Skip to main content
SpringerLink
Log in

Deep-learning-based fast TOF-PET image reconstruction using direction information

  • Research Article
  • Published:
Radiological Physics and Technology Aims and scope Submit manuscript

Abstract

Although deep learning for application in positron emission tomography (PET) image reconstruction has attracted the attention of researchers, the image quality must be further improved. In this study, we propose a novel convolutional neural network (CNN)-based fast time-of-flight PET (TOF-PET) image reconstruction method to fully utilize the direction information of coincidence events. The proposed method inputs view-grouped histo-images into a 3D CNN as a multi-channel image to use the direction information of such events. We evaluated the proposed method using Monte Carlo simulation data obtained from a digital brain phantom. Compared with a case without direction information, the peak signal-to-noise ratio and structural similarity were improved by 1.2 dB and 0.02, respectively, at a coincidence time resolution of 300 ps. The calculation times of the proposed method were significantly lower than those of a conventional iterative reconstruction. These results indicate that the proposed method improves both the speed and image quality of a TOF-PET image reconstruction.

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

Access this article

Log in via an institution

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

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8

Similar content being viewed by others

References

  1. Phelps ME. PET: molecular imaging and its biological applications. New York: Springer; 2012.

    Google Scholar 

  2. Defrise M, Kinahan PE. Data acquisition and image reconstruction for 3D PET in The Theory and Practice of 3D PET. Dordrecht: Springer; 1998.

    Google Scholar 

  3. 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  Google Scholar 

  4. Chen KT, Gong E, de Carvalho MFB, Xu J, Boumis A, Khalighi M, et al. Ultra-low-dose 18F-Florbetaben amyloid PET imaging using deep learning with multi-contrast MRI inputs. Radiology. 2019;290(3):649–56.

    Article  Google Scholar 

  5. Gong K, Guan J, Liu CC, Qi J. PET image denoising using a deep neural network through fine tuning. IEEE Trans Radiat Plasma Med Sci. 2018;3(2):153–61.

    Article  Google Scholar 

  6. Hashimoto F, Ohba H, Ote K, Teramoto A, Tsukada H. Dynamic PET image denoising using deep convolutional neural networks without prior training datasets. IEEE Access. 2019;7:96594–603.

    Article  Google Scholar 

  7. Hashimoto F, Ohba H, Ote K, Kakimoto A, Tsukada H, Ouchi Y. 4D deep image prior: dynamic PET image denoising using an unsupervised four-dimensional branch convolutional neural network. Phys Med Biol. 2021;66(1):015006.

    Article  CAS  Google Scholar 

  8. Hashimoto F, Ito M, Ote K, Isobe T, Okada H, Ouchi Y. Deep learning-based attenuation correction for brain PET with various radiotracers. Ann Nucl Med. 2021;35(6):691–701.

    Article  CAS  Google Scholar 

  9. 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(8):2405–15.

    Article  Google Scholar 

  10. Yang B, Ying L, Tang J. Artificial neural network enhanced Bayesian PET image reconstruction. IEEE Trans Med Imaging. 2018;37(6):1297–309.

    Article  Google Scholar 

  11. Gong K, Guan J, Kim K, Zhang X, Yang J, Seo Y, et al. Iterative PET image reconstruction using convolutional neural network representation. IEEE Trans Med Imaging. 2019;38(3):675–85.

    Article  Google Scholar 

  12. Gong K, Catana C, Qi J, Li Q. PET image reconstruction using deep image prior. IEEE Trans Med Imaging. 2019;38(7):1655–65.

    Article  Google Scholar 

  13. Whiteley W, Luk WK, Gregor J. DirectPET: full-size neural network PET reconstruction from sinogram data. J Med Imaging. 2020;7(3):032503.

    Article  Google Scholar 

  14. Reader AJ, Corda G, Mehranian A, da Costa-Luis C, Ellis S, Schnabel JA. Deep learning for PET image reconstruction. IEEE Trans Radiat Plasma Med Sci. 2021;5(1):1–25.

    Article  Google Scholar 

  15. 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(1):35–43.

    Article  Google Scholar 

  16. Kandarpa VSS, Bousse A, Benoit D, Visvikis D. DUG-RECON: a framework for direct image reconstruction using convolutional generative networks. IEEE Trans Radiat Plasma Med Sci. 2021;5(1):44–53.

    Article  Google Scholar 

  17. Mehranian A, Reader AJ. Model-based deep learning PET image reconstruction using forward-backward splitting expectation maximization. IEEE Trans Radiat Plasma Med Sci. 2021;5(1):54–64.

    Article  Google Scholar 

  18. Zhu B, Liu JZ, Cauley SF, Rosen BR, Rosen MS. Image reconstruction by domain-transform manifold learning. Nature. 2018;555(7697):487–92.

    Article  CAS  Google Scholar 

  19. Häggström I, Schmidtlein CR, Campanella G, Fuchs TJ. DeepPET: a deep encoder–decoder network for directly solving the PET image reconstruction inverse problem. Med Image Anal. 2019;54:253–62.

    Article  Google Scholar 

  20. Hudson HM, Larkin RS. Accelerated image reconstruction using ordered subsets of projection data. IEEE Trans Med Imaging. 1994;13(4):601–9.

    Article  CAS  Google Scholar 

  21. Whiteley W, Panin V, Zhou C, Cabello J, Bharkhada D, Gregor J. FastPET: near real-time reconstruction of PET histo-image data using a neural network. IEEE Trans Radiat Plasma Med Sci. 2021;5(1):65–77.

    Article  Google Scholar 

  22. Matej S, Surti S, Jayanthi S, Daube-Witherspoon ME, Lewitt RM, Karp JS. Efficient 3-D TOF PET reconstruction using view-grouped histo-images: DIRECT—Direct image reconstruction for TOF. IEEE Trans Med Imaging. 2009;28(5):739–51.

    Article  Google Scholar 

  23. Snyder DL, Thomas LJ, Ter-Pogossian MM. A matheematical model for positron-emission tomography systems having time-of-flight measurements. IEEE Trans Nucl Sci. 1981;28(3):3575–83.

    Article  Google Scholar 

  24. Tomitani T. Image reconstruction and noise evaluation in photon time-of-flight assisted positron emission tomography. IEEE Trans Nucl Sci. 1981;28(6):4581–9.

    Article  Google Scholar 

  25. Tanaka E. Line-writing data acquisition and singal-to-noise ratio in time-of-flight positron emission tomography. IEEE Comput Soc. 1982;448:101–8.

    Google Scholar 

  26. Çiçek Ö, Abdulkadir A, Lienkamp SS, Brox T, Ronneberger O. 3D U-Net: learning dense volumetric segmentation from sparse annotation. In: Ourselin S, Joskowicz L, Sabuncu MR, Unal G, Wells W, editors. Medical Image Computing and Computer Assisted Intervention (MICCAI) LNCS, vol. 9901. Cham: Springer; 2016. p. 424–32.

    Google Scholar 

  27. Collins DL, Zijdenbos AP, Kollokian V, Sled JG, Kabani NJ, Holmes CJ, Evans AC. Design and construction of a realistic digital brain phantom. IEEE Trans Med Imaging. 1998;17(3):463–8.

    Article  CAS  Google Scholar 

  28. Saito A, Yoshikawa E, Omura T, Yamanaka T, Ote K, Isobe T, et al. Development of a brain PET scanner with motion correction using motion capture technology. IEEE Nucl Sci Symp Med Imaging Conf 2018;M-07–146

  29. Tanaka E, Kudo H. Subset-dependent relaxation in block-iterative algorithms for image reconstruction in emission tomography. Phys Med Biol. 2003;48(10):1405–22.

    Article  Google Scholar 

  30. Nakayama T, Kudo H. Derivation and implementation of ordered-subsets algorithms for list-mode PET data. IEEE Nuc Sci Symp Med Imaging. Conf Rec 2005;3540-3

  31. Vandenberghe S, Daube-Witherspoon ME, Lewitt RM, Karp JS. Fast reconstruction of 3D time-of-flight PET data by axial rebinning and transverse mashing. Phys Med Biol. 2006;51(6):1603–21.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Central Research Laboratory, Hamamatsu Photonics K.K, Hamamatsu, 434-8601, Japan

    Kibo Ote & Fumio Hashimoto

Authors
  1. Kibo Ote
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Fumio Hashimoto
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Fumio Hashimoto.

Ethics declarations

Conflict of interest

The authors are employees of the Hamamatsu Photonics K.K. The company had no control over the interpretation, writing, or publication of this manuscript.

Ethical approval

This study did not include research on human subjects or animals.

Additional information

Publisher's Note

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

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Ote, K., Hashimoto, F. Deep-learning-based fast TOF-PET image reconstruction using direction information. Radiol Phys Technol 15, 72–82 (2022). https://doi.org/10.1007/s12194-022-00652-8

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12194-022-00652-8

Keywords

Search

Navigation