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Learning CT-free attenuation-corrected total-body PET images through deep learning.
Li, Wenbo; Huang, Zhenxing; Chen, Zixiang; Jiang, Yongluo; Zhou, Chao; Zhang, Xu; Fan, Wei; Zhao, Yumo; Zhang, Lulu; Wan, Liwen; Yang, Yongfeng; Zheng, Hairong; Liang, Dong; Hu, Zhanli.
Afiliación
  • Li W; Lauterbur Research Center for Biomedical Imaging, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, China.
  • Huang Z; Shenzhen College of Advanced Technology, University of Chinese Academy of Sciences, Beijing, 101408, China.
  • Chen Z; Lauterbur Research Center for Biomedical Imaging, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, China.
  • Jiang Y; Lauterbur Research Center for Biomedical Imaging, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, China.
  • Zhou C; Shenzhen College of Advanced Technology, University of Chinese Academy of Sciences, Beijing, 101408, China.
  • Zhang X; Department of Nuclear Medicine, Sun Yat-sen University Cancer Center, Guangzhou, 510060, China.
  • Fan W; Department of Nuclear Medicine, Sun Yat-sen University Cancer Center, Guangzhou, 510060, China.
  • Zhao Y; Department of Nuclear Medicine, Sun Yat-sen University Cancer Center, Guangzhou, 510060, China.
  • Zhang L; Department of Nuclear Medicine, Sun Yat-sen University Cancer Center, Guangzhou, 510060, China.
  • Wan L; Central Research Institute, United Imaging Healthcare Group, Shanghai, 201807, China.
  • Yang Y; Lauterbur Research Center for Biomedical Imaging, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, China.
  • Zheng H; Lauterbur Research Center for Biomedical Imaging, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, China.
  • Liang D; Lauterbur Research Center for Biomedical Imaging, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, China.
  • Hu Z; Key Laboratory of Biomedical Imaging Science and System, Chinese Academy of Sciences, Shenzhen, 518055, China.
Eur Radiol ; 34(9): 5578-5587, 2024 Sep.
Article en En | MEDLINE | ID: mdl-38355987
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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Texto completo: 1 Banco de datos: MEDLINE Asunto principal: Imagen de Cuerpo Entero / Tomografía Computarizada por Tomografía de Emisión de Positrones / Aprendizaje Profundo Límite: Adult / Aged / Female / Humans / Male / Middle aged Idioma: En Revista: Eur Radiol Asunto de la revista: RADIOLOGIA Año: 2024 Tipo del documento: Article País de afiliación: China

Texto completo: 1 Banco de datos: MEDLINE Asunto principal: Imagen de Cuerpo Entero / Tomografía Computarizada por Tomografía de Emisión de Positrones / Aprendizaje Profundo Límite: Adult / Aged / Female / Humans / Male / Middle aged Idioma: En Revista: Eur Radiol Asunto de la revista: RADIOLOGIA Año: 2024 Tipo del documento: Article País de afiliación: China