{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,4]],"date-time":"2026-10-04T16:52:18Z","timestamp":1791132738616,"version":"4.1.0"},"reference-count":54,"publisher":"Springer Science and Business Media LLC","issue":"9","license":[{"start":{"date-parts":[[2026,8,5]],"date-time":"2026-08-05T00:00:00Z","timestamp":1785888000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2026,8,5]],"date-time":"2026-08-05T00:00:00Z","timestamp":1785888000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/100014718","name":"Innovative Research Group Project of the National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["12175212"],"award-info":[{"award-number":["12175212"]}],"id":[{"id":"10.13039\/100014718","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Med Biol Eng Comput"],"published-print":{"date-parts":[[2026,9]]},"DOI":"10.1007\/s11517-026-03641-w","type":"journal-article","created":{"date-parts":[[2026,8,5]],"date-time":"2026-08-05T09:18:20Z","timestamp":1785921500000},"page":"3413-3425","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Two-stage deep learning framework for the restoration of incomplete-ring PET images"],"prefix":"10.1007","volume":"64","author":[{"ORCID":"https:\/\/orcid.org\/0009-0006-0240-2865","authenticated-orcid":false,"given":"Yeqi","family":"Fang","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Rong","family":"Zhou","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,8,5]]},"reference":[{"issue":"1 Suppl","key":"3641_CR1","first-page":"4S","volume":"45","author":"DW Townsend","year":"2004","unstructured":"Townsend DW, Carney JPJ, Yap JT, Hall NC (2004) PET\/CT today and tomorrow. J Nucl Med 45(1 Suppl):4S-14S","journal-title":"J Nucl Med"},{"issue":"13","key":"3641_CR2","doi-asserted-by":"publisher","first-page":"R117","DOI":"10.1088\/0031-9155\/51\/13\/R08","volume":"51","author":"G Muehllehner","year":"2006","unstructured":"Muehllehner G, Karp JS (2006) Positron emission tomography. Phys Med Biol 51(13):R117","journal-title":"Phys Med Biol"},{"key":"3641_CR3","doi-asserted-by":"crossref","unstructured":"Lameka K, Farwell MD, Ichise M (2016) Positron emission tomography. Handb Clin Neurol 135:209\u2013227","DOI":"10.1016\/B978-0-444-53485-9.00011-8"},{"issue":"1","key":"3641_CR4","doi-asserted-by":"publisher","first-page":"13","DOI":"10.4103\/0971-6203.25665","volume":"31","author":"AK Shukla","year":"2006","unstructured":"Shukla AK, Kumar U (2006) Positron emission tomography: An overview. J Med Phys 31(1):13\u201321","journal-title":"J Med Phys"},{"issue":"1","key":"3641_CR5","doi-asserted-by":"publisher","first-page":"11","DOI":"10.1016\/S1095-0397(00)00051-0","volume":"4","author":"R Nutt","year":"2002","unstructured":"Nutt R (2002) The history of positron emission tomography. Molecular Imaging Biol 4(1):11\u201326","journal-title":"Molecular Imaging Biol"},{"issue":"11","key":"3641_CR6","doi-asserted-by":"publisher","first-page":"2911","DOI":"10.1088\/0031-9155\/53\/11\/010","volume":"53","author":"S Surti","year":"2008","unstructured":"Surti S, Karp JS (2008) Design considerations for a limited-angle, dedicated breast TOF-PET scanner. Phys Med Biol 53(11):2911\u20132921","journal-title":"Phys Med Biol"},{"issue":"14","key":"3641_CR7","doi-asserted-by":"publisher","first-page":"4705","DOI":"10.1088\/0031-9155\/57\/14\/4705","volume":"57","author":"H Tashima","year":"2012","unstructured":"Tashima H, Yamaya T, Yoshida E, Kinouchi S, Watanabe M, Tanaka E (2012) A single-ring OpenPET enabling PET imaging during radiotherapy. Phys Med Biol 57(14):4705\u20134718","journal-title":"Phys Med Biol"},{"issue":"5","key":"3641_CR8","doi-asserted-by":"publisher","first-page":"694","DOI":"10.1109\/TRPMS.2020.3044326","volume":"5","author":"S Krishnamoorthy","year":"2021","unstructured":"Krishnamoorthy S, Teo B-KK, Zou W, McDonough J, Karp JS, Surti S (2021) A proof-of-concept study of an in-situ partial-ring time-of-flight PET scanner for proton beam verification. IEEE Trans Radiat Plasma Med Sci 5(5):694\u2013702","journal-title":"IEEE Trans Radiat Plasma Med Sci"},{"issue":"16","key":"3641_CR9","doi-asserted-by":"publisher","DOI":"10.1088\/1361-6560\/ab8c92","volume":"65","author":"H Zhang","year":"2020","unstructured":"Zhang H, Wang Y, Qi J, Abbaszadeh S (2020) Penalized maximum-likelihood reconstruction for improving limited-angle artifacts in a dedicated head and neck PET system. Phys Med Biol 65(16):165016","journal-title":"Phys Med Biol"},{"key":"3641_CR10","unstructured":"Kak AC, Slaney M (1988) Principles of computerized tomographic imaging. IEEE Press."},{"issue":"15","key":"3641_CR11","doi-asserted-by":"publisher","first-page":"R541","DOI":"10.1088\/0031-9155\/51\/15\/R01","volume":"51","author":"J Qi","year":"2006","unstructured":"Qi J, Leahy RM (2006) Iterative reconstruction techniques in emission computed tomography. Phys Med Biol 51(15):R541\u2013R578","journal-title":"Phys Med Biol"},{"key":"3641_CR12","unstructured":"Makkar S, Ye S, B\u00e9guin M, Dissertori G, Hrbacek J, Lomax A, McNamara K, Ritzer C, Weber DC, Xing L, Winterhalter C (2024) Partial-ring PET image correction using implicit neural representation learning. In XXth International Conference on the use of Computers in Radiation therapy, Lyon, France, July 2024. 8\u201311"},{"key":"3641_CR13","doi-asserted-by":"publisher","DOI":"10.1103\/PhysRevD.110.063011","volume":"110","author":"Y Fang","year":"2024","unstructured":"Fang Y, Hong W, Tao J (Sep2024) Identifying black holes through space telescopes and deep learning. Phys Rev D 110:063011","journal-title":"Phys Rev D"},{"key":"3641_CR14","doi-asserted-by":"crossref","unstructured":"Liu C, Fang Y, Xie Y, Zheng H, Li X, Wu D, Zhang T (2024) Deep learning pneumoconiosis staging and diagnosis system based on multi-stage joint approach. BMC Medical Imaging 24(165)","DOI":"10.1186\/s12880-024-01337-x"},{"issue":"1","key":"3641_CR15","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/TRPMS.2020.3014786","volume":"5","author":"AJ Reader","year":"2020","unstructured":"Reader AJ, Corda G, Mehranian A, da Costa-Luis C, Ellis S, Schnabel JA (2020) Deep learning for pet image reconstruction. IEEE Trans Radiat Plasma Med Sci 5(1):1\u201325","journal-title":"IEEE Trans Radiat Plasma Med Sci"},{"issue":"1","key":"3641_CR16","doi-asserted-by":"publisher","first-page":"44","DOI":"10.1109\/TRPMS.2020.3033172","volume":"5","author":"VSS Kandarpa","year":"2021","unstructured":"Kandarpa VSS, Bousse A, Benoit D, Visvikis D (2021) Dug-recon: a framework for direct image reconstruction using convolutional generative networks. IEEE Trans Radiat Plasma Med Sci 5(1):44\u201353","journal-title":"IEEE Trans Radiat Plasma Med Sci"},{"issue":"22","key":"3641_CR17","doi-asserted-by":"publisher","DOI":"10.1088\/1361-6560\/ab4aa9","volume":"64","author":"C-C Liu","year":"2019","unstructured":"Liu C-C, Huang H-M (2019) Partial-ring PET image restoration using a deep learning based method. Phys Med Biol 64(22):225014","journal-title":"Phys Med Biol"},{"key":"3641_CR18","doi-asserted-by":"crossref","unstructured":"Xue Y, Peng Y, Bi L, Feng D, Kim J (2023) Cg-3dsrgan: a classification guided 3d generative adversarial network for image quality recovery from low-dose pet images","DOI":"10.1109\/EMBC40787.2023.10341112"},{"issue":"1150","key":"3641_CR19","doi-asserted-by":"publisher","first-page":"20230292","DOI":"10.1259\/bjr.20230292","volume":"96","author":"AJ Reader","year":"2023","unstructured":"Reader AJ, Pan B (2023) Artificial intelligence for PET image reconstruction. Br J Radiol 96(1150):20230292","journal-title":"Br J Radiol"},{"key":"3641_CR20","doi-asserted-by":"publisher","first-page":"1466498","DOI":"10.3389\/fradi.2024.1466498","volume":"4","author":"R Vashistha","year":"2024","unstructured":"Vashistha R, Vegh V, Moradi H, Hammond A, O\u2019Brien K, Reutens D (2024) Modular GAN: PET image reconstruction using two generative adversarial networks. Front Radiol 4:1466498","journal-title":"Front Radiol"},{"key":"3641_CR21","unstructured":"Lahiri A, Maleki A, Khalil MM (2019) Lose the views: limited angle CT reconstruction via implicit sinogram completion. arXiv:1711.10388 arXiv preprint"},{"issue":"2","key":"3641_CR22","doi-asserted-by":"publisher","first-page":"1","DOI":"10.3934\/ammc.2023006","volume":"1","author":"T Germer","year":"2023","unstructured":"Germer T, Uelwer T, Conrad S, Harmeling S (2023) Limited-angle tomography reconstruction via deep end-to-end learning on synthetic data. Appl Math Modern Challenges 1(2):1\u201323","journal-title":"Appl Math Modern Challenges"},{"issue":"10","key":"3641_CR23","volume":"14","author":"J Zhao","year":"2019","unstructured":"Zhao J, Chen Z, Zhang L, Jin X (2019) Promising generative adversarial network based sinogram inpainting method for ultra-limited-angle computed tomography imaging. PLoS ONE 14(10):e0224426","journal-title":"PLoS ONE"},{"key":"3641_CR24","doi-asserted-by":"crossref","unstructured":"Wang S (2024) Knowledge-driven deep learning for fast MR imaging. Magn Reson Med","DOI":"10.1002\/mrm.30105"},{"issue":"22","key":"3641_CR25","doi-asserted-by":"publisher","first-page":"225014","DOI":"10.1088\/1361-6560\/ab4aa9","volume":"64","author":"C-C Liu","year":"2019","unstructured":"Liu C-C, Huang H-M (2019) Partial-ring PET image restoration using a deep learning based method. Phys Med Biol 64(22):225014","journal-title":"Phys Med Biol"},{"issue":"4","key":"3641_CR26","doi-asserted-by":"publisher","first-page":"416","DOI":"10.1109\/TRPMS.2023.3280674","volume":"8","author":"Q Shan","year":"2024","unstructured":"Shan Q, Wang J, Liu D (2024) Deep image prior-based PET reconstruction from partial data. IEEE Trans Radiat Plasma Med Sci 8(4):416\u2013425","journal-title":"IEEE Trans Radiat Plasma Med Sci"},{"key":"3641_CR27","unstructured":"Makkar S, Ye S, B\u00e9guin M, Dissertori G, Hrbacek J, Lomax A, McNamara K, Ritzer C, Weber DC, Xing L, Winterhalter C (2024) Partial-ring PET image correction using implicit neural representation learning. In XXth International conference on the use of computers in radiation therapy (ICCR), Lyon, France"},{"key":"3641_CR28","doi-asserted-by":"crossref","unstructured":"Shi Z, Zheng H, Xu C, Dong C, Pan B, Xie X, He A, Li T, Fu H (2024) Resfusion: Denoising diffusion probabilistic models for image restoration based on prior residual noise. Advances in Neural Information Processing Systems","DOI":"10.52202\/079017-4153"},{"key":"3641_CR29","doi-asserted-by":"publisher","first-page":"40","DOI":"10.1016\/j.nima.2016.01.081","volume":"816","author":"Z Wang","year":"2016","unstructured":"Wang Z, Sun X, Lou K, Meier J, Zhou R, Yang C, Zhu X, Shao Y (2016) Design, development and evaluation of a resistor-based multiplexing circuit for a 20$$\\times $$20 SiPM array. Nucl Instrum Methods Phys Res, Sect A 816:40\u201346","journal-title":"Nucl Instrum Methods Phys Res, Sect A"},{"issue":"1","key":"3641_CR30","first-page":"1","volume":"37","author":"C Dong","year":"2017","unstructured":"Dong C, Ma M, Zhou R, Yang C (2017) Energy resolution and time resolution of an SiPM-coupled LYSO crystal detector. Nuclear Electron Detect Technol 37(1):1\u20133","journal-title":"Nuclear Electron Detect Technol"},{"key":"3641_CR31","doi-asserted-by":"publisher","DOI":"10.1016\/j.nima.2021.165340","volume":"1003","author":"C Zhao","year":"2021","unstructured":"Zhao C, Zhu B, Zhao M, Chen Q, Wang Z, Zhou R, Yang C (2021) Development of a modular high-sensitivity high-uniformity gamma camera for radiation monitoring applications. Nucl Instrum Methods Phys Res, Sect A 1003:165340","journal-title":"Nucl Instrum Methods Phys Res, Sect A"},{"issue":"3","key":"3641_CR32","doi-asserted-by":"publisher","first-page":"375","DOI":"10.1007\/s41605-022-00333-5","volume":"6","author":"H Zhang","year":"2022","unstructured":"Zhang H, Zhou R, Yao R, Chaowen Yang SX, Shu Y, Jiang B, Liu L (2022) A study on a nonlinear least-squares fitting method for 3d positioning of gamma rays based on monolithic crystal and SiPM array. Radiat Detect Technol Methods 6(3):375\u2013390","journal-title":"Radiat Detect Technol Methods"},{"issue":"supplement 1","key":"3641_CR33","first-page":"157P","volume":"49","author":"R Harrison","year":"2008","unstructured":"Harrison R, Gillispie S, Lewellen T (2008) Simulation system for emission tomography (simset): using simulation to research ideas in emission tomography (pet and spect). J Nucl Med 49(supplement 1):157P-157P","journal-title":"J Nucl Med"},{"issue":"5","key":"3641_CR34","doi-asserted-by":"publisher","first-page":"561","DOI":"10.1016\/j.jalz.2016.10.006","volume":"13","author":"MW Weiner","year":"2017","unstructured":"Weiner MW, Veitch DP, Aisen PS, Beckett LA, Cairns NJ, Green RC, Harvey D, Jack CR, Jagust W, Morris JC et al (2017) The Alzheimer\u2019s Disease Neuroimaging Initiative 3: continued innovation for clinical trial improvement. Alzheimer\u2019s & Dementia 13(5):561\u2013571","journal-title":"Alzheimer\u2019s & Dementia"},{"key":"3641_CR35","doi-asserted-by":"crossref","unstructured":"Ronneberger O, Fischer P, Brox T (2015) U-net: Convolutional networks for biomedical image segmentation","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"3641_CR36","unstructured":"Oktay O, Schlemper J, Le Folgoc L, Lee M, Heinrich M, Misawa K, Mori K, McDonagh S, Hammerla NY, Kainz B, Glocker B, Rueckert D (2018) Attention u-net: Learning where to look for the pancreas"},{"key":"3641_CR37","doi-asserted-by":"crossref","unstructured":"Hatamizadeh A, Tang Y, Nath V, Yang D, Myronenko A, Landman B, Roth H, Xu D (2021) Unetr: Transformers for 3d medical image segmentation","DOI":"10.1109\/WACV51458.2022.00181"},{"key":"3641_CR38","unstructured":"Chen J, Lu Y, Yu Q, Luo X, Adeli E, Wang Y, Lu L, Yuille AL, Zhou Y (2021) Transformers make strong encoders for medical image segmentation, Transunet"},{"key":"3641_CR39","doi-asserted-by":"crossref","unstructured":"Hang T, Gu S, Li C, Bao J, Chen D, Hu H, Geng X, Guo B (2023) Efficient diffusion training via min-snr weighting strategy. In: Proceedings of the IEEE\/CVF international conference on computer vision (ICCV), pp 7441\u20137451","DOI":"10.1109\/ICCV51070.2023.00684"},{"key":"3641_CR40","doi-asserted-by":"publisher","first-page":"102020","DOI":"10.1016\/j.softx.2024.102020","volume":"29","author":"LA Polson","year":"2025","unstructured":"Polson LA, Fedrigo R, Li C, Sabouri M, Dzikunu O, Ahamed S, Karakatsanis N, Kurkowska S, Sheikhzadeh P, Esquinas P, Rahmim A, Uribe C (2025) Pytomography: a python library for medical image reconstruction. SoftwareX 29:102020","journal-title":"SoftwareX"},{"key":"3641_CR41","doi-asserted-by":"crossref","unstructured":"Student (1908) The probable error of a mean. Biometrika 6(1):1\u201325","DOI":"10.2307\/2331554"},{"issue":"2","key":"3641_CR42","first-page":"65","volume":"6","author":"S Holm","year":"1979","unstructured":"Holm S (1979) A simple sequentially rejective multiple test procedure. Scand J Stat 6(2):65\u201370","journal-title":"Scand J Stat"},{"key":"3641_CR43","doi-asserted-by":"crossref","unstructured":"Meltzer CC, Zubieta J-K, Brandt J, Tune LE, Mayberg HS, Frost JJ (1996) Regional hypometabolism in alzheimer\u2019s disease as measured by positron emission tomography after correction for effects of partial volume averaging. Neurology 47(2):454\u2013461","DOI":"10.1212\/WNL.47.2.454"},{"issue":"1","key":"3641_CR44","doi-asserted-by":"publisher","first-page":"39","DOI":"10.1016\/S0925-4927(01)00130-5","volume":"114","author":"ND Volkow","year":"2002","unstructured":"Volkow ND, Zhu W, Felder CA, Mueller K, Welsh TF, Wang G-J, de Leon MJ (2002) Changes in brain functional homogeneity in subjects with alzheimer\u2019s disease. Psychiat Res Neuroimaging 114(1):39\u201350","journal-title":"Psychiat Res Neuroimaging"},{"issue":"12","key":"3641_CR45","doi-asserted-by":"publisher","first-page":"3643","DOI":"10.1177\/0271678X17732508","volume":"37","author":"SK Meles","year":"2017","unstructured":"Meles SK, Pagani M, Arnaldi D, De Carli F, Dessi B, Morbelli S, Sambuceti G, Jonsson C, Leenders KL, Nobili F (2017) The Alzheimer\u2019s disease metabolic brain pattern in mild cognitive impairment. J Cerebral Blood Flow Metabol 37(12):3643\u20133648","journal-title":"J Cerebral Blood Flow Metabol"},{"key":"3641_CR46","doi-asserted-by":"crossref","unstructured":"Poon JK, Dahlbom ML, Casey ME, Qi J, Cherry SR, Badawi RD (2015) Validation of the SimSET simulation package for modeling the Siemens Biograph mCT PET scanner. Med Phys 42(5):2791\u20132791","DOI":"10.1088\/0031-9155\/60\/3\/N35"},{"key":"3641_CR47","doi-asserted-by":"publisher","first-page":"1324562","DOI":"10.3389\/fnume.2023.1324562","volume":"3","author":"G Schramm","year":"2023","unstructured":"Schramm G, Thielemans K (2023) PARALLELPROJ\u2013an open-source framework for fast calculation of projections in tomography. Front Nuclear Med 3:1324562","journal-title":"Front Nuclear Med"},{"key":"3641_CR48","doi-asserted-by":"publisher","first-page":"253","DOI":"10.1016\/j.media.2019.03.013","volume":"54","author":"I H\u00e4ggstr\u00f6m","year":"2019","unstructured":"H\u00e4ggstr\u00f6m I, Schmidtlein CR, Campanella G, Fuchs TJ (2019) Deeppet: a deep encoder-decoder network for direct pet image reconstruction. Med Image Anal 54:253\u2013262","journal-title":"Med Image Anal"},{"key":"3641_CR49","volume-title":"Python 3 Reference Manual","author":"G Van Rossum","year":"2009","unstructured":"Van Rossum G, Drake FL (2009) Python 3 Reference Manual. CreateSpace, Scotts Valley, CA"},{"key":"3641_CR50","unstructured":"Itseez (2015) Open source computer vision library. https:\/\/github.com\/itseez\/opencv"},{"key":"3641_CR51","doi-asserted-by":"crossref","unstructured":"Virtanen P, Gommers R, Oliphant TE, Haberland M, Reddy T, Cournapeau D, Burovski E, Peterson P, Weckesser W, Bright J, van der Walt SJ, Brett M, Wilson J, Jarrod Millman K, Mayorov N, Nelson ARJ, Jones E, Kern R, Larson E, Carey CJ, Polat \u0130, Feng Y, Moore EW, VanderPlas J, Laxalde D, Perktold J, Cimrman R, Henriksen I, Quintero EA, Harris CR, Archibald AM, Ribeiro AH, Pedregosa F, van Mulbregt P, SciPy 1.0 Contributors (2020) SciPy 1.0: fundamental algorithms for scientific computing in python. Nature Methods 17:261\u2013272.","DOI":"10.1038\/s41592-020-0772-5"},{"key":"3641_CR52","unstructured":"Paszke A, Gross S, Massa F, Lerer A, Bradbury J, Chanan G, Killeen T, Lin Z, Gimelshein N, Antiga L, Desmaison A, Kopf A, Yang E, DeVito Z, Raison M, Tejani A, Chilamkurthy S, Steiner B, Fang L, Bai J, Chintala S (2019) Pytorch: an imperative style, high-performance deep learning library. In Advances in Neural Information Processing Systems 32. Curran Associates, Inc, pp 8024\u20138035"},{"issue":"3","key":"3641_CR53","doi-asserted-by":"publisher","first-page":"90","DOI":"10.1109\/MCSE.2007.55","volume":"9","author":"JD Hunter","year":"2007","unstructured":"Hunter JD (2007) Matplotlib: A 2d graphics environment. Comput Sci Eng 9(3):90\u201395","journal-title":"Comput Sci Eng"},{"issue":"60","key":"3641_CR54","doi-asserted-by":"publisher","first-page":"3021","DOI":"10.21105\/joss.03021","volume":"6","author":"ML Waskom","year":"2021","unstructured":"Waskom ML (2021) seaborn: statistical data visualization. J Open Source Softw 6(60):3021","journal-title":"J Open Source Softw"}],"container-title":["Medical &amp; Biological Engineering &amp; Computing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11517-026-03641-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11517-026-03641-w","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11517-026-03641-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,10,4]],"date-time":"2026-10-04T16:03:09Z","timestamp":1791129789000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11517-026-03641-w"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,8,5]]},"references-count":54,"journal-issue":{"issue":"9","published-print":{"date-parts":[[2026,9]]}},"alternative-id":["3641"],"URL":"https:\/\/doi.org\/10.1007\/s11517-026-03641-w","relation":{},"ISSN":["0140-0118","1741-0444"],"issn-type":[{"value":"0140-0118","type":"print"},{"value":"1741-0444","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,8,5]]},"assertion":[{"value":"8 August 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"18 July 2026","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"5 August 2026","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"The authors declare that they have no conflict of interest.","order":1,"name":"Ethics","label":"Conflict of Interest","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"Brain PET images used as phantom references in this study were obtained from the Alzheimer\u2019s Disease Neuroimaging Initiative (ADNI) database (adni.loni.usc.edu). The ADNI study was conducted in accordance with the Good Clinical Practice guidelines and applicable federal regulations. Written informed consent was obtained from all ADNI participants or their authorised representatives at each participating site.","order":2,"name":"Ethics","label":"Ethics statement","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare that they have no competing interests.","order":3,"name":"Ethics","label":"Competing interests","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"Not applicable.","order":4,"name":"Ethics","label":"Consent for publication","group":{"name":"EthicsHeading","label":"Declarations"}}]}}