{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T17:23:11Z","timestamp":1782926591824,"version":"3.54.5"},"reference-count":23,"publisher":"Springer Science and Business Media LLC","issue":"9","license":[{"start":{"date-parts":[[2022,3,8]],"date-time":"2022-03-08T00:00:00Z","timestamp":1646697600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2022,3,8]],"date-time":"2022-03-08T00:00:00Z","timestamp":1646697600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Eur J Nucl Med Mol Imaging"],"published-print":{"date-parts":[[2022,7]]},"DOI":"10.1007\/s00259-022-05752-6","type":"journal-article","created":{"date-parts":[[2022,3,8]],"date-time":"2022-03-08T10:02:51Z","timestamp":1646733771000},"page":"3073-3085","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":33,"title":["Unified spatial normalization method of brain PET images using adaptive probabilistic brain atlas"],"prefix":"10.1007","volume":"49","author":[{"given":"Tianhao","family":"Zhang","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Binbin","family":"Nie","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hua","family":"Liu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7417-5063","authenticated-orcid":false,"given":"Baoci","family":"Shan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"name":"Alzheimer\u2019s Disease Neuroimaging Initiative","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2022,3,8]]},"reference":[{"key":"5752_CR1","doi-asserted-by":"crossref","unstructured":"Hooker JM, Carson RE. Human positron emission tomography neuroimaging. In: Yamush ML, editor. Annual Review of Biomedical Engineering, Vol 21; 2019. p. 551\u201381.","DOI":"10.1146\/annurev-bioeng-062117-121056"},{"key":"5752_CR2","doi-asserted-by":"publisher","first-page":"363","DOI":"10.1016\/j.neuroimage.2011.12.037","volume":"61","author":"L Zimmer","year":"2012","unstructured":"Zimmer L, Luxen A. PET radiotracers for molecular imaging in the brain: past, present and future. Neuroimage. 2012;61:363\u201370. https:\/\/doi.org\/10.1016\/j.neuroimage.2011.12.037.","journal-title":"Neuroimage"},{"key":"5752_CR3","doi-asserted-by":"publisher","first-page":"534","DOI":"10.1016\/j.neucom.2020.08.085","volume":"452","author":"S Gupta","year":"2021","unstructured":"Gupta S, Gupta P, Verma VS. Study on anatomical and functional medical image registration methods. Neurocomputing. 2021;452:534\u201348. https:\/\/doi.org\/10.1016\/j.neucom.2020.08.085.","journal-title":"Neurocomputing"},{"key":"5752_CR4","doi-asserted-by":"publisher","first-page":"S159","DOI":"10.1016\/j.neurobiolaging.2014.04.033","volume":"36","author":"P Bourgeat","year":"2015","unstructured":"Bourgeat P, Villemagne VL, Dore V, Brown B, Macaulay SL, Martins R, et al. Comparison of MR-less PiB SUVR quantification methods. Neurobiol Aging. 2015;36:S159\u201366. https:\/\/doi.org\/10.1016\/j.neurobiolaging.2014.04.033.","journal-title":"Neurobiol Aging"},{"key":"5752_CR5","doi-asserted-by":"publisher","first-page":"545","DOI":"10.1006\/nimg.1999.0431","volume":"9","author":"JH Meyer","year":"1999","unstructured":"Meyer JH, Gunn RN, Myers R, Grasby PM. Assessment of spatial normalization of PET ligand images using ligand-specific templates. Neuroimage. 1999;9:545\u201353. https:\/\/doi.org\/10.1006\/nimg.1999.0431.","journal-title":"Neuroimage"},{"key":"5752_CR6","doi-asserted-by":"publisher","first-page":"345","DOI":"10.1007\/s11682-018-9888-y","volume":"13","author":"PJ Lao","year":"2019","unstructured":"Lao PJ, Handen BL, Betthauser TJ, Cody KA, Cohen AD, Tudorascu DL, et al. Imaging neurodegeneration in Down syndrome: brain templates for amyloid burden and tissue segmentation. Brain Imaging Behav. 2019;13:345\u201353. https:\/\/doi.org\/10.1007\/s11682-018-9888-y.","journal-title":"Brain Imaging Behav"},{"key":"5752_CR7","doi-asserted-by":"publisher","first-page":"8","DOI":"10.1016\/j.jneumeth.2019.108438","volume":"328","author":"X Sun","year":"2019","unstructured":"Sun X, Liang SX, Fu LP, Zhang XJ, Feng T, Li PL, et al. A human brain tau PET template in MNI space for the voxel-wise analysis of Alzheimer\u2019s disease. J Neurosci Methods. 2019;328:8. https:\/\/doi.org\/10.1016\/j.jneumeth.2019.108438.","journal-title":"J Neurosci Methods"},{"key":"5752_CR8","doi-asserted-by":"publisher","first-page":"924","DOI":"10.1097\/RCT.0000000000000123","volume":"38","author":"SY Chae","year":"2014","unstructured":"Chae SY, Kim HO, Oh M, Lee DY, Jin S, Oh SJ, et al. Evaluation of selective positron emission tomography template method for spatial normalization of amyloid imaging with C-11-Pittsburgh compound B. J Comput Assist Tomogr. 2014;38:924\u20139.","journal-title":"J Comput Assist Tomogr"},{"key":"5752_CR9","doi-asserted-by":"publisher","first-page":"600","DOI":"10.1007\/s12149-013-0723-7","volume":"27","author":"ME Martino","year":"2013","unstructured":"Martino ME, de Villoria JG, Lacalle-Aurioles M, Olazaran J, Cruz I, Navarro E, et al. Comparison of different methods of spatial normalization of FDG-PET brain images in the voxel-wise analysis of MCI patients and controls. Ann Nucl Med. 2013;27:600\u20139. https:\/\/doi.org\/10.1007\/s12149-013-0723-7.","journal-title":"Ann Nucl Med"},{"key":"5752_CR10","doi-asserted-by":"publisher","first-page":"839","DOI":"10.1016\/j.neuroimage.2005.02.018","volume":"26","author":"J Ashburner","year":"2005","unstructured":"Ashburner J, Friston KJ. Unified segmentation. Neuroimage. 2005;26:839\u201351. https:\/\/doi.org\/10.1016\/j.neuroimage.2005.02.018.","journal-title":"Neuroimage"},{"key":"5752_CR11","doi-asserted-by":"publisher","first-page":"1293","DOI":"10.1098\/rstb.2001.0915","volume":"356","author":"J Mazziotta","year":"2001","unstructured":"Mazziotta J, Toga A, Evans A, Fox P, Lancaster J, Zilles K, et al. A probabilistic atlas and reference system for the human brain: International Consortium for Brain Mapping (ICBM). Philos Trans R Soc B Biol Sci. 2001;356:1293\u2013322. https:\/\/doi.org\/10.1098\/rstb.2001.0915.","journal-title":"Philos Trans R Soc B Biol Sci"},{"key":"5752_CR12","doi-asserted-by":"publisher","first-page":"988-+","DOI":"10.1126\/science.abb4588","volume":"369","author":"K Amunts","year":"2020","unstructured":"Amunts K, Mohlberg H, Bludau S, Zilles K. Julich-Brain: a 3D probabilistic atlas of the human brain\u2019s cytoarchitecture. Science. 2020;369:988-+. https:\/\/doi.org\/10.1126\/science.abb4588.","journal-title":"Science."},{"key":"5752_CR13","doi-asserted-by":"publisher","first-page":"205","DOI":"10.1016\/j.jalz.2016.08.005","volume":"13","author":"CR Jack","year":"2017","unstructured":"Jack CR, Wiste HJ, Weigand SD, Therneau TM, Lowe VJ, Knopman DS, et al. Defining imaging biomarker cut points for brain aging and Alzheimer\u2019s disease. Alzheimers Dementia. 2017;13:205\u201316. https:\/\/doi.org\/10.1016\/j.jalz.2016.08.005.","journal-title":"Alzheimers Dementia"},{"key":"5752_CR14","doi-asserted-by":"publisher","first-page":"1207","DOI":"10.1016\/j.neurobiolaging.2009.07.002","volume":"32","author":"SM Landau","year":"2011","unstructured":"Landau SM, Harvey D, Madison CM, Koeppe RA, Reiman EM, Foster NL, et al. Associations between cognitive, functional, and FDG-PET measures of decline in AD and MCI. Neurobiol Aging. 2011;32:1207\u201318. https:\/\/doi.org\/10.1016\/j.neurobiolaging.2009.07.002.","journal-title":"Neurobiol Aging"},{"key":"5752_CR15","doi-asserted-by":"publisher","first-page":"684","DOI":"10.1148\/rg.343135065","volume":"34","author":"RKJ Brown","year":"2014","unstructured":"Brown RKJ, Bohnen NI, Wong KK, Minoshima S, Frey KA. Brain PET in suspected dementia: patterns of altered FDG metabolism. Radiographics. 2014;34:684\u2013701. https:\/\/doi.org\/10.1148\/rg.343135065.","journal-title":"Radiographics"},{"key":"5752_CR16","doi-asserted-by":"publisher","unstructured":"Gao R, Zhang GJ, Chen XQ, Yang AM, Smith G, Wong DF, et al. CSF biomarkers and its associations with F-18-AV133 cerebral VMAT2 binding in Parkinson\u2019s disease-a preliminary report. Plos One. 2016;11. https:\/\/doi.org\/10.1371\/journal.pone.0164762.","DOI":"10.1371\/journal.pone.0164762"},{"key":"5752_CR17","doi-asserted-by":"publisher","first-page":"70","DOI":"10.2967\/jnumed.112.109009","volume":"54","author":"SM Landau","year":"2013","unstructured":"Landau SM, Breault C, Joshi AD, Pontecorvo M, Mathis CA, Jagust WJ, et al. Amyloid-beta imaging with Pittsburgh compound B and florbetapir: comparing radiotracers and quantification methods. J Nucl Med. 2013;54:70\u20137. https:\/\/doi.org\/10.2967\/jnumed.112.109009.","journal-title":"J Nucl Med"},{"key":"5752_CR18","doi-asserted-by":"publisher","first-page":"339","DOI":"10.1007\/s12021-016-9299-4","volume":"14","author":"CG Yan","year":"2016","unstructured":"Yan CG, Wang XD, Zuo XN, Zang YF. DPABI: data processing & analysis for (resting-state) brain imaging. Neuroinformatics. 2016;14:339\u201351. https:\/\/doi.org\/10.1007\/s12021-016-9299-4.","journal-title":"Neuroinformatics"},{"key":"5752_CR19","doi-asserted-by":"crossref","unstructured":"Fripp J, Bourgeat P, Raniga P, Acosta O, Villemagne V, Jones G, et al. MR-less high dimensional spatial normalization of C-11 PiB PET images on a population of elderly, mild cognitive impaired and Alzheimer disease patients. 11th International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI2008). New York, NY; 2008. p. 442-+.","DOI":"10.1007\/978-3-540-85988-8_53"},{"key":"5752_CR20","doi-asserted-by":"publisher","first-page":"1472","DOI":"10.2967\/jnumed.112.115006","volume":"54","author":"R Lundqvist","year":"2013","unstructured":"Lundqvist R, Lilja J, Thomas BA, Lotjonen J, Villemagne VL, Rowe CC, et al. Implementation and validation of an adaptive template registration method for F-18-flutemetamol imaging data. J Nucl Med. 2013;54:1472\u20138. https:\/\/doi.org\/10.2967\/jnumed.112.115006.","journal-title":"J Nucl Med"},{"key":"5752_CR21","doi-asserted-by":"publisher","first-page":"285","DOI":"10.2967\/jnumed.118.207811","volume":"60","author":"J Lilja","year":"2019","unstructured":"Lilja J, Leuzy A, Chiotis K, Savitcheva I, Sorensen J, Nordberg A. Spatial normalization of F-18-flutemetamol PET images using an adaptive principal-component template. J Nucl Med. 2019;60:285\u201391. https:\/\/doi.org\/10.2967\/jnumed.118.207811.","journal-title":"J Nucl Med"},{"key":"5752_CR22","doi-asserted-by":"crossref","unstructured":"Alven J, Heurling K, Smith R, Strandberg O, Scholl M, Hansson O, et al. A deep learning approach to MR-less spatial normalization for tau PET images. 10th International Workshop on Machine Learning in Medical Imaging (MLMI) \/ 22nd International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI). Shenzhen, PEOPLES R CHINA: Springer International Publishing Ag; 2019. p. 355\u201363.","DOI":"10.1007\/978-3-030-32245-8_40"},{"key":"5752_CR23","doi-asserted-by":"publisher","first-page":"3769","DOI":"10.1002\/hbm.24210","volume":"39","author":"SK Kang","year":"2018","unstructured":"Kang SK, Seo S, Shin SA, Byun MS, Lee DY, Kim YK, et al. Adaptive template generation for amyloid PET using a deep learning approach. Hum Brain Mapp. 2018;39:3769\u201378. https:\/\/doi.org\/10.1002\/hbm.24210.","journal-title":"Hum Brain Mapp"}],"container-title":["European Journal of Nuclear Medicine and Molecular Imaging"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00259-022-05752-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00259-022-05752-6\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00259-022-05752-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,7,2]],"date-time":"2022-07-02T05:07:32Z","timestamp":1656738452000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00259-022-05752-6"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,3,8]]},"references-count":23,"journal-issue":{"issue":"9","published-print":{"date-parts":[[2022,7]]}},"alternative-id":["5752"],"URL":"https:\/\/doi.org\/10.1007\/s00259-022-05752-6","relation":{},"ISSN":["1619-7070","1619-7089"],"issn-type":[{"value":"1619-7070","type":"print"},{"value":"1619-7089","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,3,8]]},"assertion":[{"value":"4 November 2021","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"1 March 2022","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"8 March 2022","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare no competing interests.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}]}}