Movatterモバイル変換

[0]ホーム
URL:

Skip to main content

Advertisement

Springer Nature Link
Log in

PET-Diffusion: Unsupervised PET Enhancement Based on the Latent Diffusion Model

  • Conference paper
  • First Online:

Abstract

Positron emission tomography (PET) is an advanced nuclear imaging technique with an irreplaceable role in neurology and oncology studies, but its accessibility is often limited by the radiation hazards inherent in imaging. To address this dilemma, PET enhancement methods have been developed by improving the quality of low-dose PET (LPET) images to standard-dose PET (SPET) images. However, previous PET enhancement methods rely heavily on the paired LPET and SPET data which are rare in clinic. Thus, in this paper, we propose an unsupervised PET enhancement (uPETe) framework based on the latent diffusion model, which can be trained only on SPET data. Specifically, our SPET-only uPETe consists of an encoder to compress the input SPET/LPET images into latent representations, a latent diffusion model to learn/estimate the distribution of SPET latent representations, and a decoder to recover the latent representations into SPET images. Moreover, from the theory of actual PET imaging, we improve the latent diffusion model of uPETe by 1) adoptingPET image compression for reducing the computational cost of diffusion model, 2) usingPoisson diffusion to replace Gaussian diffusion for making the perturbed samples closer to the actual noisy PET, and 3) designingCT-guided cross-attention for incorporating additional CT images into the inverse process to aid the recovery of structural details in PET. With extensive experimental validation, our uPETe can achieve superior performance over state-of-the-art methods, and shows stronger generalizability to the dose changes of PET imaging. The code of our implementation is available athttps://github.com/jiang-cw/PET-diffusion.

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

Access this chapter

Subscribe and save

Springer+ Basic
¥17,985 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
JPY 3498
Price includes VAT (Japan)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
JPY 10295
Price includes VAT (Japan)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
JPY 12869
Price includes VAT (Japan)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide -see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Similar content being viewed by others

References

  1. Buades, A., Coll, B., Morel, J.: A non-local algorithm for image denoising. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2, pp. 60–65 (2005)

    Google Scholar 

  2. Chen, S., Ma, K., Zheng, Y.: Med3D: transfer learning for 3D medical image analysis. arXiv preprintarXiv:1904.00625 (2019)

  3. Cui, J., et al.: PET image denoising using unsupervised deep learning. Eur. J. Nucl. Med. Mol. Imaging46(13), 2780–2789 (2019)

    Article  Google Scholar 

  4. Dabov, K., Foi, A., Katkovnik, V., Egiazarian, K.: Image denoising with block-matching and 3D filtering. Image Process. Algorithms Syst. Neural Netw. Mach. Learn.6064, 354–365 (2006)

    Google Scholar 

  5. Dosovitskiy, A., Brox, T.: Generating images with perceptual similarity metrics based on deep networks. In: Advances in Neural Information Processing Systems, vol. 29 (2016)

    Google Scholar 

  6. Ho, J., Jain, A., Abbeel, P.: Denoising diffusion probabilistic models. In: Advances in Neural Information Processing Systems, vol. 33, pp. 6840–6851 (2020)

    Google Scholar 

  7. Hofheinz, F., et al.: Suitability of bilateral filtering for edge-preserving noise reduction in PET. EJNMMI Res.1(1), 1–9 (2011)

    Article  Google Scholar 

  8. Jiang, C., Pan, Y., Cui, Z., Nie, D., Shen, D.: Semi-supervised standard-dose PET image generation via region-adaptive normalization and structural consistency constraint. IEEE Trans. Med. Imaging (2023)

    Google Scholar 

  9. Jiang, C., Pan, Y., Cui, Z., Shen, D.: Reconstruction of standard-dose PET from low-dose PET via dual-frequency supervision and global aggregation module. In: 2022 IEEE 19th International Symposium on Biomedical Imaging (ISBI), pp. 1–5 (2022)

    Google Scholar 

  10. Khader, F., et al.: Medical diffusion-denoising diffusion probabilistic models for 3D medical image generation. arXiv preprintarXiv:2211.03364 (2022)

  11. Lu, W., et al.: An investigation of quantitative accuracy for deep learning based denoising in oncological PET. Phys. Med. Biol.64(16), 165019 (2019)

    Article  Google Scholar 

  12. Lu, Z., Li, Z., Wang, J., Shen, D.: Two-stage self-supervised cycle-consistency network for reconstruction of thin-slice MR images. arXiv preprintarXiv:2106.15395 (2021)

  13. Luo, Y., et al.: 3D transformer-GAN for high-quality PET reconstruction. In: de Bruijne, M., et al. (eds.) MICCAI 2021. LNCS, vol. 12906, pp. 276–285. Springer, Cham (2021).https://doi.org/10.1007/978-3-030-87231-1_27

    Chapter  Google Scholar 

  14. Luo, Y., et al.: Adaptive rectification based adversarial network with spectrum constraint for high-quality PET image synthesis. Med. Image Anal.77, 102335 (2022)

    Article  Google Scholar 

  15. Noroozi, M., Favaro, P.: Unsupervised learning of visual representations by solving jigsaw puzzles. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9910, pp. 69–84. Springer, Cham (2016).https://doi.org/10.1007/978-3-319-46466-4_5

    Chapter  Google Scholar 

  16. Oktay, O., et al.: Attention U-Net: learning where to look for the pancreas. arXiv preprintarXiv:1804.03999 (2018)

  17. Onishi, Y., et al.: Anatomical-guided attention enhances unsupervised PET image denoising performance. Med. Image Anal.74, 102226 (2021)

    Article  Google Scholar 

  18. Rombach, R., Blattmann, A., Lorenz, D., Esser, P., Ommer, B.: High-resolution image synthesis with latent diffusion models. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 10684–10695 (2022)

    Google Scholar 

  19. Slovis, T.L.: The ALARA concept in pediatric CT: myth or reality? Radiology223(1), 5–6 (2002)

    Google Scholar 

  20. Song, T., Yang, F., Dutta, J.: Noise2Void: unsupervised denoising of PET images. Phys. Med. Biol.66(21), 214002 (2021)

    Article  Google Scholar 

  21. Wang, Y., et al.: 3D auto-context-based locality adaptive multi-modality GANs for PET synthesis. IEEE Trans. Med. Imaging38(6), 1328–1339 (2019)

    Article MathSciNet  Google Scholar 

  22. Yan, J., Lim, J., Townsend, D.: MRI-guided brain PET image filtering and partial volume correction. Phys. Med. Biol.60(3), 961 (2015)

    Article  Google Scholar 

  23. Yie, S., Kang, S., Hwang, D., Lee, J.: Self-supervised PET denoising. Nucl. Med. Mol. Imaging54(6), 299–304 (2020)

    Article  Google Scholar 

  24. Zhang, R., Isola, P., Efros, A.A., Shechtman, E., Wang, O.: The unreasonable effectiveness of deep features as a perceptual metric. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 586–595 (2018)

    Google Scholar 

  25. Zhang, X., et al.: Total-body dynamic reconstruction and parametric imaging on the uEXPLORER. J. Nucl. Med.61(2), 285–291 (2020)

    Article  Google Scholar 

Download references

Acknowledgment

This work was supported in part by National Natural Science Foundation of China (No. 62131015), Science and Technology Commission of Shanghai Municipality (STCSM) (No. 21010502600), The Key R&D Program of Guangdong Province, China (No. 2021B0101420006), and the China Postdoctoral Science Foundation (Nos. BX2021333, 2021M703340).

Author information

Authors and Affiliations

  1. School of Biomedical Engineering, ShanghaiTech University, Shanghai, China

    Caiwen Jiang, Yongsheng Pan, Lei Ma, Xiao Zhang, Jiameng Liu, Xiaosong Xiong & Dinggang Shen

  2. Shanghai United Imaging Intelligence Co., Ltd., Shanghai, 200230, China

    Dinggang Shen

  3. Shanghai Artificial Intelligence Laboratory, Shanghai, 200232, China

    Mianxin Liu

  4. Shanghai Clinical Research and Trial Center, Shanghai, 201210, China

    Dinggang Shen

Authors
  1. Caiwen Jiang

    You can also search for this author inPubMed Google Scholar

  2. Yongsheng Pan

    You can also search for this author inPubMed Google Scholar

  3. Mianxin Liu

    You can also search for this author inPubMed Google Scholar

  4. Lei Ma

    You can also search for this author inPubMed Google Scholar

  5. Xiao Zhang

    You can also search for this author inPubMed Google Scholar

  6. Jiameng Liu

    You can also search for this author inPubMed Google Scholar

  7. Xiaosong Xiong

    You can also search for this author inPubMed Google Scholar

  8. Dinggang Shen

    You can also search for this author inPubMed Google Scholar

Corresponding author

Correspondence toDinggang Shen.

Editor information

Editors and Affiliations

  1. Icahn School of Medicine, Mount Sinai, NYC, NY, USA, Tel Aviv University, Tel Aviv, Israel

    Hayit Greenspan

  2. Emory University, Atlanta, GA, USA

    Anant Madabhushi

  3. Queen's University, Kingston, ON, Canada

    Parvin Mousavi

  4. The University of British Columbia, Vancouver, BC, Canada

    Septimiu Salcudean

  5. Yale University, New Haven, CT, USA

    James Duncan

  6. IBM Research, San Jose, CA, USA

    Tanveer Syeda-Mahmood

  7. Johns Hopkins University, Baltimore, MD, USA

    Russell Taylor

Rights and permissions

Copyright information

© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Jiang, C.et al. (2023). PET-Diffusion: Unsupervised PET Enhancement Based on the Latent Diffusion Model. In: Greenspan, H.,et al. Medical Image Computing and Computer Assisted Intervention – MICCAI 2023. MICCAI 2023. Lecture Notes in Computer Science, vol 14220. Springer, Cham. https://doi.org/10.1007/978-3-031-43907-0_1

Download citation

Publish with us

Societies and partnerships

Access this chapter

Subscribe and save

Springer+ Basic
¥17,985 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
JPY 3498
Price includes VAT (Japan)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
JPY 10295
Price includes VAT (Japan)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
JPY 12869
Price includes VAT (Japan)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide -see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

[8]ページ先頭
©2009-2025 Movatter.jp