Eliminating the second CT scan of dual-tracer total-body PET/CT via deep learning-based image synthesis and registration

Boellaard R, Delgado-Bolton R, Oyen WJ, Giammarile F, Tatsch K, Eschner W, et al. FDG PET/CT: EANM procedure guidelines for tumour imaging: version 2.0. Eur J Nucl Med Mol Imaging. 2015;42:328–54. https://doi.org/10.1007/s00259-014-2961-x.

Article  PubMed  CAS  Google Scholar 

Bailly C, Bodet-Milin C, Bourgeois M, Gouard S, Ansquer C, Barbaud M, et al. Exploring Tumor Heterogeneity using PET imaging: the big picture. Cancers (Basel). 2019;11:1282. https://doi.org/10.3390/cancers11091282.

Article  PubMed  CAS  Google Scholar 

Hope TA, Allen-Auerbach M, Bodei L, Calais J, Dahlbom M, Dunnwald LK, et al. SNMMI Procedure Standard/EANM Practice Guideline for SSTR PET: imaging neuroendocrine tumors. J Nucl Med. 2023;64:204–10. https://doi.org/10.2967/jnumed.122.264860.

Article  PubMed  CAS  Google Scholar 

Huang R, Pu Y, Huang S, Yang C, Yang F, Pu Y, et al. FAPI-PET/CT in Cancer Imaging: a potential Novel Molecule of the Century. Front Oncol. 2022;12:854658. https://doi.org/10.3389/fonc.2022.854658.

Article  PubMed  PubMed Central  CAS  Google Scholar 

Heinzmann K, Carter LM, Lewis JS, Aboagye EO. Multiplexed imaging for diagnosis and therapy. Nat Biomedical Eng. 2017;1:697–713. https://doi.org/10.1038/s41551-017-0131-8.

Article  Google Scholar 

Pearce MS, Salotti JA, Little MP, McHugh K, Lee C, Kim KP, et al. Radiation exposure from CT scans in childhood and subsequent risk of leukaemia and brain tumours: a retrospective cohort study. Lancet. 2012;380:499–505. https://doi.org/10.1016/S0140-6736(12)60815-0.

Article  PubMed  PubMed Central  Google Scholar 

Shao YH, Tsai K, Kim S, Wu YJ, Demissie K. Exposure to Tomographic scans and Cancer risks. JNCI Cancer Spectr. 2020;4:pkz072. https://doi.org/10.1093/jncics/pkz072.

Article  PubMed  Google Scholar 

Foucault A, Ancelet S, Dreuil S, Caer-Lorho S, Le Ducou H, Brisse H, et al. Childhood cancer risks estimates following CT scans: an update of the French CT cohort study. Eur Radiol. 2022;32:5491–8. https://doi.org/10.1007/s00330-022-08602-z.

Article  PubMed  Google Scholar 

de Berrington A, Pasqual E, Veiga L. Epidemiological studies of CT scans and cancer risk: the state of the science. Br J Radiol. 2021;94:20210471. https://doi.org/10.1259/bjr.20210471.

Article  Google Scholar 

Bosch de Basea Gomez M, Thierry-Chef I, Harbron R, Hauptmann M, Byrnes G, Bernier M-O, et al. Risk of hematological malignancies from CT radiation exposure in children, adolescents and young adults. Nat Med. 2023;29:3111–9. https://doi.org/10.1038/s41591-023-02620-0.

Article  PubMed  PubMed Central  CAS  Google Scholar 

Hu Y, Liu G, Yu H, Wang Y, Li C, Tan H, et al. Feasibility of acquisitions using total-body PET/CT with an Ultra-low 18F-FDG activity. J Nucl Med. 2022;63:959–65. https://doi.org/10.2967/jnumed.121.262038.

Article  PubMed  PubMed Central  CAS  Google Scholar 

Tan H, Cai D, Sui X, Qi C, Mao W, Zhang Y, et al. Investigating ultra-low-dose total-body 18F-FDG PET/CT in colorectal cancer: initial experience. Eur J Nucl Med Mol Imaging. 2022;49:1002–11. https://doi.org/10.1007/s00259-021-05537-3.

Article  PubMed  Google Scholar 

Liu G, Hu P, Yu H, Tan H, Zhang Y, Yin H, et al. Ultra-low-activity total-body dynamic PET imaging allows equal performance to full-activity PET imaging for investigating kinetic metrics of 18F-FDG in healthy volunteers. Eur J Nucl Med Mol Imaging. 2021;48:2373–83. https://doi.org/10.1007/s00259-020-05173-3.

Article  PubMed  Google Scholar 

Sachpekidis C, Pan L, Kopp-Schneider A, Weru V, Hassel JC, Dimitrakopoulou-Strauss A. Application of the long axial field-of-view PET/CT with low-dose [18F]FDG in melanoma. Eur J Nucl Med Mol Imaging. 2023;50:1158–67. https://doi.org/10.1007/s00259-022-06070-7.

Article  PubMed  CAS  Google Scholar 

Dong X, Wang T, Lei Y, Higgins K, Liu T, Curran WJ, et al. Synthetic CT generation from non-attenuation corrected PET images for whole-body PET imaging. Phys Med Biol. 2019;64:215016. https://doi.org/10.1088/1361-6560/ab4eb7.

Article  PubMed  PubMed Central  Google Scholar 

Armanious K, Hepp T, Kustner T, Dittmann H, Nikolaou K, La Fougere C, et al. Independent attenuation correction of whole body [18F]FDG-PET using a deep learning approach with generative adversarial networks. EJNMMI Res. 2020;10:53. https://doi.org/10.1186/s13550-020-00644-y.

Article  PubMed  PubMed Central  Google Scholar 

Kong L, Lian C, Huang D, Li Z, Hu Y, Zhou Q. Breaking the Dilemma of Medical Image-to-image Translation. 35th Conference on Neural Information Processing Systems (NeurIPS). Electr Network; 2021. pp. 1964–78.

Li Z, Zhang Q, Li H, Kong L, Wang H, Liang B, et al. Using RegGAN to generate synthetic CT images from CBCT images acquired with different linear accelerators. BMC Cancer. 2023;23:828. https://doi.org/10.1186/s12885-023-11274-7.

Article  PubMed  PubMed Central  CAS  Google Scholar 

Wang H, Liu X, Kong L, Huang Y, Chen H, Ma X, et al. Improving CBCT image quality to the CT level using RegGAN in esophageal cancer adaptive radiotherapy. Strahlenther Onkol. 2023;199:485–97. https://doi.org/10.1007/s00066-022-02039-5.

Article  PubMed  PubMed Central  Google Scholar 

Shiri I, Arabi H, Geramifar P, Hajianfar G, Ghafarian P, Rahmim A, et al. Deep-JASC: joint attenuation and scatter correction in whole-body 18F-FDG PET using a deep residual network. Eur J Nucl Med Mol Imaging. 2020;47:2533–48. https://doi.org/10.1007/s00259-020-04852-5.

Article  PubMed  Google Scholar 

Li W, Huang Z, Chen Z, Jiang Y, Zhou C, Zhang X, et al. Learning CT-free attenuation-corrected total-body PET images through deep learning. Eur Radiol. 2024;34:5578–87. https://doi.org/10.1007/s00330-024-10647-1.

Article  PubMed  CAS  Google Scholar 

Guo R, Xue S, Hu J, Sari H, Mingels C, Zeimpekis K, et al. Using domain knowledge for robust and generalizable deep learning-based CT-free PET attenuation and scatter correction. Nat Commun. 2022;13:5882. https://doi.org/10.1038/s41467-022-33562-9.

Article  PubMed  PubMed Central  CAS  Google Scholar 

Hu Z, Li Y, Zou S, Xue H, Sang Z, Liu X, et al. Obtaining PET/CT images from non-attenuation corrected PET images in a single PET system using Wasserstein generative adversarial networks. Phys Med Biol. 2020;65:215010. https://doi.org/10.1088/1361-6560/aba5e9.

Article  PubMed  CAS  Google Scholar 

Li Q, Zhu X, Zou S, Zhang N, Liu X, Yang Y, et al. Eliminating CT radiation for clinical PET examination using deep learning. Eur J Radiol. 2022;154:110422. https://doi.org/10.1016/j.ejrad.2022.110422.

Article  PubMed  Google Scholar 

Rao F, Wu Z, Han L, Yang B, Han W, Zhu W. Delayed PET imaging using image synthesis network and nonrigid registration without additional CT scan. Med Phys. 2022;49:3233–45. https://doi.org/10.1002/mp.15574.

Article  PubMed  Google Scholar 

Yu H, Gu Y, Fan W, Gao Y, Wang M, Zhu X, et al. Expert consensus on oncological [18F]FDG total-body PET/CT imaging (version 1). Eur Radiol. 2023;33:615–26. https://doi.org/10.1007/s00330-022-08960-8.

Article  PubMed  Google Scholar 

Kalra MK, Maher MM, Toth TL, Schmidt B, Westerman BL, Morgan HT, et al. Techniques and applications of automatic tube current modulation for CT. Radiology. 2004;233:649–57.

Article  PubMed  Google Scholar 

Tan H, Gu Y, Yu H, Hu P, Zhang Y, Mao W, et al. Total-body PET/CT: current applications and future perspectives. AJR Am J Roentgenol. 2020;215:325–37. https://doi.org/10.2214/AJR.19.22705.

Article  PubMed  Google Scholar 

Slovis TL. The ALARA concept in pediatric CT: myth or reality? Radiology. 2002;223:5–6. https://doi.org/10.1148/radiol.2231012100.

Article  PubMed  Google Scholar 

Liu G, Mao W, Yu H, Hu Y, Gu J, Shi H. One-stop [18F]FDG and [68Ga]Ga-DOTA-FAPI-04 total-body PET/CT examination with dual-low activity: a feasibility study. Eur J Nucl Med Mol Imaging. 2023;50:2271–81. https://doi.org/10.1007/s00259-023-06207-2.

Artic

Comments (0)

No login
gif