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Learning CT-free attenuation-corrected total-body PET images through deep learning

  • Nuclear Medicine
  • Published:
European Radiology Aims and scope Submit manuscript

Abstract

Objectives

Total-body PET/CT scanners with long axial fields of view have enabled unprecedented image quality and quantitative accuracy. However, the ionizing radiation from CT is a major issue in PET imaging, which is more evident with reduced radiopharmaceutical doses in total-body PET/CT. Therefore, we attempted to generate CT-free attenuation-corrected (CTF-AC) total-body PET images through deep learning.

Methods

Based on total-body PET data from 122 subjects (29 females and 93 males), a well-established cycle-consistent generative adversarial network (Cycle-GAN) was employed to generate CTF-AC total-body PET images directly while introducing site structures as prior information. Statistical analyses, including Pearson correlation coefficient (PCC) and t-tests, were utilized for the correlation measurements.

Results

The generated CTF-AC total-body PET images closely resembled real AC PET images, showing reduced noise and good contrast in different tissue structures. The obtained peak signal-to-noise ratio and structural similarity index measure values were 36.92 ± 5.49 dB (p < 0.01) and 0.980 ± 0.041 (p < 0.01), respectively. Furthermore, the standardized uptake value (SUV) distribution was consistent with that of real AC PET images.

Conclusion

Our approach could directly generate CTF-AC total-body PET images, greatly reducing the radiation risk to patients from redundant anatomical examinations. Moreover, the model was validated based on a multidose-level NAC-AC PET dataset, demonstrating the potential of our method for low-dose PET attenuation correction. In future work, we will attempt to validate the proposed method with total-body PET/CT systems in more clinical practices.

Clinical relevance statement

The ionizing radiation from CT is a major issue in PET imaging, which is more evident with reduced radiopharmaceutical doses in total-body PET/CT. Our CT-free PET attenuation correction method would be beneficial for a wide range of patient populations, especially for pediatric examinations and patients who need multiple scans or who require long-term follow-up.

Key Points

• CT is the main source of radiation in PET/CT imaging, especially for total-body PET/CT devices, and reduced radiopharmaceutical doses make the radiation burden from CT more obvious.

• The CT-free PET attenuation correction method would be beneficial for patients who need multiple scans or long-term follow-up by reducing additional radiation from redundant anatomical examinations.

• The proposed method could directly generate CT-free attenuation-corrected (CTF-AC) total-body PET images, which is beneficial for PET/MRI or PET-only devices lacking CT image poses.

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Abbreviations

18F-FDG:

18F-Fluorodeoxyglucose

ACCT:

Attenuation-corrected CT

CTF-AC:

CT-free attenuation-corrected

Cycle-GAN:

Cycle-consistent adversarial generative network

GAN:

Generative adversarial network

LAFOV:

Long axial fields of views

NAC:

Nonattenuation-corrected

PET:

Positron emission tomography

PSF:

Point spread function

PSNR:

Peak signal-to-noise ratio

ROI:

Region of interest

SSIM:

Structural similarity index measure

STD:

Standard deviation

SUV:

Standardized uptake value

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Funding

This work was supported by the National Natural Science Foundation of China (32022042 and 62101540), the Shenzhen Excellent Technological Innovation Talent Training Project of China (RCJC20200714114436080), the Shenzhen Medical Research Funds of China (B2301002), and the Shenzhen Science and Technology Program (JCYJ20220818101804009 and RCBS20210706092218043).

Author information

Author notes

  1. Co-first authors Wenbo Li and Zhenxing Huang contributed equally to this work.

Authors and Affiliations

  1. Lauterbur Research Center for Biomedical Imaging, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, China

    Wenbo Li, Zhenxing Huang, Zixiang Chen, Lulu Zhang, Liwen Wan, Yongfeng Yang, Hairong Zheng, Dong Liang & Zhanli Hu

  2. Shenzhen College of Advanced Technology, University of Chinese Academy of Sciences, Beijing, 101408, China

    Wenbo Li & Zixiang Chen

  3. Department of Nuclear Medicine, Sun Yat-sen University Cancer Center, Guangzhou, 510060, China

    Yongluo Jiang, Chao Zhou, Xu Zhang & Wei Fan

  4. Central Research Institute, United Imaging Healthcare Group, Shanghai, 201807, China

    Yumo Zhao

  5. Key Laboratory of Biomedical Imaging Science and System, Chinese Academy of Sciences, Shenzhen, 518055, China

    Yongfeng Yang, Hairong Zheng, Dong Liang & Zhanli Hu

Authors
  1. Wenbo Li
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Corresponding author

Correspondence to Zhanli Hu.

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Guarantor

The scientific guarantor of this publication is Zhanli Hu.

Conflict of interest

YZ is an employee of the Central Research Institute, United Imaging Healthcare Group. The remaining authors of this manuscript declare no relationships with any companies whose products or services may be related to the subject matter of the article.

Statistics and biometry

No complex statistical methods were necessary for this paper.

Informed consent

Written informed consent was obtained from all subjects (patients) in this study.

Ethical approval

The Ethics Committee of Sun Yat-sen University Cancer Center approved this study.

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• experimental

• performed at one institution

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Li, W., Huang, Z., Chen, Z. et al. Learning CT-free attenuation-corrected total-body PET images through deep learning. Eur Radiol (2024). https://doi.org/10.1007/s00330-024-10647-1

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