Academic Journal of Computing & Information Science, 2021, 4(1); doi: 10.25236/AJCIS.2021.040108.
Mingdong Liu1,*, Yunye Feng2, Chang Liu1, Mingyu Gao1, Chenglin Yan1
1College of Medical Biological Information Engineering, Northeastern University, Shenyang 110167, China
2College of Software, Northeastern University, Shenyang 110167, China
*Corresponding Author
PET-CT is a high-end medical imaging technology that can detect tumors in the early stage, and has been widely used in clinical practice.Obtaining high-quality images is a prerequisite for doctors to make a correct diagnosis.For PET itself, how to continuously improve its resolution is a challenging problem.In this study, Generative Adversarial Networks (GAN) combined with Wasserstein distance and gradient penalty technology was used to achieve PET image super-resolution reconstruction.The experimental results show that the structural similarity of the final image reaches more than 60%.
PET, super resolution, deep learning, GAN
Mingdong Liu, Yunye Feng, Chang Liu, Mingyu Gao, Chenglin Yan. Super resolution reconstruction of PET images based on deep learning. Academic Journal of Computing & Information Science (2021), Vol. 4, Issue 1: 48-52. https://doi.org/10.25236/AJCIS.2021.040108.
[1] C. Ledig, L. Theis, F. Huszar, J. Caballero, A. Cunningham, A.Acosta, A. Aitken, A. Tejani, J. Totz, Z. Wang, W. Shi(2017), Photo-realistic single image super-resolution using a generative adversarial network, in: CVPR, pp. 4681–4690.
[2] I. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, Y. Bengio(2014), Generative adversarial nets, in: NIPS, pp. 2672–2680.
[3] S.-J. Park, H. Son, S. Cho, K.-S. Hong, S. Lee, Srfeat(2018): Single image super-resolution with feature discrimination, in: ECCV,, pp. 439-455.
[4] John A Kennedy; Ora Israel; Alex Frenkel; Rachel Bar-Shalom; Haim Azhari(2007)Improved image fusion in PET/CT using hybrid image reconstruction and super-resolution. International journal of biomedical imaging.1687-4188;1687-4188.
[5] II Jun Ahn 1 ; Ji Hye Kim 1 ; Yongjin Chang 1 ; Woo Hyun Nam 1 ; Jong Beom Ra 1 (2007)Super-Resolution Reconstruction of 3D PET Images Using Two Respiratory-Phase Low-Dose CT Images. IEEE TRANSACTIONS ON RADIATION AND PLASMA MEDICAL SCIENCES Vol.1 No.1 49-55
[6] Hu, Zhanli 1; Wang, Ying 1,2 ; Zhang, Xuezhu 3 ; Zhang, Mengxi 3 ; Yang, Yongfeng 1 ; Liu, Xin 1 ; Zheng, Hairong 1 ; Liang, Dong 1 ([email protected]).(2019) Super-resolution of PET image based on dictionary learning and random forests. Nuclear Instruments & Methods in Physics Research Section A.Vol.927 320-329.
[7] Malczewski, Krzysztof 1(2020). Algorithms.2020 Vol.13 No.5 129.
[8] X. Wang, K. Yu, S. Wu, J. Gu, Y. Liu, C. Dong, C. C. Loy, Y. Qiao, X. Tang(2018), EsrGAN: Enhanced super-resolution generative adversarial net-works, in: ECCV Workshop
[9] I.Gulrajani,et al. “Improved training of Wasserstein GANs.” Advances in neural information processing systems, 2017, pp.5767-5777.
[10] Q. Lyu, H. Shan and G. Wang, "MRI Super-Resolution With Ensemble Learning and Complementary Priors," in IEEE Transactions on Computational Imaging, vol. 6, pp. 615-624, 2020, doi: 10.1109/TCI.2020.2964201.