Skip to main content
Log in

Applications of Generative Adversarial Networks (GANs) in Positron Emission Tomography (PET) imaging: A review

  • Review Article
  • Published:
European Journal of Nuclear Medicine and Molecular Imaging Aims and scope Submit manuscript

Abstract

Purpose

This paper reviews recent applications of Generative Adversarial Networks (GANs) in Positron Emission Tomography (PET) imaging. Recent advances in Deep Learning (DL) and GANs catalysed the research of their applications in medical imaging modalities. As a result, several unique GAN topologies have emerged and been assessed in an experimental environment over the last two years.

Methods

The present work extensively describes GAN architectures and their applications in PET imaging. The identification of relevant publications was performed via approved publication indexing websites and repositories. Web of Science, Scopus, and Google Scholar were the major sources of information.

Results

The research identified a hundred articles that address PET imaging applications such as attenuation correction, de-noising, scatter correction, removal of artefacts, image fusion, high-dose image estimation, super-resolution, segmentation, and cross-modality synthesis. These applications are presented and accompanied by the corresponding research works.

Conclusion

GANs are rapidly employed in PET imaging tasks. However, specific limitations must be eliminated to reach their full potential and gain the medical community's trust in everyday clinical practice.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11

Similar content being viewed by others

References

  1. Yinka-Banjo C, Ugot O-A. A review of generative adversarial networks and its application in cybersecurity. Artif Intell Rev. 2020;53:1721–36.

    Article  Google Scholar 

  2. Alqahtani H, Kavakli-Thorne M, Kumar G. Applications of Generative Adversarial Networks (GANs): An Updated Review. Arch Comput Methods Eng. 2021;28:525–52.

    Article  Google Scholar 

  3. Nensa F, Demircioglu A, Rischpler C. Artificial Intelligence in Nuclear Medicine. J Nucl Med. 2019;60:29S-37S.

    Article  PubMed  Google Scholar 

  4. Seifert R, Weber M, Kocakavuk E, Rischpler C, Kersting D. Artificial Intelligence and Machine Learning in Nuclear Medicine: Future Perspectives. Semin Nucl Med. 2021;51:170–7.

    Article  PubMed  Google Scholar 

  5. Slomka PJ, Miller RJ, Isgum I, Dey D. Application and Translation of Artificial Intelligence to Cardiovascular Imaging in Nuclear Medicine and Noncontrast CT. Semin Nucl Med. 2020;50:357–66.

    Article  PubMed  Google Scholar 

  6. Koshino K, Werner RA, Pomper MG, Bundschuh RA, Toriumi F, Higuchi T, et al. Narrative review of generative adversarial networks in medical and molecular imaging. Ann Transl Med. 2021;9:821–821.

    Article  PubMed  PubMed Central  Google Scholar 

  7. Cheng Z, Wen J, Huang G, Yan J. Applications of artificial intelligence in nuclear medicine image generation. Quant Imaging Med Surg. 2021;11:2792–822.

    Article  PubMed  PubMed Central  Google Scholar 

  8. Vey BL, Gichoya JW, Prater A, Hawkins CM. The Role of Generative Adversarial Networks in Radiation Reduction and Artifact Correction in Medical Imaging. J Am Coll Radiol. 2019;16:1273–8.

    Article  PubMed  Google Scholar 

  9. Arabi H, AkhavanAllaf A, Sanaat A, Shiri I, Zaidi H. The promise of artificial intelligence and deep learning in PET and SPECT imaging. Phys Med. 2021;83:122–37.

    Article  PubMed  Google Scholar 

  10. Manjooran GP, Malakkaran AJ, Joseph A, Babu HM. M S M. A Review on Cross-modality Synthesis from MRI to PET. 2021 2nd Int Conf Secure Cyber Comput Commun ICSCCC [Internet]. Jalandhar, India: IEEE; 2021. p. 126–31. Available from: https://ieeexplore.ieee.org/document/9478170/. Accessed 13 Oct 2021.

  11. Zaharchuk G. Next generation research applications for hybrid PET/MR and PET/CT imaging using deep learning. Eur J Nucl Med Mol Imaging. 2019;46:2700–7.

    Article  PubMed  PubMed Central  Google Scholar 

  12. Zaharchuk G, Davidzon G. Artificial Intelligence for Optimization and Interpretation of PET/CT and PET/MR Images. Semin Nucl Med. 2021;51:134–42.

    Article  PubMed  Google Scholar 

  13. Wang T, Lei Y, Fu Y, Curran WJ, Liu T, Nye JA, et al. Machine learning in quantitative PET: A review of attenuation correction and low-count image reconstruction methods. Phys Med. 2020;76:294–306.

    Article  PubMed  PubMed Central  Google Scholar 

  14. Goodfellow I, Pouget-Abadie J, Mirza M, Xu B, Warde-Farley D, Ozair S, et al. Generative Adversarial Nets. In: Ghahramani Z, Welling M, Cortes C, Lawrence ND, Weinberger KQ, editors. Adv Neural Inf Process Syst 27 [Internet]. Curran Associates, Inc.; 2014. p. 2672–80. Available from: http://papers.nips.cc/paper/5423-generative-adversarial-nets.pdf.

  15. Mirza M, Osindero S. Conditional generative adversarial nets. arXiv preprint arXiv:1411.1784. 2014.

  16. Isola P, Zhu J-Y, Zhou T, Efros AA. Image-to-Image Translation with Conditional Adversarial Networks. ArXiv161107004 Cs [Internet]. 2018 [cited 2021 Nov 22]; Available from: http://arxiv.org/abs/1611.07004.

  17. Zhu J-Y, 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. 2017. pp. 2223–2232. 

  18. Arjovsky M, Chintala S, Bottou L. Wasserstein generative adversarial networks. In: International conference on machine learning. PMLR; 2017. p. 214–23.

  19. Apostolopoulos ID, Papathanasiou ND, Panayiotakis GS. Classification of lung nodule malignancy in computed tomography imaging utilising generative adversarial networks and semi-supervised transfer learning. Biocybern Biomed Eng. 2021;41:1243–57.

    Article  Google Scholar 

  20. Islam J, Zhang Y. GAN-based synthetic brain PET image generation. Brain Inform. 2020;7:3.

    Article  PubMed  PubMed Central  Google Scholar 

  21. Kimura Y, Watanabe A, Yamada T, Watanabe S, Nagaoka T, Nemoto M, et al. AI approach of cycle-consistent generative adversarial networks to synthesise PET images to train computer-aided diagnosis algorithm for dementia. Ann Nucl Med. 2020;34:512–5.

    Article  PubMed  Google Scholar 

  22. Komori S, Kimura Y, Hatano K, Kosugi T, Nishizawa S, Okada H, et al. Image-based deep-learning prediction of future FDG PET patterns in aging and dementia. J Nucl Med. 2019;60:1211.

    Google Scholar 

  23. Kim HW, Lee HE, Lee S, Oh KT, Yun M, Yoo SK. Slice-selective learning for Alzheimer’s disease classification using a generative adversarial network: a feasibility study of external validation. Eur J Nucl Med Mol Imaging. 2020;47:2197–206.

    Article  CAS  PubMed  Google Scholar 

  24. Silva G, Domingues I, Duarte H, Santos JAM. Automatic Generation of Lymphoma Post-Treatment PETs using Conditional-GANs. 2019 Digit Image Comput Tech Appl DICTA [Internet]. Perth, Australia: IEEE; 2019 [cited 2021 Nov 5]. p. 1–6. Available from: https://ieeexplore.ieee.org/document/8945835/.

  25. Baydargil HB, Park J-S, Kang D-Y. Anomaly Analysis of Alzheimer’s Disease in PET Images Using an Unsupervised Adversarial Deep Learning Model. Appl Sci. 2021;11:2187.

    Article  CAS  Google Scholar 

  26. Sajjad M, Ramzan F, Khan MUG, Rehman A, Kolivand M, Fati SM, et al. Deep convolutional generative adversarial network for Alzheimer’s disease classification using positron emission tomography ( PET ) and synthetic data augmentation. Microsc Res Tech. 2021. https://doi.org/10.1002/jemt.23861.

    Article  PubMed  Google Scholar 

  27. Noella RSN, Priyadarshini J. Diagnosis of Dementia Using a Generative Deep Convolution Neural Network. Arab J Sci Eng [Internet]. 2021 [cited 2021 Nov 5]; Available from: https://link.springer.com/https://doi.org/10.1007/s13369-021-05982-0.

  28. Amyar A, Ruan S, Vera P, Decazes P, Modzelewski R. RADIOGAN:Deep Convolutional Conditional Generative Adversarial Network to Generate PET Images. 2020 7th Int Conf Bioinforma Res Appl [Internet]. Berlin Germany: ACM; 2020 [cited 2021 Nov 3]. p. 28–33. Available from: https://dl.acm.org/doi/https://doi.org/10.1145/3440067.3440073.

  29. Kang H, Park J-S, Cho K, Kang D-Y. Visual and Quantitative Evaluation of Amyloid Brain PET Image Synthesis with Generative Adversarial Network. Appl Sci. 2020;10:2628.

    Article  CAS  Google Scholar 

  30. Bi L, Kim J, Kumar A, Feng D, Fulham M. Synthesis of Positron Emission Tomography (PET) Images via Multi-channel Generative Adversarial Networks (GANs). In: Cardoso MJ, Arbel T, Gao F, Kainz B, van Walsum T, Shi K, et al., editors. Mol Imaging Reconstr Anal Mov Body Organs Stroke Imaging Treat [Internet]. Cham: Springer International Publishing; 2017 [cited 2020 Feb 16]. p. 43–51. Available from: http://link.springer.com/https://doi.org/10.1007/978-3-319-67564-0_5.

  31. Cao K, Bi L, Feng D, Kim J. Improving PET-CT Image Segmentation via Deep Multi-modality Data Augmentation. In: Deeba F, Johnson P, Würfl T, Ye JC, editors. Mach Learn Med Image Reconstr [Internet]. Cham: Springer International Publishing; 2020 [cited 2021 Nov 5]. p. 145–52. Available from: http://link.springer.com/https://doi.org/10.1007/978-3-030-61598-7_14.

  32. Arabi H, Zeng G, Zheng G, Zaidi H. Novel adversarial semantic structure deep learning for MRI-guided attenuation correction in brain PET/MRI. Eur J Nucl Med Mol Imaging. 2019;46:2746–59.

    Article  PubMed  Google Scholar 

  33. Armanious K, Hepp T, Küstner T, Dittmann H, Nikolaou K, La Fougère C, et al. Independent attenuation correction of whole body [18F]FDG-PET using a deep learning approach with Generative Adversarial Networks. EJNMMI Res. 2020;10:53.

    Article  PubMed  PubMed Central  Google Scholar 

  34. Hu Z, Li Y, Zou S, Xue H, Sang Z, Liu X, et al. Obtaining PET/CT images from non-attenuation corrected PET images in a single PET system using Wasserstein generative adversarial networks. Phys Med Biol. 2020;65:215010.

    Article  CAS  PubMed  Google Scholar 

  35. Jiang C, Zhang X, Zhang N, Zhang Q, Zhou C, Yuan J, et al. Synthesising PET/MR (T1-weighted) images from non-attenuation-corrected PET images. Phys Med Biol. 2021;66:135006.

    Article  CAS  Google Scholar 

  36. Lei Y, Wang T, Dong X, Higgins K, Liu T, Curran WJ, et al. PET attenuation correction (AC) using non-AC PET-based synthetic CT. In: Bosmans H, Chen G-H, editors. Med Imaging 2020 Phys Med Imaging [Internet]. Houston, United States: SPIE; 2020 [cited 2021 Nov 5]. p. 154. Available from: https://www.spiedigitallibrary.org/conference-proceedings-of-spie/11312/2548468/PET-attenuation-correction-AC-using-non-AC-PET-based-synthetic/https://doi.org/10.1117/12.2548468.full.

  37. Lei Y, Dong X, Wang T, Higgins K, Liu T, Curran WJ, et al. MRI-aided attenuation correction for PET imaging with deep learning. In: Gimi BS, Krol A, editors. Med Imaging 2020 Biomed Appl Mol Struct Funct Imaging [Internet]. Houston, United States: SPIE; 2020 [cited 2021 Nov 5]. p. 73. Available from: https://www.spiedigitallibrary.org/conference-proceedings-of-spie/11317/2549388/MRI-aided-attenuation-correction-for-PET-imaging-with-deep-learning/https://doi.org/10.1117/12.2549388.full.

  38. Anaya E, Levin C. Automatic Generation of MR-based Attenuation Map using Conditional Generative Adversarial Network for Attenuation Correction in PET/MR. 2020 IEEE Nucl Sci Symp Med Imaging Conf NSSMIC [Internet]. Boston, MA, USA: IEEE; 2020 [cited 2021 Nov 5]. p. 1–3. Available from: https://ieeexplore.ieee.org/document/9507903/.

  39. Armanious K, Küstner T, Reimold M, Nikolaou K, La Fougère C, Yang B, Gatidis S. 18 Independent brain F-FDG PET attenuation correction using a deep learning approach with Generative Adversarial Networks. Hell J Nucl Med. 2019;22:179–86.

    PubMed  Google Scholar 

  40. Colmeiro RR, Verrastro C, Minsky D, Grosges T. Whole Body Positron Emission Tomography Attenuation Correction Map Synthesizing using 3D Deep Generative Adversarial Networks [Internet]. In Review; 2020 Jul. Available from: https://www.researchsquare.com/article/rs-46953/v1. Accessed 05 Nov 2021.

  41. Dong X, Lei Y, Wang T, Higgins K, Liu T, Curran WJ, et al. Deep learning-based attenuation correction in the absence of structural information for whole-body positron emission tomography imaging. Phys Med Biol. 2020;65:055011.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  42. Gong K, Yang J, Larson PEZ, Behr SC, Hope TA, Seo Y, et al. MR-Based Attenuation Correction for Brain PET Using 3-D Cycle-Consistent Adversarial Network. IEEE Trans Radiat Plasma Med Sci. 2021;5:185–92.

    Article  PubMed  Google Scholar 

  43. Pozaruk A, Pawar K, Li S, Carey A, Cheng J, Sudarshan VP, et al. Augmented deep learning model for improved quantitative accuracy of MR-based PET attenuation correction in PSMA PET-MRI prostate imaging. Eur J Nucl Med Mol Imaging. 2021;48:9–20.

    Article  PubMed  Google Scholar 

  44. Qian P, Xu K, Wang T, Zheng Q, Yang H, Baydoun A, et al. Estimating CT from MR Abdominal Images Using Novel Generative Adversarial Networks. J Grid Comput. 2020;18:211–26.

    Article  Google Scholar 

  45. Tao L, Li X, Fisher J, Levin CS. Application of Conditional Adversarial Networks for Automatic Generation of MR-based Attenuation Map in PET/MR. 2018 IEEE Nucl Sci Symp Med Imaging Conf Proc NSSMIC [Internet]. Sydney, Australia: IEEE; 2018 [cited 2021 Nov 5]. p. 1–3. Available from: https://ieeexplore.ieee.org/document/8824444/.

  46. Tao L, Fisher J, Anaya E, Li X, Levin CS. Pseudo CT Image Synthesis and Bone Segmentation From MR Images Using Adversarial Networks With Residual Blocks for MR-Based Attenuation Correction of Brain PET Data. IEEE Trans Radiat Plasma Med Sci. 2021;5:193–201.

    Article  Google Scholar 

  47. Li Y, Wu W. A deep learning-based approach for direct PET attenuation correction using Wasserstein generative adversarial network. J Phys Conf Ser. 2021;1848:012006.

    Article  Google Scholar 

  48. Yang X, Lei Y, Dong X, Wang T, Higgins K, Liu T, et al. Attenuation and Scatter Correction for Whole-body PET Using 3D Generative Adversarial Networks. J Nucl Med. 2019;60:174.

    Google Scholar 

  49. Fukui R, Fujii S, Ninomiya H, Fujiwara Y, Ida T. Generation of the Pseudo CT Image Based on the Deep Learning Technique Aimed for the Attenuation Correction of the PET Image. Jpn J Radiol Technol. 2020;76:1152–62.

    Article  Google Scholar 

  50. Schaefferkoetter J, Yan J, Moon S, Chan R, Ortega C, Metser U, et al. Deep learning for whole-body medical image generation. Eur J Nucl Med Mol Imaging. 2021;48:3817–26.

    Article  PubMed  Google Scholar 

  51. Dong X, Wang T, Lei Y, Higgins K, Liu T, Curran WJ, et al. Synthetic CT generation from non-attenuation corrected PET images for whole-body PET imaging. Phys Med Biol. 2019;64:215016.

    Article  PubMed  PubMed Central  Google Scholar 

  52. Geng M, Meng X, Yu J, Zhu L, Jin L, Jiang Z, et al. Content-Noise Complementary Learning for Medical Image Denoising. IEEE Trans Med Imaging. 2021;41(2):407–19.

    Article  Google Scholar 

  53. Sundar LK, Iommi D, Muzik O, Chalampalakis Z, Klebermass EM, Hienert M, et al. Conditional Generative Adversarial Networks Aided Motion Correction of Dynamic 18 F-FDG PET Brain Studies. J Nucl Med. 2021;62:871–80.

    Article  CAS  Google Scholar 

  54. Du Q, Ren X, Wang J, Qiang Y, Yang X, Kazihise NG. Iterative PET image reconstruction using cascaded data consistency generative adversarial network. IET Image Process. 2020;14:3989–99.

    Article  Google Scholar 

  55. Shiyam Sundar L, Iommi D, Spencer B, Wang Q, Cherry S, Beyer T, et al. Data-driven motion compensation using cGAN for total-body [18F]FDG-PET imaging. J Nucl Med. 2021;62:35.

    Article  CAS  Google Scholar 

  56. Zhou B, Tsai Y-J, Chen X, Duncan JS, Liu C. MDPET: A Unified Motion Correction and Denoising Adversarial Network for Low-Dose Gated PET. IEEE Trans Med Imaging. 2021;40:3154–64.

    Article  PubMed  PubMed Central  Google Scholar 

  57. Wang Y, Zhou L, Yu B, Wang L, Zu C, Lalush DS, et al. 3D Auto-Context-Based Locality Adaptive Multi-Modality GANs for PET Synthesis. IEEE Trans Med Imaging. 2019;38:1328–39.

    Article  PubMed  Google Scholar 

  58. Wang Y, Yu B, Wang L, Zu C, Lalush DS, Lin W, et al. 3D conditional generative adversarial networks for high-quality PET image estimation at low dose. Neuroimage. 2018;174:550–62.

    Article  PubMed  Google Scholar 

  59. Ouyang J, Chen KT, Gong E, Pauly J, Zaharchuk G. Ultra-low-dose PET reconstruction using generative adversarial network with feature matching and task-specific perceptual loss. Med Phys. 2019;46:3555–64.

    Article  CAS  PubMed  Google Scholar 

  60. Chen KT, Gong E, de Carvalho Macruz FB, Xu J, Boumis A, Khalighi M, et al. Ultra–Low-Dose 18 F-Florbetaben Amyloid PET Imaging Using Deep Learning with Multi-Contrast MRI Inputs. Radiology. 2019;290:649–56.

    Article  PubMed  Google Scholar 

  61. Hu Z, Xue H, Zhang Q, Gao J, Zhang N, Zou S, et al. DPIR-Net: Direct PET Image Reconstruction Based on the Wasserstein Generative Adversarial Network. IEEE Trans Radiat Plasma Med Sci. 2021;5:35–43.

    Article  Google Scholar 

  62. Sanaat A, Shiri I, Arabi H, Mainta I, Nkoulou R, Zaidi H. Whole-body PET Image Synthesis from Low-Dose Images Using Cycle-consistent Generative Adversarial Networks. 2020 IEEE Nucl Sci Symp Med Imaging Conf NSSMIC [Internet]. Boston, MA, USA: IEEE; 2020 [cited 2021 Nov 5]. p. 1–3. Available from: https://ieeexplore.ieee.org/document/9507947/.

  63. Xue H, Zhang Q, Zou S, Zhang W, Zhou C, Tie C, et al. LCPR-Net: low-count PET image reconstruction using the domain transform and cycle-consistent generative adversarial networks. Quant Imaging Med Surg. 2021;11:749–62.

    Article  PubMed  PubMed Central  Google Scholar 

  64. Du Q, Qiang Y, Yang W, Wang Y, Ma Y, Zia MB. DRGAN: a deep residual generative adversarial network for PET image reconstruction. IET Image Process. 2020;14:1690–700.

    Article  Google Scholar 

  65. Lei Y, Wang T, Dong X, Higgins K, Liu T, Curran WJ, et al. Low dose PET imaging with CT-aided cycle-consistent adversarial networks. In: Bosmans H, Chen G-H, editors. Med Imaging 2020 Phys Med Imaging [Internet]. Houston, United States: SPIE; 2020 [cited 2021 Nov 5]. p. 152. Available from: https://www.spiedigitallibrary.org/conference-proceedings-of-spie/11312/2549386/Low-dose-PET-imaging-with-CT-aided-cycle-consistent-adversarial/https://doi.org/10.1117/12.2549386.full.

  66. Lei Y, Dong X, Wang T, Higgins K, Liu T, Curran WJ, et al. Estimating standard-dose PET from low-dose PET with deep learning. In: Landman BA, Išgum I, editors. Med Imaging 2020 Image Process [Internet]. Houston, United States: SPIE; 2020 [cited 2021 Nov 5]. p. 73. Available from: https://www.spiedigitallibrary.org/conference-proceedings-of-spie/11313/2548461/Estimating-standard-dose-PET-from-low-dose-PET-with-deep/https://doi.org/10.1117/12.2548461.full.

  67. Zhao K, Zhou L, Gao S, Wang X, Wang Y, Zhao X, et al. Study of low-dose PET image recovery using supervised learning with CycleGAN. Hatt M, editor. PLOS ONE. 2020; 15:e0238455.

  68. Gong Y, Shan H, Teng Y, Tu N, Li M, Liang G, et al. Parameter-Transferred Wasserstein Generative Adversarial Network (PT-WGAN) for Low-Dose PET Image Denoising. IEEE Trans Radiat Plasma Med Sci. 2021;5:213–23.

    Article  PubMed  Google Scholar 

  69. Lu W, Onofrey JA, Lu Y, Shi L, Ma T, Liu Y, et al. An investigation of quantitative accuracy for deep learning based de-noising in oncological PET. Phys Med Biol. 2019;64:165019.

    Article  CAS  PubMed  Google Scholar 

  70. Wang Y, Zhou L, Wang L, Yu B, Zu C, Lalush DS, et al. Locality Adaptive Multi-modality GANs for High-Quality PET Image Synthesis. In: Frangi AF, Schnabel JA, Davatzikos C, Alberola-López C, Fichtinger G, editors. Med Image Comput Comput Assist Interv – MICCAI 2018 [Internet]. Cham: Springer International Publishing; 2018 [cited 2021 Nov 5]. p. 329–37. Available from: http://link.springer.com/https://doi.org/10.1007/978-3-030-00928-1_38.

  71. Xie Z, Baikejiang R, Li T, Zhang X, Gong K, Zhang M, et al. Generative adversarial network based regularised image reconstruction for PET. Phys Med Biol. 2020;65:125016.

    Article  PubMed  PubMed Central  Google Scholar 

  72. Zhou L, Schaefferkoetter JD, Tham IWK, Huang G, Yan J. Supervised learning with cyclegan for low-dose FDG PET image de-noising. Med Image Anal. 2020;65:101770.

    Article  PubMed  Google Scholar 

  73. Sanaat A, Shiri I, Arabi H, Mainta I, Nkoulou R, Zaidi H. Deep learning-assisted ultra-fast/low-dose whole-body PET/CT imaging. Eur J Nucl Med Mol Imaging. 2021;48:2405–15.

    Article  PubMed  PubMed Central  Google Scholar 

  74. Jeong YJ, Park HS, Jeong JE, Yoon HJ, Jeon K, Cho K, et al. Restoration of amyloid PET images obtained with short-time data using a generative adversarial networks framework. Sci Rep. 2021;11:4825.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  75. Sanaat A, Mirsadeghi E, Razeghi B, Ginovart N, Zaidi H. Fast Dynamic Brain PET Imaging Using a Generative Adversarial Network. 2020 IEEE Nucl Sci Symp Med Imaging Conf NSSMIC [Internet]. Boston, MA, USA: IEEE; 2020 [cited 2021 Nov 5]. p. 1–3. Available from: https://ieeexplore.ieee.org/document/9507894/.

  76. Kim J-W, Kim J-Y, Lim H, Kim J. Comparative Evaluation of 18 F-FDG Brain PET/CT AI Images Obtained Using Generative Adversarial Network. Korean J Nucl Med Technol. The Korean Society of Nuclear Medicine Technology. 2020; 24:15–9.

  77. Xue H, Teng Y, Tie C, Wan Q, Wu J, Li M, et al. A 3D attention residual encoder–decoder least-square GAN for low-count PET de-noising. Nucl Instrum Methods Phys Res Sect Accel Spectrometers Detect Assoc Equip. 2020;983:164638.

    Article  CAS  Google Scholar 

  78. Lei Y, Dong X, Wang T, Higgins K, Liu T, Curran WJ, et al. Whole-body PET estimation from low count statistics using cycle-consistent generative adversarial networks. Phys Med Biol. 2019;64:215017.

    Article  PubMed  PubMed Central  Google Scholar 

  79. Liu Z, Chen H, Liu H. Deep Learning Based Framework for Direct Reconstruction of PET Images. In: Shen D, Liu T, Peters TM, Staib LH, Essert C, Zhou S, et al., editors. Med Image Comput Comput Assist Interv – MICCAI 2019 [Internet]. Cham: Springer International Publishing; 2019 [cited 2021 Nov 5]. p. 48–56. Available from: http://link.springer.com/https://doi.org/10.1007/978-3-030-32248-9_6.

  80. Song T-A, Chowdhury SR, Yang F, Dutta J. PET image super-resolution using generative adversarial networks. Neural Netw. 2020;125:83–91.

    Article  PubMed  PubMed Central  Google Scholar 

  81. Song T-A, Roy Chowdhury S, Yang F, Dutta J. Self Supervised Super-Resolution PET Using A Generative Adversarial Network. 2019 IEEE Nucl Sci Symp Med Imaging Conf NSSMIC [Internet]. Manchester, United Kingdom: IEEE; 2019 [cited 2021 Nov 5]. p. 1–3. Available from: https://ieeexplore.ieee.org/document/9059947/.

  82. Oh KT, Kim D, Ye BS, Lee S, Yun M, Yoo SK. Segmentation of white matter hyperintensities on 18F-FDG PET/CT images with a generative adversarial network. Eur J Nucl Med Mol Imaging. 2021;48:3422–31.

    Article  PubMed  Google Scholar 

  83. Oh KT, Lee S, Lee H, Yun M, Yoo SK. Semantic Segmentation of White Matter in FDG-PET Using Generative Adversarial Network. J Digit Imaging. 2020;33:816–25.

    Article  PubMed  PubMed Central  Google Scholar 

  84. Wu X, Bi L, Fulham M, Kim J. Unsupervised Positron Emission Tomography Tumor Segmentation via GAN based Adversarial Auto-Encoder. 2020 16th Int Conf Control Autom Robot Vis ICARCV [Internet]. Shenzhen, China: IEEE; 2020 [cited 2021 Nov 5]. p. 448–53. Available from: https://ieeexplore.ieee.org/document/9305364/.

  85. Yousefirizi F, Rahmim A. GAN-Based Bi-Modal Segmentation Using Mumford-Shah Loss: Application to Head and Neck Tumors in PET-CT Images. In: Andrearczyk V, Oreiller V, Depeursinge A, editors. Head Neck Tumor Segmentation [Internet]. Cham: Springer International Publishing; 2021 [cited 2021 Nov 5]. p. 99–108. Available from: http://link.springer.com/https://doi.org/10.1007/978-3-030-67194-5_11.

  86. Ma J, Xu H, Jiang J, Mei X, Zhang X-P. DDcGAN: A Dual-Discriminator Conditional Generative Adversarial Network for Multi-Resolution Image Fusion. IEEE Trans Image Process. 2020;29:4980–95.

    Article  Google Scholar 

  87. Kang J, Lu W, Zhang W. Fusion of Brain PET and MRI Images Using Tissue-Aware Conditional Generative Adversarial Network With Joint Loss. IEEE Access. 2020;8:6368–78.

    Article  Google Scholar 

  88. Huang J, Le Z, Ma Y, Fan F, Zhang H, Yang L. MGMDcGAN: Medical Image Fusion Using Multi-Generator Multi-Discriminator Conditional Generative Adversarial Network. IEEE Access. 2020;8:55145–57.

    Article  Google Scholar 

  89. Yang Z, Chen Y, Le Z, Fan F, Pan E. Multi-Source Medical Image Fusion Based on Wasserstein Generative Adversarial Networks. IEEE Access. 2019;7:175947–58.

    Article  Google Scholar 

  90. Liu H, Nai Y-H, Saridin F, Tanaka T, O’Doherty J, Hilal S, et al. Improved amyloid burden quantification with non-specific estimates using deep learning. Eur J Nucl Med Mol Imaging. 2021;48:1842–53.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  91. Kang SK, Choi H, Lee JS. Translating amyloid PET of different radiotracers by a deep generative model for interchangeability. NeuroImage. 2021;232:117890.

    Article  CAS  PubMed  Google Scholar 

  92. Kang SK, Seo S, Shin SA, Byun MS, Lee DY, Kim YK, et al. Adaptive template generation for amyloid PET using a deep learning approach. Hum Brain Mapp. 2018;39:3769–78.

    Article  PubMed  PubMed Central  Google Scholar 

  93. Liu H, Nai Y-H, Chen C, Reilhac A. Deep Learning-Based Estimation of Non-Specific Uptake in Amyloid-PET Images from Structural MRI for Improved Quantification of Amyloid Load in Alzheimer’s Disease. 2020 IEEE 33rd Int Symp Comput-Based Med Syst CBMS [Internet]. Rochester, MN, USA: IEEE; 2020 [cited 2021 Nov 5]. p. 573–8. Available from: https://ieeexplore.ieee.org/document/9182970/.

  94. Ma S, Hu Z, Ye K, Zhang X, Wang Y, Peng H. Feasibility study of patient-specific dose verification in proton therapy utilising positron emission tomography (PET) and generative adversarial network (GAN). Med Phys. 2020;47:5194–208.

    Article  CAS  PubMed  Google Scholar 

  95. Hognon C, Tixier F, Colin T, Gallinato O, Visvikis D, Jaouen V. Influence of gradient difference loss on MR to PET brain image synthesis using GANs. J Nucl Med. 2020;61:1431.

    Google Scholar 

  96. Hu S, Lei B, Wang S, Wang Y, Feng Z, Shen Y. Bidirectional Mapping Generative Adversarial Networks for Brain MR to PET Synthesis. IEEE Trans Med Imaging. 2022;41(1):145–57.

    Article  PubMed  Google Scholar 

  97. Wei W, Poirion E, Bodini B, Durrleman S, Ayache N, Stankoff B, et al. Predicting PET-derived demyelination from multi-modal MRI using sketcher-refiner adversarial training for multiple sclerosis. Med Image Anal. 2019;58:101546.

    Article  PubMed  Google Scholar 

  98. Pan Y, Liu M, Lian C, Xia Y, Shen D. Spatially-Constrained Fisher Representation for Brain Disease Identification With Incomplete Multi-Modal Neuroimages. IEEE Trans Med Imaging. 2020;39:2965–75.

    Article  PubMed  PubMed Central  Google Scholar 

  99. Shin H-C, Ihsani A, Mandava S, Sreenivas ST, Forster C, Cha J et al. GANBERT: Generative Adversarial Networks with Bidirectional Encoder Representations from Transformers for MRI to PET synthesis. ArXiv200804393 Cs Eess [Internet]. 2020 [cited 2021 Nov 5]; Available from: http://arxiv.org/abs/2008.04393.

  100. Hu S, Yuan J, Wang S. Cross-modality Synthesis from MRI to PET Using Adversarial U-Net with Different Normalisation. 2019 Int Conf Med Imaging Phys Eng ICMIPE [Internet]. Shenzhen, China: IEEE; 2019 [cited 2021 Nov 5]. p. 1–5. Available from: https://ieeexplore.ieee.org/document/9098219/.

  101. Hu S, Shen Y, Wang S, Lei B. Brain MR to PET Synthesis via Bidirectional Generative Adversarial Network. In: Martel AL, Abolmaesumi P, Stoyanov D, Mateus D, Zuluaga MA, Zhou SK, et al., editors. Med Image Comput Comput Assist Interv – MICCAI 2020 [Internet]. Cham: Springer International Publishing; 2020 [cited 2021 Nov 5]. p. 698–707. Available from: https://link.springer.com/https://doi.org/10.1007/978-3-030-59713-9_67.

  102. Jung MM, van den Berg B, Postma E, Huijbers W. Inferring PET from MRI with pix2pix. Inferring PET MRI Pix2pix [Internet]. 2018. Available from: https://bnaic2018.nl/.

  103. Lin W, Lin W, Chen G, Zhang H, Gao Q, Huang Y, et al. Bidirectional Mapping of Brain MRI and PET With 3D Reversible GAN for the Diagnosis of Alzheimer’s Disease. Front Neurosci. 2021;15:646013.

    Article  PubMed  PubMed Central  Google Scholar 

  104. Lan H, the Alzheimer Disease Neuroimaging Initiative, Toga AW, Sepehrband F. Three-dimensional self-attention conditional GAN with spectral normalisation for multi-modal neuroimaging synthesis. Magn Reson Med. 2021;86:1718–33.

    Article  CAS  PubMed  Google Scholar 

  105. Yaakub SN, McGinnity CJ, Clough JR, Kerfoot E, Girard N, Guedj E et al. Pseudo-normal PET Synthesis with Generative Adversarial Networks for Localising Hypometabolism in Epilepsies. In: Burgos N, Gooya A, Svoboda D, editors. Simul Synth Med Imaging [Internet]. Cham: Springer International Publishing; 2019 [cited 2021 Nov 5]. p. 42–51. Available from: http://link.springer.com/https://doi.org/10.1007/978-3-030-32778-1_5.

  106. Pan Y, Liu M, Lian C, Zhou T, Xia Y, Shen D. Synthesizing Missing PET from MRI with Cycle-consistent Generative Adversarial Networks for Alzheimer’s Disease Diagnosis. In: Frangi AF, Schnabel JA, Davatzikos C, Alberola-López C, Fichtinger G, editors. Med Image Comput Comput Assist Interv – MICCAI 2018 [Internet]. Cham: Springer International Publishing; 2018 [cited 2021 Nov 5]. p. 455–63. Available from: http://link.springer.com/https://doi.org/10.1007/978-3-030-00931-1_52.

  107. Sikka A, Skand, Virk JS, Bathula DR. MRI to PET Cross-Modality Translation using Globally and Locally Aware GAN (GLA-GAN) for Multi-Modal Diagnosis of Alzheimer’s Disease. ArXiv210802160 Cs Eess [Internet]. 2021 [cited 2021 Nov 5]; Available from: http://arxiv.org/abs/2108.02160.

  108. Yan Y, Lee H, Somer E, Grau V. Generation of Amyloid PET Images via Conditional Adversarial Training for Predicting Progression to Alzheimer’s Disease. In: Rekik I, Unal G, Adeli E, Park SH, editors. Predict Intell Med [Internet]. Cham: Springer International Publishing; 2018 [cited 2021 Nov 5]. p. 26–33. Available from: http://link.springer.com/https://doi.org/10.1007/978-3-030-00320-3_4.

  109. Gao X, Shi F, Shen D, Liu M. Task-induced Pyramid and Attention GAN for Multi-modal Brain Image Imputation and Classification in Alzheimers disease. IEEE J Biomed Health Inform. 2021;26(1):36–43.

    Article  Google Scholar 

  110. Choi H, Lee DS. Generation of Structural MR Images from Amyloid PET: Application to MR-Less Quantification. J Nucl Med. 2018;59:1111–7.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  111. Armanious K, Jiang C, Fischer M, Küstner T, Hepp T, Nikolaou K, et al. MedGAN: Medical image translation using GANs. Comput Med Imaging Graph. 2020;79:101684.

    Article  PubMed  Google Scholar 

  112. Santini G, Fourcade C, Moreau N, Rousseau C, Ferrer L, Lacombe M et al. Unpaired PET/CT image synthesis of liver region using CycleGAN. In: Brieva J, Lepore N, Romero Castro E, Linguraru MG, editors. 16th Int Symp Med Inf Process Anal [Internet]. Lima, Peru: SPIE; 2020 [cited 2021 Nov 5]. p. 6. Available from: https://www.spiedigitallibrary.org/conference-proceedings-of-spie/11583/2576095/Unpaired-PETCT-image-synthesis-of-liver-region-using-CycleGAN/https://doi.org/10.1117/12.2576095.full.

  113. Sharma A, Jindal N. Cross-Modality Breast Image Translation with Improved Resolution Using Generative Adversarial Networks. Wirel Pers Commun. 2021;119:2877–91.

    Article  Google Scholar 

  114. Ben-Cohen A, Klang E, Raskin SP, Soffer S, Ben-Haim S, Konen E, et al. Cross-modality synthesis from CT to PET using FCN and GAN networks for improved automated lesion detection. Eng Appl Artif Intell. 2019;78:186–94.

    Article  Google Scholar 

  115. Leydon P, O’Connell M, Greene D, Curran K. Synthetic positron emission tomography using conditional-generative adversarial networks for healthy bone marrow baseline image generation. IMVIP 2019: Irish Machine Vision & Image Processing, Technological University Dublin: Dublin, Ireland; 2019. https://doi.org/10.21427/xmkn-d265.

  116. Liebgott A, Hindere D, Armanious K, Bartler A, Nikolaou K, Gatidis S et al. Prediction of FDG uptake in Lung Tumors from CT Images Using Generative Adversarial Networks. 2019. pp. 1–5. https://doi.org/10.23919/EUSIPCO.2019.8902935.

  117. Ben-Cohen A, Klang E, Raskin SP, Amitai MM, Greenspan H. Virtual PET Images from CT Data Using Deep Convolutional Networks: Initial Results. In: Tsaftaris SA, Gooya A, Frangi AF, Prince JL, editors. Simul Synth Med Imaging [Internet]. Cham: Springer International Publishing; 2017 [cited 2021 Nov 5]. p. 49–57. Available from: http://link.springer.com/https://doi.org/10.1007/978-3-319-68127-6_6.

  118. Huang B, Chen Z, Law M, Feng S, Li Q, Huang B. Progressive Generative Adversarial Networks: Deep Learning in Head and Neck Cancer CT Images to Synthesized PET Images Generation for Hybrid PET/CT Application. DEStech Trans Comput Sci Eng [Internet]. 2018 [cited 2021 Nov 5]; Available from: http://dpi-proceedings.com/index.php/dtcse/article/view/24701.

  119. Plachouris D, Tzolas I, Gatos I, Papadimitroulas P, Spyridonidis T, Apostolopoulos D, et al. A deep-learning-based prediction model for the biodistribution of 90 Y microspheres in liver radioembolisation. Med Phys. 2021;48:7427–38.

    Article  CAS  PubMed  Google Scholar 

  120. Chen JS, Coyner AS, Chan RVP, Hartnett ME, Moshfeghi DM, Owen LA, et al. Deepfakes in Ophthalmology. Ophthalmol Sci. 2021;1:100079.

    Article  Google Scholar 

  121. Chu LC, Anandkumar A, Shin HC, Fishman EK. The Potential Dangers of Artificial Intelligence for Radiology and Radiologists. J Am Coll Radiol. 2020;17:1309–11.

    Article  PubMed  PubMed Central  Google Scholar 

  122. Apostolopoulos ID, Pintelas EG, Livieris IE, Apostolopoulos DJ, Papathanasiou ND, Pintelas PE, et al. Automatic classification of solitary pulmonary nodules in PET/CT imaging employing transfer learning techniques. Med Biol Eng Comput. 2021;59:1299–310.

    Article  PubMed  Google Scholar 

  123. Apostolopoulos ID, Apostolopoulos DI, Spyridonidis TI, Papathanasiou ND, Panayiotakis GS. Multi-input deep learning approach for Cardiovascular Disease diagnosis using Myocardial Perfusion Imaging and clinical data. Phys Med. 2021;84:168–77.

    Article  PubMed  Google Scholar 

Download references

Funding

The authors declare that no funds, grants, or other support was received during the preparation of this manuscript.

Author information

Authors and Affiliations

Authors

Contributions

All authors contributed to the study conception and design. Material preparation, data collection, and analysis were performed by Ioannis D. Apostolopoulos, Nikolaos D. Papathanasiou, and Dimitris J. Apostolopoulos. The first draft of the manuscript was written by Ioannis D. Apostolopoulos and George S. Panayiotakis, and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.

Corresponding author

Correspondence to Ioannis D. Apostolopoulos.

Ethics declarations

Competing interests

The authors have no relevant financial or non-financial interests to disclose.

Additional information

Publisher's note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

This article is part of the Topical Collection on Advanced Image Analyses (Radiomics and Artificial Intelligence).

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Apostolopoulos, I.D., Papathanasiou, N.D., Apostolopoulos, D.J. et al. Applications of Generative Adversarial Networks (GANs) in Positron Emission Tomography (PET) imaging: A review. Eur J Nucl Med Mol Imaging 49, 3717–3739 (2022). https://doi.org/10.1007/s00259-022-05805-w

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00259-022-05805-w

Keywords

Navigation