{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,14]],"date-time":"2026-05-14T15:16:44Z","timestamp":1778771804254,"version":"3.51.4"},"reference-count":59,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2026,5,14]],"date-time":"2026-05-14T00:00:00Z","timestamp":1778716800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"},{"start":{"date-parts":[[2026,5,14]],"date-time":"2026-05-14T00:00:00Z","timestamp":1778716800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"}],"funder":[{"name":"Shanghai Magnolia Talent Plan Pujiang Project","award":["25PJD082"],"award-info":[{"award-number":["25PJD082"]}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62471299"],"award-info":[{"award-number":["62471299"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["82372038"],"award-info":[{"award-number":["82372038"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program of China","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Natural Science Foundation of Guangdong Province in China","award":["2023B1515120007"],"award-info":[{"award-number":["2023B1515120007"]}]},{"name":"Shen- zhen Science, the Technology Program of China","award":["KJZD20240903101307010"],"award-info":[{"award-number":["KJZD20240903101307010"]}]},{"name":"National Natu- ral Science Foundation of China project","award":["UNNC Project ID B0166"],"award-info":[{"award-number":["UNNC Project ID B0166"]}]},{"name":"Yongjiang Technology Innovation Project","award":["2022A-097-G"],"award-info":[{"award-number":["2022A-097-G"]}]},{"name":"Shanghai Municipal Key Clinical Specialty","award":["shslczdzk03403"],"award-info":[{"award-number":["shslczdzk03403"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["npj Digit. Med."],"DOI":"10.1038\/s41746-026-02760-w","type":"journal-article","created":{"date-parts":[[2026,5,14]],"date-time":"2026-05-14T12:25:57Z","timestamp":1778761557000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Generalizable CT-free PET attenuation and scatter correction via few-shot cross domain adaptation"],"prefix":"10.1038","volume":"9","author":[{"given":"Hanzhong","family":"Wang","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Meiyuan","family":"Wen","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiaoya","family":"Qiao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Qianhao","family":"Chen","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yi","family":"An","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xin","family":"Chen","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Rui","family":"Guo","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Qiu","family":"Huang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiaohua","family":"Zhu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhaoping","family":"Cheng","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jiehua","family":"Xu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hairong","family":"Zheng","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Dong","family":"Liang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiangjian","family":"He","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhanli","family":"Hu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Biao","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2026,5,14]]},"reference":[{"key":"2760_CR1","doi-asserted-by":"publisher","first-page":"24","DOI":"10.1038\/s41591-018-0316-z","volume":"25","author":"A Esteva","year":"2019","unstructured":"Esteva, A. et al. A guide to deep learning in healthcare. Nat. Med. 25, 24\u201329 (2019).","journal-title":"Nat. Med."},{"key":"2760_CR2","doi-asserted-by":"publisher","DOI":"10.1038\/s41467-021-26216-9","volume":"12","author":"S Wang","year":"2021","unstructured":"Wang, S. et al. Annotation-efficient deep learning for automatic medical image segmentation. Nat. Commun. 12, 5915 (2021).","journal-title":"Nat. Commun."},{"key":"2760_CR3","doi-asserted-by":"publisher","first-page":"288","DOI":"10.1038\/s42256-021-00305-2","volume":"3","author":"E Korot","year":"2021","unstructured":"Korot, E. et al. Code-free deep learning for multi-modality medical image classification. Nat. Mach. Intell. 3, 288\u2013298 (2021).","journal-title":"Nat. Mach. Intell."},{"key":"2760_CR4","doi-asserted-by":"publisher","first-page":"102444","DOI":"10.1016\/j.media.2022.102444","volume":"79","author":"X Chen","year":"2022","unstructured":"Chen, X. et al. Recent advances and clinical applications of deep learning in medical image analysis. Med. Image Anal. 79, 102444 (2022).","journal-title":"Med. Image Anal."},{"key":"2760_CR5","doi-asserted-by":"publisher","first-page":"1583","DOI":"10.1109\/JPROC.2024.3507831","volume":"112","author":"JS Yoon","year":"2024","unstructured":"Yoon, J. S., Oh, K., Shin, Y., Mazurowski, M. A. & Suk, H.-I. Domain generalization for medical image analysis: A review. Proc. IEEE 112, 1583\u20131609 (2024).","journal-title":"Proc. IEEE"},{"key":"2760_CR6","doi-asserted-by":"publisher","first-page":"474","DOI":"10.1038\/s41568-023-00576-4","volume":"23","author":"J Schwenck","year":"2023","unstructured":"Schwenck, J. et al. Advances in pet imaging of cancer. Nat. Rev. Cancer 23, 474\u2013490 (2023).","journal-title":"Nat. Rev. Cancer"},{"key":"2760_CR7","doi-asserted-by":"publisher","first-page":"11S","DOI":"10.2967\/jnumed.108.057182","volume":"50","author":"R Boellaard","year":"2009","unstructured":"Boellaard, R. Standards for PET image acquisition and quantitative data analysis. J. Nucl. Med. 50, 11S\u201320S (2009).","journal-title":"J. Nucl. Med."},{"key":"2760_CR8","doi-asserted-by":"publisher","first-page":"2395","DOI":"10.1007\/s00259-021-05282-7","volume":"48","author":"I Alberts","year":"2021","unstructured":"Alberts, I. et al. Clinical performance of long axial field of view PET\/CT: a head-to-head intra-individual comparison of the Biograph Vision Quadra with the Biograph Vision PET\/CT. Eur. J. Nucl. Med. Mol. Imaging 48, 2395\u20132404 (2021).","journal-title":"Eur. J. Nucl. Med. Mol. Imaging"},{"key":"2760_CR9","first-page":"012006","volume":"1248","author":"ASA Sabri","year":"2019","unstructured":"Sabri, A. S. A. & Wong, J. H. D. Estimation of effective dose for whole body 18f-fdg pet\/ct examination. J. Phys.: Conf. Ser. 1248, 012006 (2019).","journal-title":"J. Phys.: Conf. Ser."},{"key":"2760_CR10","doi-asserted-by":"publisher","first-page":"710","DOI":"10.1001\/jamainternmed.2025.0505","volume":"185","author":"R Smith-Bindman","year":"2025","unstructured":"Smith-Bindman, R. et al. Projected lifetime cancer risks from current computed tomography imaging. JAMA Intern. Med. 185, 710\u2013719 (2025).","journal-title":"JAMA Intern. Med."},{"key":"2760_CR11","doi-asserted-by":"publisher","first-page":"139","DOI":"10.1002\/mp.16862","volume":"51","author":"S Mostafapour","year":"2024","unstructured":"Mostafapour, S. et al. Ultra-low dose CT scanning for PET\/CT. Med. Phys. 51, 139\u2013155 (2024).","journal-title":"Med. Phys."},{"key":"2760_CR12","first-page":"1985","volume":"66","author":"S Mostafapour","year":"2025","unstructured":"Mostafapour, S. et al. Influence of ultra-low-dose ct on pet image quantification and visual assessment. J. Nucl. Med. 66, 1985\u20131992 (2025).","journal-title":"J. Nucl. Med."},{"key":"2760_CR13","doi-asserted-by":"publisher","DOI":"10.1186\/s40658-023-00569-0","volume":"10","author":"G Krokos","year":"2023","unstructured":"Krokos, G., MacKewn, J., Dunn, J. & Marsden, P. A review of PET attenuation correction methods for PET-MR. EJNMMI Phys. 10, 52 (2023).","journal-title":"EJNMMI Phys."},{"key":"2760_CR14","doi-asserted-by":"publisher","first-page":"62","DOI":"10.1007\/s00259-024-06872-x","volume":"52","author":"Y Lu","year":"2024","unstructured":"Lu, Y. et al. Deep learning-aided respiratory motion compensation in PET\/CT: addressing motion-induced resolution loss, attenuation correction artifacts, and PET-CT misalignment. Eur. J. Nucl. Med. Mol. Imaging 52, 62\u201373 (2024).","journal-title":"Eur. J. Nucl. Med. Mol. Imaging"},{"key":"2760_CR15","doi-asserted-by":"publisher","first-page":"160","DOI":"10.1109\/TRPMS.2020.3009269","volume":"5","author":"JS Lee","year":"2020","unstructured":"Lee, J. S. A review of deep-learning-based approaches for attenuation correction in positron emission tomography. IEEE Trans. Radiat. Plasma Med. Sci. 5, 160\u2013184 (2020).","journal-title":"IEEE Trans. Radiat. Plasma Med. Sci."},{"key":"2760_CR16","first-page":"e200137","volume":"3","author":"J Yang","year":"2020","unstructured":"Yang, J., Sohn, J. H., Behr, S. C., Gullberg, G. T. & Seo, Y. Ct-less direct correction of attenuation and scatter in the image space using deep learning for whole-body FDG PET: potential benefits and pitfalls. Radiology: Artif. Intell. 3, e200137 (2020).","journal-title":"Radiology: Artif. Intell."},{"key":"2760_CR17","doi-asserted-by":"publisher","first-page":"393","DOI":"10.1109\/42.774167","volume":"18","author":"J Nuyts","year":"1999","unstructured":"Nuyts, J. et al. Simultaneous maximum a posteriori reconstruction of attenuation and activity distributions from emission sinograms. IEEE Trans. Med. Imaging 18, 393\u2013403 (1999).","journal-title":"IEEE Trans. Med. Imaging"},{"key":"2760_CR18","doi-asserted-by":"publisher","first-page":"2224","DOI":"10.1109\/TMI.2012.2212719","volume":"31","author":"A Rezaei","year":"2012","unstructured":"Rezaei, A. et al. Simultaneous reconstruction of activity and attenuation in time-of-flight PET. IEEE Trans. Med. Imaging 31, 2224\u20132233 (2012).","journal-title":"IEEE Trans. Med. Imaging"},{"key":"2760_CR19","doi-asserted-by":"publisher","first-page":"1624","DOI":"10.2967\/jnumed.117.202317","volume":"59","author":"D Hwang","year":"2018","unstructured":"Hwang, D. et al. Improving the accuracy of simultaneously reconstructed activity and attenuation maps using deep learning. J. Nucl. Med. 59, 1624\u20131629 (2018).","journal-title":"J. Nucl. Med."},{"key":"2760_CR20","doi-asserted-by":"publisher","first-page":"3086","DOI":"10.1007\/s00259-022-05748-2","volume":"49","author":"T Toyonaga","year":"2022","unstructured":"Toyonaga, T. et al. Deep learning\u2013based attenuation correction for whole-body PET\u2014a multi-tracer study with 18F-FDG, 68Ga-DOTATATE, and 18F-fluciclovine. Eur. J. Nucl. Med. Mol. Imaging 49, 3086\u20133097 (2022).","journal-title":"Eur. J. Nucl. Med. Mol. Imaging"},{"key":"2760_CR21","doi-asserted-by":"publisher","first-page":"1833","DOI":"10.1007\/s00259-021-05637-0","volume":"49","author":"D Hwang","year":"2022","unstructured":"Hwang, D., Kang, S. K., Kim, K. Y., Choi, H. & Lee, J. S. Comparison of deep learning-based emission-only attenuation correction methods for positron emission tomography. Eur. J. Nucl. Med. Mol. Imaging 49, 1833\u20131842 (2022).","journal-title":"Eur. J. Nucl. Med. Mol. Imaging"},{"key":"2760_CR22","doi-asserted-by":"publisher","first-page":"852","DOI":"10.2967\/jnumed.117.198051","volume":"59","author":"AP Leynes","year":"2018","unstructured":"Leynes, A. P. 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. 59, 852\u2013858 (2018).","journal-title":"J. Nucl. Med."},{"key":"2760_CR23","doi-asserted-by":"publisher","first-page":"215016","DOI":"10.1088\/1361-6560\/ab4eb7","volume":"64","author":"X Dong","year":"2019","unstructured":"Dong, X. et al. Synthetic CT generation from non-attenuation corrected PET images for whole-body PET imaging. Phys. Med. Biol. 64, 215016 (2019).","journal-title":"Phys. Med. Biol."},{"key":"2760_CR24","doi-asserted-by":"publisher","first-page":"429","DOI":"10.2967\/jnumed.118.209288","volume":"60","author":"A Torrado-Carvajal","year":"2019","unstructured":"Torrado-Carvajal, A. et al. Dixon-vibe deep learning (divide) pseudo-CT synthesis for pelvis PET\/MR attenuation correction. J. Nucl. Med. 60, 429\u2013435 (2019).","journal-title":"J. Nucl. Med."},{"key":"2760_CR25","doi-asserted-by":"crossref","unstructured":"Shi, L. et al. A novel loss function incorporating imaging acquisition physics for PET attenuation map generation using deep learning. In Shen, D. et al. (eds.) International Conference on Medical Image Computing and Computer-assisted Intervention, 723\u2013731 (Springer, 2019).","DOI":"10.1007\/978-3-030-32251-9_79"},{"key":"2760_CR26","doi-asserted-by":"publisher","DOI":"10.1038\/s41467-022-33562-9","volume":"13","author":"R Guo","year":"2022","unstructured":"Guo, R. et al. Using domain knowledge for robust and generalizable deep learning-based CT-free PET attenuation and scatter correction. Nat. Commun. 13, 5882 (2022).","journal-title":"Nat. Commun."},{"key":"2760_CR27","doi-asserted-by":"publisher","first-page":"1022","DOI":"10.1007\/s12350-022-03092-4","volume":"30","author":"Y Du","year":"2023","unstructured":"Du, Y. et al. Deep-learning-based estimation of attenuation map improves attenuation correction performance over direct attenuation estimation for myocardial perfusion spect. J. Nucl. Cardiol. 30, 1022\u20131037 (2023).","journal-title":"J. Nucl. Cardiol."},{"key":"2760_CR28","doi-asserted-by":"publisher","first-page":"870","DOI":"10.1002\/mp.16914","volume":"51","author":"R Jahangir","year":"2024","unstructured":"Jahangir, R., Kamali-Asl, A., Arabi, H. & Zaidi, H. Strategies for deep learning-based attenuation and scatter correction of brain 18F-FDG PET images in the image domain. Med. Phys. 51, 870\u2013880 (2024).","journal-title":"Med. Phys."},{"key":"2760_CR29","doi-asserted-by":"crossref","unstructured":"Hou, J. et al. An investigation into the cross-tracer generalizability of deep learning-based PET attenuation correction. IEEE Trans. Radiat. Plasma Med. Sci. (2025).","DOI":"10.1109\/TRPMS.2025.3566630"},{"key":"2760_CR30","doi-asserted-by":"publisher","first-page":"6867","DOI":"10.1007\/s00330-019-06229-1","volume":"29","author":"I Shiri","year":"2019","unstructured":"Shiri, I. et al. Direct attenuation correction of brain PET images using only emission data via a deep convolutional encoder-decoder (deep-dac). Eur. Radiol. 29, 6867\u20136879 (2019).","journal-title":"Eur. Radiol."},{"key":"2760_CR31","doi-asserted-by":"publisher","first-page":"055011","DOI":"10.1088\/1361-6560\/ab652c","volume":"65","author":"X Dong","year":"2020","unstructured":"Dong, X. et al. Deep learning-based attenuation correction in the absence of structural information for whole-body positron emission tomography imaging. Phys. Med. Biol. 65, 055011 (2020).","journal-title":"Phys. Med. Biol."},{"key":"2760_CR32","doi-asserted-by":"publisher","first-page":"2533","DOI":"10.1007\/s00259-020-04852-5","volume":"47","author":"I Shiri","year":"2020","unstructured":"Shiri, I. 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 47, 2533\u20132548 (2020).","journal-title":"Eur. J. Nucl. Med. Mol. Imaging"},{"key":"2760_CR33","doi-asserted-by":"publisher","first-page":"3667","DOI":"10.1002\/hbm.25039","volume":"41","author":"H Arabi","year":"2020","unstructured":"Arabi, H., Bortolin, K., Ginovart, N., Garibotto, V. & Zaidi, H. Deep learning-guided joint attenuation and scatter correction in multitracer neuroimaging studies. Hum. Brain Mapp. 41, 3667\u20133679 (2020).","journal-title":"Hum. Brain Mapp."},{"key":"2760_CR34","doi-asserted-by":"publisher","first-page":"1034","DOI":"10.1007\/s00259-022-06053-8","volume":"50","author":"I Shiri","year":"2023","unstructured":"Shiri, I. et al. Decentralized collaborative multi-institutional pet attenuation and scatter correction using federated deep learning. Eur. J. Nucl. Med. Mol. Imaging 50, 1034\u20131050 (2023).","journal-title":"Eur. J. Nucl. Med. Mol. Imaging"},{"key":"2760_CR35","doi-asserted-by":"publisher","DOI":"10.1186\/s40658-024-00666-8","volume":"11","author":"H Sun","year":"2024","unstructured":"Sun, H. et al. Artificial intelligence-based joint attenuation and scatter correction strategies for multi-tracer total-body PET. EJNMMI Phys. 11, 66 (2024).","journal-title":"EJNMMI Phys."},{"key":"2760_CR36","doi-asserted-by":"publisher","first-page":"1173","DOI":"10.1109\/TBME.2021.3117407","volume":"69","author":"H Guan","year":"2021","unstructured":"Guan, H. & Liu, M. Domain adaptation for medical image analysis: a survey. IEEE Trans. Biomed. Eng. 69, 1173\u20131185 (2021).","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"2760_CR37","doi-asserted-by":"publisher","first-page":"230","DOI":"10.1016\/j.neucom.2021.08.159","volume":"489","author":"X Yu","year":"2022","unstructured":"Yu, X. et al. Transfer learning for medical image analyses: A survey. Neurocomputing 489, 230\u2013254 (2022).","journal-title":"Neurocomputing"},{"key":"2760_CR38","doi-asserted-by":"publisher","first-page":"3852","DOI":"10.1007\/s00259-022-05817-6","volume":"49","author":"A Piccardo","year":"2022","unstructured":"Piccardo, A. et al. Joint EANM\/SIOPE\/RAPNO practice guidelines\/snmmi procedure standards for imaging of paediatric gliomas using pet with radiolabelled amino acids and [18F] fdg: version 1.0. Eur. J. Nucl. Med. Mol. Imaging 49, 3852\u20133869 (2022).","journal-title":"Eur. J. Nucl. Med. Mol. Imaging"},{"key":"2760_CR39","first-page":"e230024","volume":"5","author":"J Wasserthal","year":"2023","unstructured":"Wasserthal, J. et al. Totalsegmentator: robust segmentation of 104 anatomic structures in CT images. Radiology: Artif. Intell. 5, e230024 (2023).","journal-title":"Radiology: Artif. Intell."},{"key":"2760_CR40","doi-asserted-by":"publisher","first-page":"122S","DOI":"10.2967\/jnumed.108.057307","volume":"50","author":"RL Wahl","year":"2009","unstructured":"Wahl, R. L., Jacene, H., Kasamon, Y. & Lodge, M. A. From recist to percist: evolving considerations for pet response criteria in solid tumors. J. Nucl. Med. 50, 122S\u2013150S (2009).","journal-title":"J. Nucl. Med."},{"key":"2760_CR41","doi-asserted-by":"publisher","first-page":"241","DOI":"10.1007\/s00259-014-2903-7","volume":"42","author":"H-J Im","year":"2015","unstructured":"Im, H.-J. et al. Prognostic value of volumetric parameters of 18F-FDG PET in non-small-cell lung cancer: a meta-analysis. Eur. J. Nucl. Med. Mol. Imaging 42, 241\u2013251 (2015).","journal-title":"Eur. J. Nucl. Med. Mol. Imaging"},{"key":"2760_CR42","doi-asserted-by":"publisher","first-page":"5","DOI":"10.1007\/s13139-017-0493-6","volume":"52","author":"H-J Im","year":"2018","unstructured":"Im, H.-J., Bradshaw, T., Solaiyappan, M. & Cho, S. Y. Current methods to define metabolic tumor volume in positron emission tomography: which one is better? Nucl. Med. Mol. Imaging 52, 5\u201315 (2018).","journal-title":"Nucl. Med. Mol. Imaging"},{"key":"2760_CR43","doi-asserted-by":"publisher","first-page":"1424","DOI":"10.2967\/jnumed.121.263067","volume":"63","author":"J Driessen","year":"2022","unstructured":"Driessen, J. et al. The impact of semiautomatic segmentation methods on metabolic tumor volume, intensity, and dissemination radiomics in 18F-FDG PET scans of patients with classical Hodgkin lymphoma. J. Nucl. Med. 63, 1424\u20131430 (2022).","journal-title":"J. Nucl. Med."},{"key":"2760_CR44","doi-asserted-by":"publisher","first-page":"109","DOI":"10.1016\/j.patcog.2018.03.005","volume":"80","author":"Y Li","year":"2018","unstructured":"Li, Y., Wang, N., Shi, J., Hou, X. & Liu, J. Adaptive batch normalization for practical domain adaptation. Pattern Recognit. 80, 109\u2013117 (2018).","journal-title":"Pattern Recognit."},{"key":"2760_CR45","doi-asserted-by":"publisher","first-page":"922","DOI":"10.1007\/s00259-002-0796-3","volume":"29","author":"C Burger","year":"2002","unstructured":"Burger, C. et al. Pet attenuation coefficients from CT images: experimental evaluation of the transformation of CT into PET 511-keV attenuation coefficients. Eur. J. Nucl. Med. Mol. Imaging 29, 922\u2013927 (2002).","journal-title":"Eur. J. Nucl. Med. Mol. Imaging"},{"key":"2760_CR46","doi-asserted-by":"publisher","first-page":"166","DOI":"10.1148\/radiol.2511081300","volume":"251","author":"B Huang","year":"2009","unstructured":"Huang, B., Law, M. W.-M. & Khong, P.-L. Whole-body PET\/CT scanning: estimation of radiation dose and cancer risk. Radiology 251, 166\u2013174 (2009).","journal-title":"Radiology"},{"key":"2760_CR47","doi-asserted-by":"publisher","first-page":"543","DOI":"10.1016\/j.cpet.2021.06.010","volume":"16","author":"AB McMillan","year":"2021","unstructured":"McMillan, A. B. & Bradshaw, T. J. Artificial intelligence\u2013based data corrections for attenuation and scatter in position emission tomography and single-photon emission computed tomography. PET Clin. 16, 543\u2013552 (2021).","journal-title":"PET Clin."},{"key":"2760_CR48","doi-asserted-by":"publisher","first-page":"102949","DOI":"10.1016\/j.artmed.2024.102949","volume":"156","author":"E Pachetti","year":"2024","unstructured":"Pachetti, E. & Colantonio, S. A systematic review of few-shot learning in medical imaging. Artif. Intell. Med. 156, 102949 (2024).","journal-title":"Artif. Intell. Med."},{"key":"2760_CR49","doi-asserted-by":"crossref","unstructured":"Ronneberger, O., Fischer, P. & Brox, T. U-net: Convolutional networks for biomedical image segmentation. In Navab, N., Hornegger, J., Wells, W. M. & Frangi, A. F. (eds.) International Conference on Medical Image Computing and Computer-assisted Intervention, 234\u2013241 (Springer, 2015).","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"2760_CR50","doi-asserted-by":"publisher","first-page":"2577","DOI":"10.1007\/s00259-025-07086-5","volume":"52","author":"H Wang","year":"2025","unstructured":"Wang, H. et al. Optimizing MR-based attenuation correction in hybrid PET\/MR using deep learning: validation with a flatbed insert and consistent patient positioning. Eur. J. Nucl. Med. Mol. Imaging 52, 2577\u20132588 (2025).","journal-title":"Eur. J. Nucl. Med. Mol. Imaging"},{"key":"2760_CR51","doi-asserted-by":"crossref","unstructured":"Ahnen, M. et al. Performance of the ultra-compact fully integrated brain pet system bpet. In Editors, I. N. (ed.) 2020 IEEE Nuclear Science Symposium and Medical Imaging Conference (NSS\/MIC), 1\u20134 (IEEE, 2020).","DOI":"10.1109\/NSS\/MIC42677.2020.9508026"},{"key":"2760_CR52","doi-asserted-by":"publisher","first-page":"3558","DOI":"10.1007\/s00259-023-06341-x","volume":"50","author":"S Vandenberghe","year":"2023","unstructured":"Vandenberghe, S. et al. Walk-through flat panel total-body pet: a patient-centered design for high throughput imaging at lower cost using doi-capable high-resolution monolithic detectors. Eur. J. Nucl. Med. Mol. Imaging 50, 3558\u20133571 (2023).","journal-title":"Eur. J. Nucl. Med. Mol. Imaging"},{"key":"2760_CR53","doi-asserted-by":"publisher","first-page":"41259","DOI":"10.52202\/075280-1789","volume":"36","author":"A Bansal","year":"2023","unstructured":"Bansal, A. et al. Cold diffusion: Inverting arbitrary image transforms without noise. Adv. Neural Inf. Process. Syst. 36, 41259\u201341282 (2023).","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"2760_CR54","first-page":"75460","volume":"38","author":"Z Geng","year":"2026","unstructured":"Geng, Z., Deng, M., Bai, X., Kolter, J. Z. & He, K. Mean flows for one-step generative modeling. Adv. Neural Inf. Process. Syst. 38, 75460\u201375482 (2026).","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"2760_CR55","doi-asserted-by":"publisher","first-page":"1867","DOI":"10.1007\/s11263-020-01303-4","volume":"128","author":"U Dmitry","year":"2020","unstructured":"Dmitry, U., Vedaldi, A. & Victor, L. Deep image prior. Int. J. Comput. Vis. 128, 1867\u20131888 (2020).","journal-title":"Int. J. Comput. Vis."},{"key":"2760_CR56","doi-asserted-by":"publisher","first-page":"3004","DOI":"10.1007\/s00259-025-07156-8","volume":"52","author":"H Wang","year":"2025","unstructured":"Wang, H. et al. Robust and generalizable artificial intelligence for multi-organ segmentation in ultra-low-dose total-body PET imaging: a multi-center and cross-tracer study. Eur. J. Nucl. Med. Mol. Imaging 52, 3004\u20133018 (2025).","journal-title":"Eur. J. Nucl. Med. Mol. Imaging"},{"key":"2760_CR57","doi-asserted-by":"crossref","unstructured":"Perez, E., Strub, F., De Vries, H., Dumoulin, V. & Courville, A. Film: Visual reasoning with a general conditioning layer. Proceedings of the AAAI Conference on Artificial Intelligence 32, (2018).","DOI":"10.1609\/aaai.v32i1.11671"},{"key":"2760_CR58","doi-asserted-by":"publisher","first-page":"742","DOI":"10.1007\/s11263-019-01198-w","volume":"128","author":"Y Wu","year":"2020","unstructured":"Wu, Y. & He, K. Group normalization. Int. J. Comput. Vis. 128, 742\u2013755 (2020).","journal-title":"Int. J. Comput. Vis."},{"key":"2760_CR59","unstructured":"Glorot, X., Bordes, A. & Bengio, Y. Deep sparse rectifier neural networks. In Gordon, G., Dunson, D. & Dud\u00edk, M. (eds.) Proceedings of the 14th International Conference on Artificial Intelligence and Statistics, 315\u2013323 (JMLR Workshop and Conference Proceedings, 2011)."}],"container-title":["npj Digital Medicine"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.nature.com\/articles\/s41746-026-02760-w","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41746-026-02760-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41746-026-02760-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,5,14]],"date-time":"2026-05-14T15:02:52Z","timestamp":1778770972000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.nature.com\/articles\/s41746-026-02760-w"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,5,14]]},"references-count":59,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2026,12]]}},"alternative-id":["2760"],"URL":"https:\/\/doi.org\/10.1038\/s41746-026-02760-w","relation":{},"ISSN":["2398-6352"],"issn-type":[{"value":"2398-6352","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,5,14]]},"assertion":[{"value":"22 September 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"5 May 2026","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"14 May 2026","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"The authors declare no competing interests.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"374"}}