Evidence map›Paper›PMID 38355987›Full record

ArticleEuropean radiology2024

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

Wenbo Li, Zhenxing Huang, Zixiang Chen, Yongluo Jiang, Chao Zhou, Xu Zhang, Wei Fan, Yumo Zhao, Lulu Zhang, Liwen Wan and 4 more

Abstract read
PubMed Publisher
In one paragraph

Article in European radiology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.

0numbers the graph read from it
0cells of the map it votes in
12citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

12 citing papers in PubMed.

  1. Article
  2. Review
  3. Beyond CT: Attenuation correction for stand-alone brain PET.Zeitschrift fur medizinische Physik · 2026
    Review
  4. Article
  5. Article
  6. PET scout: A valuable technology in pediatricEuropean journal of nuclear medicine and molecular imaging · 2026
    Article
  7. Article
  8. Article
  9. Evaluation of the efficacy ofFrontiers in oncology · 2025
    Article
  10. Review
  11. Article
  12. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

14 authors.

Wenbo Li *Lauterbur Research Center for Biomedical Imaging, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, China.
Zhenxing Huang *Lauterbur Research Center for Biomedical Imaging, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, China.
Zixiang ChenLauterbur Research Center for Biomedical Imaging, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, China.
Yongluo JiangDepartment of Nuclear Medicine, Sun Yat-sen University Cancer Center, Guangzhou, 510060, China.
Chao ZhouDepartment of Nuclear Medicine, Sun Yat-sen University Cancer Center, Guangzhou, 510060, China.
Xu ZhangDepartment of Nuclear Medicine, Sun Yat-sen University Cancer Center, Guangzhou, 510060, China.
Wei FanDepartment of Nuclear Medicine, Sun Yat-sen University Cancer Center, Guangzhou, 510060, China.
Yumo ZhaoCentral Research Institute, United Imaging Healthcare Group, Shanghai, 201807, China.
Lulu ZhangLauterbur Research Center for Biomedical Imaging, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, China.
Liwen WanLauterbur Research Center for Biomedical Imaging, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, China.
Yongfeng YangLauterbur Research Center for Biomedical Imaging, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, China.
Hairong ZhengLauterbur Research Center for Biomedical Imaging, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, China.
Dong LiangLauterbur Research Center for Biomedical Imaging, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, China.
Zhanli HuLauterbur Research Center for Biomedical Imaging, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, China. zl.hu@siat.ac.cn.ORCID http://orcid.org/0000-0003-0618-6240

Funding

National Natural Science Foundation of China 32022042National Natural Science Foundation of China 62101540Shenzhen Excellent Technological Innovation Talent Training Project of China RCJC20200714114436080Shenzhen Medical Research Funds of China B2301002Shenzhen Science and Technology Innovation Program JCYJ20220818101804009Shenzhen Science and Technology Innovation Program RCBS20210706092218043
6 · The paper itself

Abstract

objectivesTotal-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.

methodsBased 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.

resultsThe 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.

conclusionOur 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.

Indexed as

Deep LearningPositron Emission Tomography Computed TomographyWhole Body ImagingAdultAgedFemaleHumansImage Processing, Computer-AssistedMaleMiddle AgedSignal-To-Noise RatioYoung AdultDeep learningPositron emission tomographyRadiation

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.