{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,25]],"date-time":"2026-05-25T01:02:27Z","timestamp":1779670947214,"version":"3.53.1"},"reference-count":82,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2026,3,30]],"date-time":"2026-03-30T00:00:00Z","timestamp":1774828800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"},{"start":{"date-parts":[[2026,5,25]],"date-time":"2026-05-25T00:00:00Z","timestamp":1779667200000},"content-version":"vor","delay-in-days":56,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"}],"funder":[{"name":"the Basic Research Talent Development Program of Huashan Hospital, Fudan University","award":["2025JC077"],"award-info":[{"award-number":["2025JC077"]}]},{"DOI":"10.13039\/501100001809","name":"the National Natural Science Foundation of China","doi-asserted-by":"crossref","award":["82394432"],"award-info":[{"award-number":["82394432"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100001809","name":"the National Natural Science Foundation of China","doi-asserted-by":"crossref","award":["82394434,82272039, and 82021002"],"award-info":[{"award-number":["82394434,82272039, and 82021002"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"name":"the Shanghai Medical Innovation & Development Foundation","award":["SMIDF-150-2025A18"],"award-info":[{"award-number":["SMIDF-150-2025A18"]}]},{"name":"Shanghai Science and Technology Program Project","award":["25TS1405000"],"award-info":[{"award-number":["25TS1405000"]}]},{"name":"STI2030-Major Projects","award":["2022ZD0211600"],"award-info":[{"award-number":["2022ZD0211600"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["npj Digit. Med."],"DOI":"10.1038\/s41746-026-02570-0","type":"journal-article","created":{"date-parts":[[2026,3,30]],"date-time":"2026-03-30T13:10:11Z","timestamp":1774876211000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["A unified deep learning framework for cross-platform harmonization of multi-tracer PET quantification in neurodegenerative disease"],"prefix":"10.1038","volume":"9","author":[{"given":"Jing","family":"Wang","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Aocheng","family":"Zhong","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qian","family":"Xu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Haolin","family":"Huang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yuhua","family":"Zhu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jiaying","family":"Lu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Min","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jiehui","family":"Jiang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chengyang","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ming","family":"Ni","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kaicong","family":"Sun","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yihui","family":"Guan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jie","family":"Lu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mei","family":"Tian","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Dinggang","family":"Shen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Huiwei","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qian","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chuantao","family":"Zuo","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,3,30]]},"reference":[{"key":"2570_CR1","doi-asserted-by":"publisher","first-page":"7","DOI":"10.1097\/RLI.0000000000000200","volume":"51","author":"S Gatidis","year":"2016","unstructured":"Gatidis, S. et al. Comprehensive oncologic imaging in infants and preschool children with substantially reduced radiation exposure using combined simultaneous \u00b9\u2078F-fluorodeoxyglucose positron emission tomography\/magnetic resonance imaging: a direct comparison to \u00b9\u2078F-fluorodeoxyglucose positron emission tomography\/computed tomography. Investig. Radiol. 51, 7\u201314 (2016).","journal-title":"Investig. Radiol."},{"key":"2570_CR2","doi-asserted-by":"publisher","first-page":"1131","DOI":"10.2967\/jnumed.119.233940","volume":"61","author":"O Martin","year":"2020","unstructured":"Martin, O. et al. PET\/MRI versus PET\/CT for whole-body staging: results from a single-center observational study on 1,003 sequential examinations. J. Nucl. Med. 61, 1131\u20131136 (2020).","journal-title":"J. Nucl. Med."},{"key":"2570_CR3","doi-asserted-by":"publisher","DOI":"10.1259\/bjr.20210388","volume":"94","author":"M Hosono","year":"2021","unstructured":"Hosono, M. et al. Cumulative radiation doses from recurrent PET\u2013CT examinations. Br. J. Radiol. 94, 20210388 (2021).","journal-title":"Br. J. Radiol."},{"key":"2570_CR4","doi-asserted-by":"publisher","first-page":"9","DOI":"10.1056\/NEJMoa2212948","volume":"388","author":"CH van Dyck","year":"2023","unstructured":"van Dyck, C. H. et al. Lecanemab in early Alzheimer\u2019s disease. N. Engl. J. Med. 388, 9\u201321 (2023).","journal-title":"N. Engl. J. Med."},{"key":"2570_CR5","doi-asserted-by":"publisher","first-page":"1691","DOI":"10.1056\/NEJMoa2100708","volume":"384","author":"MA Mintun","year":"2021","unstructured":"Mintun, M. A. et al. Donanemab in early Alzheimer\u2019s disease. N. Engl. J. Med. 384, 1691\u20131704 (2021).","journal-title":"N. Engl. J. Med."},{"key":"2570_CR6","doi-asserted-by":"publisher","DOI":"10.1002\/alz.14338","volume":"21","author":"GD Rabinovici","year":"2025","unstructured":"Rabinovici, G. D. et al. Updated appropriate use criteria for amyloid and tau PET: a report from the Alzheimer\u2019s Association and Society for Nuclear Medicine and Molecular Imaging Workgroup. Alzheimer's Dement. 21, e14338 (2025).","journal-title":"Alzheimer's Dement."},{"key":"2570_CR7","doi-asserted-by":"publisher","first-page":"669","DOI":"10.2967\/jnumed.123.265670","volume":"64","author":"WA Weber","year":"2023","unstructured":"Weber, W. A. et al. What is theranostics? J. Nucl. Med. 64, 669\u2013670 (2023).","journal-title":"J. Nucl. Med."},{"key":"2570_CR8","doi-asserted-by":"publisher","first-page":"362","DOI":"10.14283\/jpad.2023.30","volume":"10","author":"J Cummings","year":"2023","unstructured":"Cummings, J. et al. Lecanemab: appropriate use recommendations. J. Prev. Alzheimer's Dis. 10, 362\u2013377 (2023).","journal-title":"J. Prev. Alzheimer's Dis."},{"key":"2570_CR9","doi-asserted-by":"publisher","first-page":"5102","DOI":"10.1002\/alz.13883","volume":"20","author":"M Shekari","year":"2024","unstructured":"Shekari, M. et al. Stress testing the Centiloid: precision and variability of PET quantification of amyloid pathology. Alzheimer's Dement. 20, 5102\u20135113 (2024).","journal-title":"Alzheimer's Dement."},{"key":"2570_CR10","doi-asserted-by":"publisher","first-page":"99","DOI":"10.1186\/s13195-021-00836-1","volume":"13","author":"SK Royse","year":"2021","unstructured":"Royse, S. K. et al. Validation of amyloid PET positivity thresholds in centiloids: a multisite PET study approach. Alzheimer's Res. Ther. 13, 99 (2021).","journal-title":"Alzheimer's Res. Ther."},{"key":"2570_CR11","doi-asserted-by":"publisher","first-page":"1565","DOI":"10.1016\/j.jalz.2018.06.1353","volume":"14","author":"M Navitsky","year":"2018","unstructured":"Navitsky, M. et al. Standardization of amyloid quantitation with florbetapir standardized uptake value ratios to the Centiloid scale. Alzheimer's Dement. 14, 1565\u20131571 (2018).","journal-title":"Alzheimer's Dement."},{"key":"2570_CR12","doi-asserted-by":"publisher","first-page":"302","DOI":"10.1007\/s00259-020-04942-4","volume":"48","author":"BJ Hanseeuw","year":"2020","unstructured":"Hanseeuw, B. J. et al. Defining a Centiloid scale threshold predicting long-term progression to dementia in patients attending the memory clinic: an [18\u2009F] flutemetamol amyloid PET study. Eur. J. Nucl. Med. Mol. Imaging 48, 302\u2013310 (2020).","journal-title":"Eur. J. Nucl. Med. Mol. Imaging"},{"key":"2570_CR13","doi-asserted-by":"publisher","first-page":"5833","DOI":"10.1002\/alz.13908","volume":"20","author":"A Leuzy","year":"2024","unstructured":"Leuzy, A. et al. Harmonizing tau positron emission tomography in Alzheimer\u2019s disease: the CenTauR scale and the joint propagation model. Alzheimer's Dement. 20, 5833\u20135848 (2024).","journal-title":"Alzheimer's Dement."},{"key":"2570_CR14","doi-asserted-by":"publisher","first-page":"670","DOI":"10.2967\/jnumed.123.265766","volume":"65","author":"WJ Jagust","year":"2024","unstructured":"Jagust, W. J. et al. Quantitative brain amyloid PET. J. Nucl. Med. 65, 670\u2013678 (2024).","journal-title":"J. Nucl. Med."},{"key":"2570_CR15","doi-asserted-by":"publisher","first-page":"66","DOI":"10.1097\/RLI.0000000000000208","volume":"51","author":"F Seith","year":"2016","unstructured":"Seith, F. et al. Comparison of positron emission tomography quantification using magnetic resonance\u2013 and computed tomography\u2013based attenuation correction in physiological tissues and lesions. Investig. Radiol. 51, 66\u201371 (2016).","journal-title":"Investig. Radiol."},{"key":"2570_CR16","doi-asserted-by":"publisher","first-page":"103185","DOI":"10.1016\/j.ejmp.2023.103185","volume":"117","author":"JM Sousa","year":"2024","unstructured":"Sousa, J. M. et al. Comparison of quantitative [11\u2009C]PE2I brain PET studies between an integrated PET\/MR and a stand-alone PET system. Phys. Med. 117, 103185 (2024).","journal-title":"Phys. Med."},{"key":"2570_CR17","doi-asserted-by":"publisher","first-page":"346","DOI":"10.1016\/j.neuroimage.2016.12.010","volume":"147","author":"CN Ladefoged","year":"2017","unstructured":"Ladefoged, C. N. et al. A multi-centre evaluation of eleven clinically feasible brain PET\/MRI attenuation correction techniques using a large cohort of patients. NeuroImage 147, 346\u2013359 (2017).","journal-title":"NeuroImage"},{"key":"2570_CR18","doi-asserted-by":"publisher","first-page":"23TR02","DOI":"10.1088\/1361-6560\/abb0f8","volume":"65","author":"C Catana","year":"2020","unstructured":"Catana, C. Attenuation correction for human PET\/MRI studies. Phys. Med. Biol. 65, 23TR02 (2020).","journal-title":"Phys. Med. Biol."},{"key":"2570_CR19","doi-asserted-by":"publisher","DOI":"10.1186\/s40658-023-00590-3","volume":"10","author":"M Hamdi","year":"2023","unstructured":"Hamdi, M., Ying, C., An, H. & Laforest, R. An automatic pipeline for PET\/MRI attenuation correction validation in the brain. EJNMMI Phys. 10, 71 (2023).","journal-title":"EJNMMI Phys."},{"key":"2570_CR20","doi-asserted-by":"publisher","DOI":"10.1016\/j.neuroimage.2020.117221","volume":"222","author":"CN Ladefoged","year":"2020","unstructured":"Ladefoged, C. N. et al. AI-driven attenuation correction for brain PET\/MRI: clinical evaluation of a dementia cohort and importance of the training group size. Neuroimage 222, 117221 (2020).","journal-title":"Neuroimage"},{"key":"2570_CR21","doi-asserted-by":"publisher","first-page":"1351","DOI":"10.1007\/s00259-020-05061-w","volume":"48","author":"K Gong","year":"2020","unstructured":"Gong, K. et al. Attenuation correction using deep Learning and integrated UTE\/multi-echo Dixon sequence: evaluation in amyloid and tau PET imaging. Eur. J. Nucl. Med. Mol. Imaging 48, 1351\u20131361 (2020).","journal-title":"Eur. J. Nucl. Med. Mol. Imaging"},{"key":"2570_CR22","doi-asserted-by":"publisher","first-page":"918","DOI":"10.2967\/jnumed.115.166967","volume":"57","author":"T Koesters","year":"2016","unstructured":"Koesters, T. et al. Dixon sequence with superimposed model-based bone compartment provides highly accurate PET\/MR attenuation correction of the brain. J. Nucl. Med. 57, 918\u2013924 (2016).","journal-title":"J. Nucl. Med."},{"key":"2570_CR23","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":"2570_CR24","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":"2570_CR25","doi-asserted-by":"publisher","first-page":"71","DOI":"10.1007\/s12149-022-01820-x","volume":"37","author":"G Akamatsu","year":"2023","unstructured":"Akamatsu, G. et al. A review of harmonization strategies for quantitative PET. Ann. Nucl. Med. 37, 71\u201388 (2023).","journal-title":"Ann. Nucl. Med."},{"key":"2570_CR26","doi-asserted-by":"publisher","first-page":"17","DOI":"10.1007\/s00259-017-3740-2","volume":"44","author":"N Aide","year":"2017","unstructured":"Aide, N. et al. EANM\/EARL harmonization strategies in PET quantification: from daily practice to multicentre oncological studies. Eur. J. Nucl. Med. Mol. Imaging 44, 17\u201331 (2017).","journal-title":"Eur. J. Nucl. Med. Mol. Imaging"},{"key":"2570_CR27","doi-asserted-by":"publisher","first-page":"035014","DOI":"10.1088\/1361-6560\/acaf49","volume":"68","author":"L Shi","year":"2023","unstructured":"Shi, L. et al. Deep learning-based attenuation map generation with simultaneously reconstructed PET activity and attenuation and low-dose application. Phys. Med. Biol. 68, 035014 (2023).","journal-title":"Phys. Med. Biol."},{"key":"2570_CR28","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2023.103046","volume":"92","author":"S Dayarathna","year":"2024","unstructured":"Dayarathna, S. et al. Deep learning based synthesis of MRI, CT and PET: review and analysis. Med. Image Anal. 92, 103046 (2024).","journal-title":"Med. Image Anal."},{"key":"2570_CR29","doi-asserted-by":"crossref","unstructured":"Guan, Y. et al. Synthetic CT generation via variant invertible network for brain PET attenuation correction. IEEE Trans. Radiat. Plasma Med. Sci. 9, 325\u2013336 (2024).","DOI":"10.1109\/TRPMS.2024.3453009"},{"key":"2570_CR30","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2023.107268","volume":"164","author":"Z Liu","year":"2023","unstructured":"Liu, Z. et al. Recent progress in transformer-based medical image analysis. Comput. Biol. Med. 164, 107268 (2023).","journal-title":"Comput. Biol. Med."},{"key":"2570_CR31","doi-asserted-by":"publisher","DOI":"10.1007\/s10916-024-02105-8","volume":"48","author":"S Takahashi","year":"2024","unstructured":"Takahashi, S. et al. Comparison of vision transformers and convolutional neural networks in medical image analysis: a systematic review. J. Med. Syst. 48, 84 (2024).","journal-title":"J. Med. Syst."},{"key":"2570_CR32","doi-asserted-by":"crossref","unstructured":"He, K. et al. Masked autoencoders are scalable vision learners. In Proc. IEEE\/CVF Conference on Computer Vision and Pattern Recognition, 16000\u201316009 (IEEE, 2022).","DOI":"10.1109\/CVPR52688.2022.01553"},{"key":"2570_CR33","doi-asserted-by":"publisher","first-page":"1566","DOI":"10.1007\/s00259-025-07096-3","volume":"52","author":"Z Shen","year":"2025","unstructured":"Shen, Z. et al. Cross-modality PET image synthesis for Parkinson's Disease diagnosis: a leap from [18\u2009F]FDG to [11\u2009C]CFT. Eur. J. Nucl. Med. Mol. Imaging 52, 1566 \u20131575 (2025).","journal-title":"Eur. J. Nucl. Med. Mol. Imaging"},{"key":"2570_CR34","doi-asserted-by":"publisher","DOI":"10.1016\/j.xcrm.2024.101463","volume":"5","author":"M Salehjahromi","year":"2024","unstructured":"Salehjahromi, M. et al. Synthetic PET from CT improves diagnosis and prognosis for lung cancer: proof of concept. Cell Rep. Med. 5, 101463 (2024).","journal-title":"Cell Rep. Med."},{"key":"2570_CR35","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2024.103190","volume":"96","author":"X Guo","year":"2024","unstructured":"Guo, X. et al. TAI-GAN: a temporally and anatomically informed generative adversarial network for early-to-late frame conversion in dynamic cardiac PET inter-frame motion correction. Med. Image Anal. 96, 103190 (2024).","journal-title":"Med. Image Anal."},{"key":"2570_CR36","first-page":"3371","volume":"11","author":"P Vincent","year":"2010","unstructured":"Vincent, P. et al. Stacked denoising autoencoders: Learning useful representations in a deep network with a local denoising criterion. J. Mach. Learn. Res. 11, 3371\u20133408 (2010).","journal-title":"J. Mach. Learn. Res."},{"key":"2570_CR37","doi-asserted-by":"publisher","first-page":"100386","DOI":"10.1016\/j.jpi.2024.100386","volume":"15","author":"S Jiang","year":"2024","unstructured":"Jiang, S., Hondelink, L., Suriawinata, A. A. & Hassanpour, S. Masked pre-training of transformers for histology image analysis. J. Pathol. Inform. 15, 100386 (2024).","journal-title":"J. Pathol. Inform."},{"key":"2570_CR38","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2025.103770","volume":"107","author":"F Tang","year":"2026","unstructured":"Tang, F. et al. Hi-End-MAE: hierarchical encoder-driven masked autoencoders are stronger vision learners for medical image segmentation. Med. Image Anal. 107, 103770 (2026).","journal-title":"Med. Image Anal."},{"key":"2570_CR39","doi-asserted-by":"crossref","unstructured":"Liang, J. et al. Swinir: Image restoration using Swin transformer. In Proc. IEEE\/CVF International Conference on Computer Vision, 1833\u20131844 (IEEE, 2021).","DOI":"10.1109\/ICCVW54120.2021.00210"},{"key":"2570_CR40","doi-asserted-by":"crossref","unstructured":"Zamir, S. W. et al. Restormer: efficient transformer for high-resolution image restoration. In Proc. IEEE\/CVF Conference on Computer Vision and Pattern Recognition, 5728\u20135739 (IEEE, 2022).","DOI":"10.1109\/CVPR52688.2022.00564"},{"key":"2570_CR41","doi-asserted-by":"crossref","unstructured":"Yang, Z. et al. Drmc: a generalist model with dynamic routing for multi-center pet image synthesis. In Proc. International Conference on Medical Image Computing and Computer-Assisted Intervention, 36\u201346 (Springer, 2023).","DOI":"10.1007\/978-3-031-43898-1_4"},{"key":"2570_CR42","doi-asserted-by":"crossref","unstructured":"Yang, Z. et al. All-in-one medical image restoration via task-adaptive routing. In Proc. International Conference on Medical Image Computing and Computer-Assisted Intervention, 67\u201377 (Springer, 2024).","DOI":"10.1007\/978-3-031-72104-5_7"},{"key":"2570_CR43","doi-asserted-by":"publisher","first-page":"3276","DOI":"10.1007\/s00259-023-06279-0","volume":"50","author":"A Jovalekic","year":"2023","unstructured":"Jovalekic, A. et al. Validation of quantitative assessment of florbetaben PET scans as an adjunct to the visual assessment across 15 software methods. Eur. J. Nucl. Med. Mol. Imaging 50, 3276\u20133289 (2023).","journal-title":"Eur. J. Nucl. Med. Mol. Imaging"},{"key":"2570_CR44","doi-asserted-by":"crossref","unstructured":"Kim, J. W., Khan, A. U. & Banerjee, I. Systematic review of hybrid vision transformer architectures for radiological image analysis. J. Imaging Inform. Med. 38, 3248\u20133262 (2025).","DOI":"10.1007\/s10278-024-01322-4"},{"key":"2570_CR45","doi-asserted-by":"crossref","unstructured":"Taleb, A., Kirchler, M., Monti, R. & Lippert, C. Contig: self-supervised multimodal contrastive learning for medical imaging with genetics. In Proc. IEEE\/CVF Conference on Computer Vision and Pattern Recognition, 20908\u201320921 (IEEE, 2022).","DOI":"10.1109\/CVPR52688.2022.02024"},{"key":"2570_CR46","doi-asserted-by":"publisher","first-page":"102656","DOI":"10.1016\/j.media.2022.102656","volume":"83","author":"S Zhang","year":"2023","unstructured":"Zhang, S., Zhang, J., Tian, B., Lukasiewicz, T. & Xu, Z. Multi-modal contrastive mutual learning and pseudo-label re-learning for semi-supervised medical image segmentation. Med. Image Anal. 83, 102656 (2023).","journal-title":"Med. Image Anal."},{"key":"2570_CR47","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-based attenuation correction for whole-body PET \u2014 a multi-tracer study with 18F-FDG, 68 Ga-DOTATATE, and 18F-Fluciclovine. Eur. J. Nucl. Med. Mol. Imaging 49, 3086\u20133097 (2022).","journal-title":"Eur. J. Nucl. Med. Mol. Imaging"},{"key":"2570_CR48","doi-asserted-by":"publisher","first-page":"357","DOI":"10.1016\/S1474-4422(13)70044-9","volume":"12","author":"VL Villemagne","year":"2013","unstructured":"Villemagne, V. L. et al. Amyloid \u03b2 deposition, neurodegeneration, and cognitive decline in sporadic Alzheimer\u2019s disease: a prospective cohort study. Lancet Neurol. 12, 357\u2013367 (2013).","journal-title":"Lancet Neurol."},{"key":"2570_CR49","doi-asserted-by":"publisher","first-page":"1517","DOI":"10.1093\/brain\/awy059","volume":"141","author":"CR Jack Jr.","year":"2018","unstructured":"Jack, C. R. Jr. et al. Longitudinal tau PET in ageing and Alzheimer\u2019s disease. Brain 141, 1517\u20131528 (2018).","journal-title":"Brain"},{"key":"2570_CR50","doi-asserted-by":"publisher","first-page":"27","DOI":"10.1186\/s13195-019-0478-z","volume":"11","author":"G Salvad\u00f3","year":"2019","unstructured":"Salvad\u00f3, G. et al. Centiloid cut-off values for optimal agreement between PET and CSF core AD biomarkers. Alzheimer's Res. Ther. 11, 27 (2019).","journal-title":"Alzheimer's Res. Ther."},{"key":"2570_CR51","doi-asserted-by":"publisher","first-page":"933","DOI":"10.1007\/s10278-025-01528-0","volume":"39","author":"Y Salimi","year":"2025","unstructured":"Salimi, Y., Mansouri, Z., Nkoulou, R., Mainta, I. & Zaidi, H. Deep learning-based CT-less cardiac segmentation of PET images: a robust methodology for multi-tracer nuclear cardiovascular imaging. J. Imaging Inform. Med. 39, 933\u2013947 (2025).","journal-title":"J. Imaging Inform. Med."},{"key":"2570_CR52","doi-asserted-by":"publisher","first-page":"172","DOI":"10.2967\/jnumed.121.262464","volume":"63","author":"F Orlhac","year":"2022","unstructured":"Orlhac, F. et al. A guide to ComBat harmonization of imaging biomarkers in multicenter studies. J. Nucl. Med. 63, 172\u2013179 (2022).","journal-title":"J. Nucl. Med."},{"key":"2570_CR53","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0309540","volume":"19","author":"F Yang","year":"2024","unstructured":"Yang, F. et al. Multicentric study on the reproducibility and robustness of PET-based radiomics features with a realistic activity painting phantom. PLoS ONE 19, e0309540 (2024).","journal-title":"PLoS ONE"},{"key":"2570_CR54","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":"2570_CR55","doi-asserted-by":"publisher","first-page":"206","DOI":"10.1016\/j.neuroimage.2013.08.042","volume":"84","author":"FL Andersen","year":"2014","unstructured":"Andersen, F. L. et al. Combined PET\/MR imaging in neurology: MR-based attenuation correction implies a strong spatial bias when ignoring bone. Neuroimage 84, 206\u2013216 (2014).","journal-title":"Neuroimage"},{"key":"2570_CR56","doi-asserted-by":"publisher","first-page":"192","DOI":"10.1002\/mp.16863","volume":"51","author":"ME Lindemann","year":"2023","unstructured":"Lindemann, M. E. et al. Systematic evaluation of human soft tissue attenuation correction in whole-body PET\/MR: implications from PET\/CT for optimization of MR-based AC in patients with normal lung tissue. Med. Phys. 51, 192\u2013208 (2023).","journal-title":"Med. Phys."},{"key":"2570_CR57","doi-asserted-by":"publisher","first-page":"2165","DOI":"10.1002\/alz.13677","volume":"20","author":"K Chen","year":"2024","unstructured":"Chen, K. et al. Harmonizing florbetapir and PiB PET measurements of cortical A\u03b2 plaque burden using multiple regions-of-interest and machine learning techniques: an alternative to the Centiloid approach. Alzheimer\u2019s Dement. 20, 2165\u20132172 (2024).","journal-title":"Alzheimer\u2019s Dement."},{"key":"2570_CR58","doi-asserted-by":"publisher","first-page":"183","DOI":"10.1111\/j.1365-2796.2004.01388.x","volume":"256","author":"RC Petersen","year":"2004","unstructured":"Petersen, R. C. Mild cognitive impairment as a diagnostic entity. J. Intern. Med. 256, 183\u2013194 (2004).","journal-title":"J. Intern. Med."},{"key":"2570_CR59","doi-asserted-by":"publisher","first-page":"651","DOI":"10.1017\/S0033291700035765","volume":"26","author":"G Lewis","year":"1996","unstructured":"Lewis, G. DSM-IV. Diagnostic and Statistical Manual of Mental Disorders, 4th edn. By the American Psychiatric Association.(Pp. 886;\u00a3 34.95.) APA: Washington, DC. 1994. Psychol. Med. 26, 651\u2013652 (1996).","journal-title":"Psychol. Med."},{"key":"2570_CR60","doi-asserted-by":"publisher","first-page":"263","DOI":"10.1016\/j.jalz.2011.03.005","volume":"7","author":"GM McKhann","year":"2011","unstructured":"McKhann, G. M. et al. The diagnosis of dementia due to Alzheimer\u2019s disease: recommendations from the National Institute on Aging-Alzheimer\u2019s Association workgroups on diagnostic guidelines for Alzheimer\u2019s disease. Alzheimer\u2019s Dement. 7, 263\u2013269 (2011).","journal-title":"Alzheimer\u2019s Dement."},{"key":"2570_CR61","doi-asserted-by":"publisher","first-page":"844","DOI":"10.1016\/j.jalz.2014.01.001","volume":"10","author":"F Jessen","year":"2014","unstructured":"Jessen, F. et al. A conceptual framework for research on subjective cognitive decline in preclinical Alzheimer\u2019s disease. Alzheimer's Dement. 10, 844\u2013852 (2014).","journal-title":"Alzheimer's Dement."},{"key":"2570_CR62","doi-asserted-by":"publisher","first-page":"2160","DOI":"10.1212\/WNL.43.10.2160","volume":"43","author":"GC Rom\u00e1n","year":"1993","unstructured":"Rom\u00e1n, G. C. & Tatemichi, T. K. Vascular dementia. Neurology, 43, 2160\u20132160 (1993).","journal-title":"Neurology"},{"key":"2570_CR63","doi-asserted-by":"publisher","first-page":"2450","DOI":"10.1093\/brain\/awr208","volume":"134","author":"R Vandenberghe","year":"2011","unstructured":"Vandenberghe, R. Sense and sensitivity of novel criteria for frontotemporal dementia. Brain 134, 2450\u20132453 (2011).","journal-title":"Brain"},{"key":"2570_CR64","doi-asserted-by":"publisher","first-page":"88","DOI":"10.1212\/WNL.0000000000004058","volume":"89","author":"IG McKeith","year":"2017","unstructured":"McKeith, I. G. et al. Diagnosis and management of dementia with Lewy bodies: Fourth consensus report of the DLB Consortium. Neurology 89, 88\u2013100 (2017).","journal-title":"Neurology"},{"key":"2570_CR65","doi-asserted-by":"publisher","first-page":"496","DOI":"10.1212\/WNL.0b013e31827f0fd1","volume":"80","author":"MJ Armstrong","year":"2013","unstructured":"Armstrong, M. J. et al. Criteria for the diagnosis of corticobasal degeneration. Neurology 80, 496\u2013503 (2013).","journal-title":"Neurology"},{"key":"2570_CR66","doi-asserted-by":"publisher","first-page":"1591","DOI":"10.1002\/mds.26424","volume":"30","author":"RB Postuma","year":"2015","unstructured":"Postuma, R. B. et al. MDS clinical diagnostic criteria for Parkinson\u2019s disease. Mov. Disord. 30, 1591\u20131601 (2015).","journal-title":"Mov. Disord."},{"key":"2570_CR67","doi-asserted-by":"publisher","first-page":"853","DOI":"10.1002\/mds.26987","volume":"32","author":"GU H\u00f6glinger","year":"2017","unstructured":"H\u00f6glinger, G. U. et al. Clinical diagnosis of progressive supranuclear palsy: the movement disorder society criteria. Mov. Disord. 32, 853\u2013864 (2017).","journal-title":"Mov. Disord."},{"key":"2570_CR68","doi-asserted-by":"publisher","first-page":"670","DOI":"10.1212\/01.wnl.0000324625.00404.15","volume":"71","author":"S Gilman","year":"2008","unstructured":"Gilman, S. et al. Second consensus statement on the diagnosis of multiple system atrophy. Neurology 71, 670\u2013676 (2008).","journal-title":"Neurology"},{"key":"2570_CR69","doi-asserted-by":"publisher","first-page":"845","DOI":"10.1001\/jamaneurol.2024.1612","volume":"81","author":"C Groot","year":"2024","unstructured":"Groot, C. et al. Tau positron emission tomography for predicting dementia in individuals with mild cognitive impairment. JAMA Neurol. 81, 845\u2013856 (2024).","journal-title":"JAMA Neurol."},{"key":"2570_CR70","doi-asserted-by":"publisher","first-page":"e-1","DOI":"10.1016\/j.jalz.2013.01.002","volume":"9","author":"KA Johnson","year":"2013","unstructured":"Johnson, K. A. et al. Appropriate use criteria for amyloid PET: a report of the Amyloid Imaging Task Force, the Society of Nuclear Medicine and Molecular Imaging, and the Alzheimer\u2019s Association. Alzheimer's Dement. 9, e-1\u201316 (2013).","journal-title":"Alzheimer's Dement."},{"key":"2570_CR71","doi-asserted-by":"publisher","first-page":"2123","DOI":"10.1148\/rg.2018180160","volume":"38","author":"TF Lundeen","year":"2018","unstructured":"Lundeen, T. F., Seibyl, J. P., Covington, M. F., Eshghi, N. & Kuo, P. H. Signs and artifacts in amyloid PET. Radiographics 38, 2123\u20132133 (2018).","journal-title":"Radiographics"},{"key":"2570_CR72","doi-asserted-by":"publisher","first-page":"829","DOI":"10.1001\/jamaneurol.2020.0528","volume":"77","author":"AS Fleisher","year":"2020","unstructured":"Fleisher, A. S. et al. Positron emission tomography imaging with [18\u2009F]flortaucipir and postmortem assessment of Alzheimer's disease neuropathologic changes. JAMA Neurol. 77, 829\u2013839 (2020).","journal-title":"JAMA Neurol."},{"key":"2570_CR73","doi-asserted-by":"publisher","first-page":"1395","DOI":"10.1007\/s00259-022-06104-0","volume":"50","author":"F-T Liu","year":"2023","unstructured":"Liu, F.-T. et al. 18F-Florzolotau PET imaging captures the distribution patterns and regional vulnerability of tau pathology in progressive supranuclear palsy. Eur. J. Nucl. Med. Mol. Imaging 50, 1395\u20131405 (2023).","journal-title":"Eur. J. Nucl. Med. Mol. Imaging"},{"key":"2570_CR74","doi-asserted-by":"publisher","first-page":"2314","DOI":"10.1002\/mds.28672","volume":"36","author":"L Li","year":"2021","unstructured":"Li, L. et al. Clinical utility of 18\u2009F-APN-1607 Tau PET imaging in patients with progressive supranuclear palsy. Mov. Disord. 36, 2314\u20132323 (2021).","journal-title":"Mov. Disord."},{"key":"2570_CR75","doi-asserted-by":"publisher","first-page":"150","DOI":"10.1109\/LSP.2023.3245947","volume":"30","author":"T Chen","year":"2023","unstructured":"Chen, T., Li, B. & Zeng, J. Learning traces by yourself: blind image forgery localization via anomaly detection with ViT-VAE. IEEE Signal Process. Lett. 30, 150\u2013154 (2023).","journal-title":"IEEE Signal Process. Lett."},{"key":"2570_CR76","doi-asserted-by":"crossref","unstructured":"Woo, S., Park, J., Lee, J.-Y. & Kweon, I. S. Cbam: convolutional block attention module. In Proc. European Conference on Computer Vision (ECCV), 3\u201319 (Springer, 2018).","DOI":"10.1007\/978-3-030-01234-2_1"},{"key":"2570_CR77","doi-asserted-by":"publisher","first-page":"600","DOI":"10.1109\/TIP.2003.819861","volume":"13","author":"Z Wang","year":"2004","unstructured":"Wang, Z., Bovik, A. C., Sheikh, H. R. & Simoncelli, E. P. Image quality assessment: from error visibility to structural similarity. IEEE Trans. Image Process. 13, 600\u2013612 (2004).","journal-title":"IEEE Trans. Image Process."},{"key":"2570_CR78","doi-asserted-by":"publisher","first-page":"968","DOI":"10.1016\/j.neuroimage.2006.01.021","volume":"31","author":"RS Desikan","year":"2006","unstructured":"Desikan, R. S. et al. An automated labeling system for subdividing the human cerebral cortex on MRI scans into gyral based regions of interest. Neuroimage 31, 968\u2013980 (2006).","journal-title":"Neuroimage"},{"key":"2570_CR79","doi-asserted-by":"publisher","first-page":"341","DOI":"10.1016\/S0896-6273(02)00569-X","volume":"33","author":"B Fischl","year":"2002","unstructured":"Fischl, B. et al. Whole brain segmentation: automated labeling of neuroanatomical structures in the human brain. Neuron 33, 341\u2013355 (2002).","journal-title":"Neuron"},{"key":"2570_CR80","doi-asserted-by":"publisher","first-page":"1421","DOI":"10.1038\/s41593-020-00711-6","volume":"23","author":"Y Tian","year":"2020","unstructured":"Tian, Y., Margulies, D. S., Breakspear, M. & Zalesky, A. Topographic organization of the human subcortex unveiled with functional connectivity gradients. Nat. Neurosci. 23, 1421\u20131432 (2020).","journal-title":"Nat. Neurosci."},{"key":"2570_CR81","doi-asserted-by":"publisher","first-page":"7900","DOI":"10.1073\/pnas.1602413113","volume":"113","author":"A Eklund","year":"2016","unstructured":"Eklund, A., Nichols, T. E. & Knutsson, H. Cluster failure: why fMRI inferences for spatial extent have inflated false-positive rates. Proc. Natl. Acad. Sci. USA 113, 7900\u20137905 (2016).","journal-title":"Proc. Natl. Acad. Sci. USA"},{"key":"2570_CR82","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1002\/hbm.1058","volume":"15","author":"TE Nichols","year":"2002","unstructured":"Nichols, T. E. & Holmes, A. P. Nonparametric permutation tests for functional neuroimaging: a primer with examples. Hum. Brain Mapp. 15, 1\u201325 (2002).","journal-title":"Hum. Brain Mapp."}],"container-title":["npj Digital Medicine"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.nature.com\/articles\/s41746-026-02570-0","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41746-026-02570-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41746-026-02570-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,5,25]],"date-time":"2026-05-25T00:02:56Z","timestamp":1779667376000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.nature.com\/articles\/s41746-026-02570-0"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,3,30]]},"references-count":82,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2026,12]]}},"alternative-id":["2570"],"URL":"https:\/\/doi.org\/10.1038\/s41746-026-02570-0","relation":{},"ISSN":["2398-6352"],"issn-type":[{"value":"2398-6352","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,3,30]]},"assertion":[{"value":"19 November 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"12 March 2026","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"30 March 2026","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"D.G.S. is a consultant and employee of Shanghai United Imaging Intelligence Co., Ltd. The company had no role in designing or performing the study, nor in analyzing or interpreting the data. The other authors declare no competing interests.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"396"}}