CT scan Reconstruction Using Generative Adversarial Network

References

[1] hen H, Zhang Y, Zhang W, Liao P, Li K, Zhou J, Wang G. Low-dose CT via convolutional neural network. Biomed Opt Express. 2017 Jan 9;8(2):679-694. doi: 10.1364/BOE.8.000679. PMID: 28270976; PMCID: PMC5330597.

[2] Nie D, Trullo R, Lian J, Petitjean C, Ruan S, Wang Q, Shen D. Med-ical Image Synthesis with Context-Aware Generative Adversarial Net-works. Med Image Comput Comput Assist Interv. 2017 Sep;10435:417-425. doi: 10.1007/978-3-319-66179-7-48. Epub 2017 Sep 4. PMID:30009283; PMCID: PMC6044459.

[3] Wolterink JM, Leiner T, Viergever MA, Isgum I. Generative Adversarial Networks for Noise Reduction in Low-Dose CT. IEEE Trans Med Imaging. 2017 Dec;36(12):2536-2545. doi: 10.1109/TMI.2017.2708987. Epub 2017 May 26. PMID: 28574346.

[4] Goodfellow, I., et al., 2014. Generative adversarial nets. Advances in Neural Information Processing Systems, pp. 2672–2680.

[5] Yang et al., ”Low-Dose CT Image Denoising Using a Generative Adversarial Network With Wasserstein Distance and Perceptual Loss,” in IEEE Transactions on Medical Imaging, vol. 37, no. 6, pp. 1348-1357, June 2018, doi: 10.1109/TMI.2018.2827462.

[6] ohnson J, Alahi A, Fei-Fei L. Perceptual losses for real-time style trans-fer and super-resolution. 2016 [Online]. Available: arXiv:1603.08155.

[7] Ledig C, Theis L, Huszar F, Caballero J, Cunningham A, Acosta A, Aitken A, Tejani A, Totz J, Wang Z, Shi W. Photo-realistic single image super-resolution using a generative adversarial network. 2016 [Online]. Available: arXiv:1609.04802.

[8] Armanious, Karim and Yang, Chenming and Fischer, Marc and Kustner,¨ Thomas and Nikolaou, Konstantin and Gatidis, Sergios and Yang, Bin. (2018). MedGAN: Medical Image Translation using GANs.

[9] CT Super-resolution GAN Constrained by the Identical, Residual, and Cycle Learning Ensemble (GAN-CIRCLE) Chenyu You, Guang Li, Yi Zhang*, Senior Member, IEEE, Xiaoliu Zhang, Hongming Shan, Mengzhou Li, Shenghong Ju, Zhen Zhao, Zhuiyang Zhang, Wenxiang Cong, Michael W. Vannier, Member, IEEE, Punam K. Saha, Senior Member, IEEE, Eric A. Hoffman, Member, IEEE, and Ge Wang*, Fellow, IEEE

@article{1703911,
      author = {Rajlaxmi Patil, Tanvi Khare, Sanika Inamdar, Madhuri Mane},
      title = {CT scan Reconstruction Using Generative Adversarial Network},
      journal = {Iconic Research And Engineering Journals},
      year = {2022},
      volume = {6},
      number = {6},
      pages = {31-36},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1703911.pdf},
      abstract = {Computer Vision and Image Enhancement through Artificial Intelligence is being used for the improvement in the resolution of the images obtained from variety of sources such as surveillance camera, PAUS , CT scans, radiology results. Implemented method uses a machine learning algorithm, Gen-erative Adversarial Networks (GAN) for achieving the goal of enhancement and reconstruction of images. Super resolution of images allows us to obtain images with better resolution and less noise . Existing models tend to add artifacts to reconstructed im-ages or remove important details from enhanced scans. Cleaner images are obtained at cost of some information loss. Noise in scans should be removed while keeping important information intact and providing a better resolution.In this paper we explore research that has been done for reconstruction of CT scans using various methods.},
      keywords = {Generative Adversarial Network, Image Enhancement, Computer Vision, noise reduction, Computed tomography, CNN},
      month = {December},
  }