation for low dose CT imaging. Physics in Medicine & Biology 64(13), 135007 (2019) https://doi.org/10.1088/1361-6560/ab18db

Article  Google Scholar 

Wang, J., Lu, H., Li, T., Liang, Z.: Sinogram Noise Reduction for Low-Dose CT by statistics-based Nonlinear Filters. Medical Imaging 2005: Image Processing 5747, 2058–2066 (2005)

Vrushali, N.R., Ruikar, S.D.: Image Quality Improvement in Computed Tomography Using Anisotropic Diffusion. In: 2013 IEEE International Conference On Emerging Trends in Computing, Communication and Nanotechnology, pp. 601–605 (2013). https://doi.org/10.1109/ICE-CCN.2013.6528570

Liu, Y., Liang, Z., Ma, J., Lu, H., Wang, K., Zhang, H., Moore, W.: Total Variation Stokes Strategy for Sparse-View X-ray CT Image Reconstruction. In: IEEE Transactions on Medical Imaging, vol. 33, pp. 749–763 (2014). https://doi.org/10.1109/TMI.2013.2295738

Wang, H., Ma, H.: Adaptive Combined Denoising Based Low-Dose X-ray CT Reconstruction. In: 2012 IEEE 11th International Conference on Signal Processing, vol. 3, pp. 1650–1653 (2012). https://doi.org/10.1109/ICoSP.2012.6481897

Ding, Q., Long, Y., Zhang, X., Fessler, J.A.: Modelling mixed Poisson-Gaussian noise in Statistical Image Reconstruction for X-ray CT. In: Arbor, vol. 1001, pp. 399–402 (2016)

Lu, H., Hsiao, I., Li, X., Liang, Z.: Noise Properties of Low-Dose CT Projections and Noise Treatment by Scale Transformations. In: 2001 IEEE Nuclear Science Symposium Conference Record(Cat.No.01CH37310), vol. 3, pp. 1662–1666 (2001). https://doi.org/10.1109/NSSMIC.2001.1008660

Yamada, Y., Jinzaki, M., Tanami, Y., Shiomi, E., Sugiura, H., Abe, T., Kuribayashi, S.: Model-based iterative reconstruction technique for ultra low-dose computed tomography of the lung: a pilot study. Investigative radiology 47(8), 482–489 (2012)

Article  PubMed  Google Scholar 

Beister, M., Kolditz, D., Kalender, W.A.: Iterative Reconstruction Methods in X-ray CT. Physica Medica 28(2), 94–108 (2012) https://doi.org/10.1016/j.ejmp.2012.01.003

Article  PubMed  Google Scholar 

Poletti, P.-A., Becker, M., Becker, C.D., Poletti, A.H., Rutschman, O.T., Zaidi, H., Perneger, T., Platon, A.: Emergency assessment of patients with acute abdominal pain using low-dose CT with iterative reconstruction: a comparative study. European Radiology 27, 3300–3309 (2017) https://doi.org/10.1007/s00330-016-4712-9

Nelson, R.C., Feuerlein, S., Boll, D.T.: New iterative reconstruction techniques for cardiovascular computed tomography: How do they work, and what are the advantages and disadvantages? Journal of Cardiovascualr Computed Tomography 5(5), 286–292 (2011) https://doi.org/10.1016/j.jcct.2011.07.001

Article  Google Scholar 

Willemink, M.J., Jong, P.A., Leiner, T., Heer, L.M., Nievelstein, R.A.J., Budde, R.P.J., Schilham, A.M.R.: Iterative reconstruction techniques for computed tomography Part 1: Technical principles. European Radiology 23, 1623–1631 (2013) https://doi.org/10.1007/s00330-012-2765-y

Kaur, R., Juneja, M., Mandal, A.K.: A comprehensive review of denoising techniques for abdominal CT images. Multimedia Tools Appl 77, 22735–22770 (2018) https://doi.org/10.1007/s11042-017-5500-5

Gu, S., Zhang, L., Zuo, W., Feng, X.: Weighted nuclear norm minimization with application to image denoising. 2014 IEEE Conference on Computer Vision and Pattern Recognition, 2862–2869 (2014) https://doi.org/10.1109/CVPR.2014.366

Song, J., Jeong, J.-H., Park, D.-S., Kim, H.-H., Seo, D.-C., Ye, J.C.: Unsupervised Denoising for Satellite Imagery Using Wavelet Directional CycleGAN. IEEE Transactions on Geoscience and Remote Sensing 59(8), 6823–6839 (2020) https://doi.org/10.1109/TGRS.2020.3025601

Article  Google Scholar 

Dharejo, F.A., Zawish, M., Deeba, F., Zhou, Y., Dev, K., Khowaja, S.A., Muhammad, N., Qureshi, F.: Multimodal-Boost: Multimodal Medical Image Super-Resolution Using Multi-Attention Network With Wavelet Transform. IEEE/ACM Transactions on Computational Biology and Bioinformatics, 1–14 (2020) https://doi.org/10.1109/TCBB.2022.3191387

Zhao, T., Hoffman, J., McNitt-Gray, M., Ruan, D.: Ultra-low-dose CT Image Denoising Using Modified BM3D Scheme tailored to Data Statistics. Medical Physics: The International Journal of Medical Physics Research and Practice 46(1), 190–198 (2018) https://doi.org/10.1002/mp.13252

Article  Google Scholar 

Makinen, Y., Azzari, L., Foi, A.: Collaborative Filtering of Correlated Noise: Exact Transform-Domain Variance for Improved Shrinkage and Patch Matching. IEEE Transactions on Image Processing 29, 8339–8354 (2020) https://doi.org/10.1109/TIP.2020.3014721

Gholizadeh-Ansari, M., Alirezaie, J., Babyn, P.: Deep Learning for Low-Dose CT Denoising using Perceptual Loss and Edge Detection Layer. Journal of Digital Imaging 33(2), 505–514 (2019) https://doi.org/10.1007/s10278-019-00274-4

Article  Google Scholar 

Li, H., Yang, X., Yang, S., Wang, D., Jeon, G.: Transformer with Double Enhancement for Low-Dose CT Denoising. IEEE Journal of Biomedical and Health Informatics (2022) https://doi.org/10.1109/JBHI.2022.3216887

Article  PubMed  PubMed Central  Google Scholar 

Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., Uszkoreit, J., Houlsby, N.: An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale. International Conference on Learning Representations (ICLR) (2021)

Parvaiz, A., Khalid, M.A., Zafar, R., Ameer, H., Ali, M., Fraz, M.M.: Vision transformers in medical computer vision-a contemplative retrospection. Engineering Applications of Artificial Intelligence 122, 106126 (2023)

Article  Google Scholar 

Heo, B., Yun, S., Han, D., Chun, S., Choe, J., Oh, S.J.: Rethinking spatial dimensions of vision transformers. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 11936–11945 (2021)

Unnikrishnan, A., Pavithran, A., Naik, A.G., Jiju, A.P., Sudeep, P.: U-shaped transformer for enhancing low-dose ct images. In: Communication and Intelligent Systems: Proceedings of ICCIS 2021, pp. 1031–1042. Springer, ??? (2022)

Zhang, Z., Yu, L., Liang, X., Zhao, W., Xing, L.: TransCT: Dual-path Transformer for Low Dose Computed Tomography. Medical Image Computing and Computer Assisted Intervention - MICCAI 2021 12906 (2021) https://doi.org/10.1007/978-3-030-87231-1_6

Wang, D., Wu, Z., Yu, H.: TED-Net: Convolution-free T2T vision transformer-based encoder-decoder dilation network for low-dose CT denoising. Machine Learning in Medical Imaging, 416–425 (2021) https://doi.org/10.1007/978-3-030-87589-3_43

Xue, T., Ma, P.: TC-Net: transformer combined with cnn for image denoising. Applied Intelligence (2022) https://doi.org/10.1007/s10489-022-03785-w

Article  Google Scholar 

Luo, J., Zhao, L., Zhu, L., Tao, W.: Multi-scale receptive field fusion network for lightweight image super-resolution. Neurocomputing 493, 314–326 (2022)

Article  Google Scholar 

Paul, S., Chen, P.-Y.: Vision Transformers are Robust Learners. Proceedings of the AAAI Conference on Artificial Intelligence 36(2), 2071–2081 (2022) https://doi.org/10.1609/aaai.v36i2.20103

Article  Google Scholar 

Yuan, L., Chen, Y., Wang, T., Yu, W., Shi, Y., Jiang, Z.-H., Tay, F.E., Feng, J., Yan, S.: Tokens-to-token vit: Training vision transformers from scratch on imagenet. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 558–567 (2021)

Liu, Z., Lin, Y., Cao, Y., Hu, H., Wei, Y., Zhang, Z., Lin, S., Guo, B.: Swin transformer: Hierarchical vision transformer using shifted windows. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 10012–10022 (2021)

Lei, Y., Zhang, J., Shan, H.: Strided Self-Supervised Low-Dose CT Denoising for Lung Nodule Classification. Phenomics 1(6), 257–268 (2021) https://doi.org/10.1007/s43657-021-00025-y

Article  PubMed  PubMed Central  Google Scholar 

Yi, X., Babyn, P.: Sharpness-Aware Low Dose CT Denoising Using Conditional Generative Adversarial Network. Journal of Digital Imaging 31, 655–669 (2018) https://doi.org/10.1007/s10278-018-0056-0

Zhao, F., Liu, M., Gao, Z., Jiang, X., Wang, R., Zhang, L.: Dual-scale similarity-guided cycle generative adversarial network for unsupervised low-dose CT denoising. Computers in Biology and Medicine 161, 107029 (2023)

Article  PubMed  Google Scholar