Torigian DA, Zaidi H, Kwee TC, et al. PET/MR imaging: technical aspects and potential clinical applications. Radiology. 2013;267(1):26–44. https://doi.org/10.1148/radiol.13121038.

Article  PubMed  Google Scholar 

Zhang M, Guo R, Wang H, et al. Rapid advancements in PET/MR: the 4th chinese PET/MR academic symposium. Eur J Nucl Med Mol Imaging. 2024;51(9):2526–7. https://doi.org/10.1007/s00259-024-06779-7.

Article  PubMed  Google Scholar 

Zhang M, Guo R, Wang H, et al. PET/MR in practice: the 3rd Chinese PET/MR Academic Symposium. Eur J Nucl Med Mol Imaging. 2023;51(1):1–2. https://doi.org/10.1007/s00259-023-06397-9.

Article  PubMed  Google Scholar 

Bezrukov I, Mantlik F, Schmidt H, et al. MR-Based PET attenuation correction for PET/MR imaging. Semin Nucl Med. 2013;43(1):45–59. https://doi.org/10.1053/j.semnuclmed.2012.08.002.

Article  PubMed  Google Scholar 

Keereman V, Fierens Y, Broux T, et al. MRI-based attenuation correction for PET/MRI using ultrashort echo time sequences. J Nucl Med. 2010;51(5):812–8. https://doi.org/10.2967/jnumed.109.065425.

Article  PubMed  Google Scholar 

Zeimpekis K, Delso G, Wiesinger F, et al. Investigation of 3D UTE MRI for lung PET attenuation correction. J Nucl Med. 2014;55(Supplement 1):2103–2103. https://jnm.snmjournals.org/content/55/supplement_1/2103.short.

Delso G, Wiesinger F, Sacolick LI, et al. Clinical evaluation of zero-echo-time MR imaging for the segmentation of the skull. J Nucl Med. 2015;56(3):417–22. https://doi.org/10.2967/jnumed.114.149997.

Article  PubMed  Google Scholar 

Martinez-Möller A, Souvatzoglou M, Delso G, et al. Tissue classification as a potential approach for attenuation correction in whole-body PET/MRI: evaluation with PET/CT data. J Nucl Med. 2009;50(4):520–6. https://doi.org/10.2967/jnumed.108.054726.

Article  PubMed  Google Scholar 

Zaidi H, Ojha N, Morich M, et al. Design and performance evaluation of a whole-body Ingenuity TF PET-MRI system. Phys Med Biol. 2011;56(10):3091–106. https://doi.org/10.1088/0031-9155/56/10/013.

Article  CAS  PubMed  PubMed Central  Google Scholar 

Bezrukov I, Schmidt H, Mantlik F, et al. MR-based attenuation correction methods for improved PET quantification in lesions within bone and susceptibility artifact regions. J Nucl Med. 2013;54(10):1768–74. https://doi.org/10.2967/jnumed.112.113209.

Article  PubMed  Google Scholar 

Arabi H, Rager O, Alem A, et al. Clinical assessment of MR-guided 3-class and 4-class attenuation correction in PET/MR. Mol Imaging Biol. 2015;17(2):264–76. https://doi.org/10.1007/s11307-014-0777-5.

Article  CAS  PubMed  Google Scholar 

Szegedy C. Ioffe S, Vanhoucke V, et al. Inception-ResNet and the impact of residual connections on learning. 2016. arXiv preprint arXiv:1602.07261.

Qiao X, Jiang C, Li P, et al. Improving breast tumor segmentation in PET via attentive transformation based normalization. IEEE J Biomed Health Inform. 2022;26(7):3261–71. https://doi.org/10.1109/jbhi.2022.3164570.

Article  PubMed  Google Scholar 

Chen C, Miao J, Wu D, et al. MA-SAM: Modality-agnostic SAM adaptation for 3D medical image segmentation. Med Image Anal. 2024;98:103310. https://doi.org/10.1016/j.media.2024.103310.

Article  PubMed  Google Scholar 

Ding W, Ding Q, Chen K, et al. A shortened model for logan reference plot implemented via the self-supervised neural network for parametric PET imaging. IEEE Trans Med Imaging. 2023;42(10):2842–52. https://doi.org/10.1109/tmi.2023.3266455.

Article  PubMed  Google Scholar 

Qiao X, Wang H, Meng H, et al. Multi-modality deep learning-based [(68)Ga]Ga-DOTA-FAPI-04 PET polar map generation: potential value in detecting reactive fibrosis after myocardial infarction. Eur J Nucl Med Mol Imaging. 2024;51(13):3944–59. https://doi.org/10.1007/s00259-024-06850-3.

Article  CAS  PubMed  Google Scholar 

Xie H, Liu Q, Zhou B, et al. Unified noise-aware network for low-count PET denoising with varying count levels. IEEE Trans Radiat Plasma Med Sci. Apr2024;8(4):366–78. https://doi.org/10.1109/trpms.2023.3334105.

Article  PubMed  Google Scholar 

Guo R, Xue S, Hu J, et al. "Using domain knowledge for robust and generalizable deep learning-based CT-free PET attenuation and scatter correction. Nat Commun. 2022;13(1):5882. https://doi.org/10.1038/s41467-022-33562-9.

Article  CAS  PubMed  PubMed Central  Google Scholar 

Leynes AP, Yang J, Wiesinger F, et al. Zero-echo-time and dixon deep pseudo-CT (ZeDD CT): direct generation of Pseudo-CT images for Pelvic PET/MRI attenuation correction using deep convolutional neural networks with multiparametric MRI. J Nucl Med. 2018;59(5):852–8. https://doi.org/10.2967/jnumed.117.198051.

Article  PubMed  PubMed Central  Google Scholar 

ShiyamSundar LK, Yu J, Muzik O, et al. Fully automated, semantic segmentation of whole-body (18)F-FDG PET/CT images based on data-centric artificial intelligence. J Nucl Med. 2022;63(12):1941–8. https://doi.org/10.2967/jnumed.122.264063

Article  CAS  Google Scholar 

Tustison NJ, Avants BB, Cook PA, et al. N4ITK: improved N3 bias correction. IEEE Trans Med Imaging. 2010;29(6):1310–20. https://doi.org/10.1109/tmi.2010.2046908.

Article  PubMed  PubMed Central  Google Scholar 

Gatidis S, Früh M, Fabritius MP, et al. Results from the autoPET challenge on fully automated lesion segmentation in oncologic PET/CT imaging. Nat Mach Intell. 2024. 2024/10/30. https://doi.org/10.1038/s42256-024-00912-9.

Wasserthal J, Breit HC, Meyer MT, et al. TotalSegmentator: robust segmentation of 104 anatomic structures in CT images. Radiol Artif Intell. 2023;5(5):e230024. https://doi.org/10.1148/ryai.230024.

Article  PubMed  PubMed Central  Google Scholar 

Zhang J, Huang Y, Zhang Z, et al. Whole-body lesion segmentation in 18f-fdg pet/ct. arXiv preprint arXiv:2209.07851.

Elschot M, Selnæs KM, Johansen H, et al. The effect of including bone in dixon-based attenuation correction for (18)F-Fluciclovine PET/MRI of prostate cancer. J Nucl Med. Dec2018;59(12):1913–7. https://doi.org/10.2967/jnumed.118.208868.

Article  CAS  PubMed  Google Scholar