Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2022 Mar;15(1):72-82.
doi: 10.1007/s12194-022-00652-8. Epub 2022 Feb 8.

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

Kibo Ote  1 , Fumio Hashimoto  2
Affiliations

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

Kibo Ote et al. Radiol Phys Technol. 2022 Mar.

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.

Keywords: Deep learning; Direction information; Image reconstruction; Positron emission tomography; Time-of-flight.

PubMed Disclaimer

References

    1. Phelps ME. PET: molecular imaging and its biological applications. New York: Springer; 2012.
    1. Defrise M, Kinahan PE. Data acquisition and image reconstruction for 3D PET in The Theory and Practice of 3D PET. Dordrecht: Springer; 1998.
    1. 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. - DOI
    1. 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. - DOI
    1. 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. - DOI

MeSH terms

LinkOut - more resources