{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,25]],"date-time":"2026-09-25T18:01:21Z","timestamp":1790359281228,"version":"4.1.0"},"reference-count":77,"publisher":"Elsevier BV","issue":"7","license":[{"start":{"date-parts":[[2025,7,1]],"date-time":"2025-07-01T00:00:00Z","timestamp":1751328000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2025,7,1]],"date-time":"2025-07-01T00:00:00Z","timestamp":1751328000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2025,6,2]],"date-time":"2025-06-02T00:00:00Z","timestamp":1748822400000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100003710","name":"Korea Health Industry Development Institute","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100003710","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003725","name":"National Research Foundation of Korea","doi-asserted-by":"publisher","award":["RS-2023-00262527"],"award-info":[{"award-number":["RS-2023-00262527"]}],"id":[{"id":"10.13039\/501100003725","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003625","name":"Ministry of Health and Welfare","doi-asserted-by":"publisher","award":["RS-2023-00265820"],"award-info":[{"award-number":["RS-2023-00265820"]}],"id":[{"id":"10.13039\/501100003625","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003625","name":"Ministry of Health and Welfare","doi-asserted-by":"publisher","award":["HR20C0026"],"award-info":[{"award-number":["HR20C0026"]}],"id":[{"id":"10.13039\/501100003625","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["cell.com","elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Cell Reports Medicine"],"published-print":{"date-parts":[[2025,7]]},"DOI":"10.1016\/j.xcrm.2025.102207","type":"journal-article","created":{"date-parts":[[2025,6,27]],"date-time":"2025-06-27T10:46:06Z","timestamp":1751021166000},"page":"102207","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":3,"title":["Enhancing 3D dopamine transporter imaging as a biomarker for Parkinson\u2019s disease via self-supervised learning with diffusion models"],"prefix":"10.1016","volume":"6","author":[{"given":"Jongjun","family":"Won","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0007-5600-0834","authenticated-orcid":false,"given":"Grace Yoojin","family":"Lee","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sungyang","family":"Jo","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jihyun","family":"Lee","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sangjin","family":"Lee","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jae Seung","family":"Kim","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Changhwan","family":"Sung","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jungsu S.","family":"Oh","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kyum-Yil","family":"Kwon","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Soo Bin","family":"Park","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Joonsang","family":"Lee","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jieun","family":"Yum","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sun Ju","family":"Chung","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3438-2217","authenticated-orcid":false,"given":"Namkug","family":"Kim","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"key":"10.1016\/j.xcrm.2025.102207_bib1","doi-asserted-by":"crossref","first-page":"283","DOI":"10.1016\/S0140-6736(23)01419-8","article-title":"The epidemiology of Parkinson's disease","volume":"403","author":"Ben-Shlomo","year":"2024","journal-title":"Lancet"},{"key":"10.1016\/j.xcrm.2025.102207_bib2","unstructured":"Food and Drug Administration. FDA DaT Scan Approval Letter. (2011). https:\/\/www.accessdata.fda.gov\/drugsatfda_docs\/label\/2011\/022454Orig1s000Lbl.pdf."},{"key":"10.1016\/j.xcrm.2025.102207_bib3","unstructured":"Summary of the European Public Assessment Report for DaTSCAN. (2011). ema.europa.eu\/en\/medicines\/human\/EPAR\/datscan#product-information-section."},{"key":"10.1016\/j.xcrm.2025.102207_bib4","doi-asserted-by":"crossref","DOI":"10.1212\/WNL.0000000000209453","article-title":"Neuropathologic Validation and Diagnostic Accuracy of Presynaptic Dopaminergic Imaging in the Diagnosis of Parkinsonism","volume":"102","author":"Hastings","year":"2024","journal-title":"Neurology"},{"key":"10.1016\/j.xcrm.2025.102207_bib5","doi-asserted-by":"crossref","first-page":"43","DOI":"10.1038\/s41531-021-00185-8","article-title":"Clinical utility of DaTscan in patients with suspected Parkinsonian syndrome: a systematic review and meta-analysis","volume":"7","author":"Bega","year":"2021","journal-title":"NPJ Parkinsons Dis."},{"key":"10.1016\/j.xcrm.2025.102207_bib6","doi-asserted-by":"crossref","first-page":"454","DOI":"10.1007\/s00259-008-0989-5","article-title":"Direct comparison of FP-CIT SPECT and F-DOPA PET in patients with Parkinson's disease and healthy controls","volume":"36","author":"Eshuis","year":"2009","journal-title":"Eur. J. Nucl. Med. Mol. Imaging"},{"key":"10.1016\/j.xcrm.2025.102207_bib7","article-title":"Head-to-head comparison of (18) F-FP-CIT and (123) I-FP-CIT for dopamine transporter imaging in patients with Parkinson's disease: A preliminary study","volume":"72","author":"Lee","year":"2018","journal-title":"Synapse (N. Y.)"},{"key":"10.1016\/j.xcrm.2025.102207_bib8","first-page":"999","article-title":"Crossover study of 99mTc-TRODAT-1 SPECT and 18F-FDOPA PET in Parkinson\u2019s disease patients","volume":"44","author":"Huang","year":"2003","journal-title":"J. Nucl. Med."},{"key":"10.1016\/j.xcrm.2025.102207_bib9","doi-asserted-by":"crossref","first-page":"1262","DOI":"10.1001\/jamaneurol.2021.1312","article-title":"Emerging Neuroimaging Biomarkers Across Disease Stage in Parkinson Disease: A Review","volume":"78","author":"Mitchell","year":"2021","journal-title":"JAMA Neurol."},{"key":"10.1016\/j.xcrm.2025.102207_bib10","doi-asserted-by":"crossref","first-page":"e2672","DOI":"10.1212\/WNL.0000000000201269","article-title":"Association Between White Matter Networks and the Pattern of Striatal Dopamine Depletion in Patients With Parkinson Disease","volume":"99","author":"Chung","year":"2022","journal-title":"Neurology"},{"key":"10.1016\/j.xcrm.2025.102207_bib11","doi-asserted-by":"crossref","first-page":"787","DOI":"10.1097\/RLU.0000000000002251","article-title":"The Pattern of Striatal Dopamine Depletion as a Prognostic Marker in De Novo Parkinson Disease","volume":"43","author":"Chung","year":"2018","journal-title":"Clin. Nucl. Med."},{"key":"10.1016\/j.xcrm.2025.102207_bib12","doi-asserted-by":"crossref","first-page":"e296","DOI":"10.1097\/RLU.0000000000003609","article-title":"Levodopa-Induced Dyskinesia in Parkinson Disease Specifically Associates With Dopaminergic Depletion in Sensorimotor-Related Functional Subregions of the Striatum","volume":"46","author":"Labrador-Espinosa","year":"2021","journal-title":"Clin. Nucl. Med."},{"key":"10.1016\/j.xcrm.2025.102207_bib13","doi-asserted-by":"crossref","first-page":"2014","DOI":"10.1002\/mds.29997","article-title":"Rest Tremor in Parkinson's Disease Is Associated with Ipsilateral Striatal Dopamine Transporter Binding","volume":"39","author":"Niemi","year":"2024","journal-title":"Mov. Disord."},{"key":"10.1016\/j.xcrm.2025.102207_bib14","first-page":"350","article-title":"Elaboration of the SPM template for the standardization of SPECT images with 123I-Ioflupane","volume":"32","author":"Garc\u00eda-G\u00f3mez","year":"2013","journal-title":"Rev. Espa\u00f1ola Med. Nucl. Imagen Mol."},{"key":"10.1016\/j.xcrm.2025.102207_bib15","first-page":"23","article-title":"Elaboraci\u00f3n de una plantilla de SPM para la normalizaci\u00f3n de im\u00e1genes de PET con 18F-DOPA","volume":"9","author":"G\u00f3mez","year":"2018","journal-title":"Imagen Diagn\u00f3stica"},{"key":"10.1016\/j.xcrm.2025.102207_bib16","article-title":"CT-guided spatial normalization of nuclear hybrid imaging adapted to enlarged ventricles: Impact on striatal uptake quantification","volume":"294","author":"Alae Eddine","year":"2024","journal-title":"Neuroimage"},{"key":"10.1016\/j.xcrm.2025.102207_bib17","doi-asserted-by":"crossref","first-page":"17","DOI":"10.1038\/s41746-024-01012-z","article-title":"Diagnostic performance of artificial intelligence-assisted PET imaging for Parkinson's disease: a systematic review and meta-analysis","volume":"7","author":"Wang","year":"2024","journal-title":"NPJ Digit. Med."},{"key":"10.1016\/j.xcrm.2025.102207_bib18","doi-asserted-by":"crossref","first-page":"2798","DOI":"10.1007\/s00259-022-05804-x","article-title":"Decoding the dopamine transporter imaging for the differential diagnosis of parkinsonism using deep learning","volume":"49","author":"Zhao","year":"2022","journal-title":"Eur. J. Nucl. Med. Mol. Imaging"},{"key":"10.1016\/j.xcrm.2025.102207_bib19","doi-asserted-by":"crossref","first-page":"156","DOI":"10.1038\/s41586-023-06555-x","article-title":"A foundation model for generalizable disease detection from retinal images","volume":"622","author":"Zhou","year":"2023","journal-title":"Nature"},{"key":"10.1016\/j.xcrm.2025.102207_bib20","doi-asserted-by":"crossref","first-page":"181","DOI":"10.1038\/s41586-024-07441-w","article-title":"A whole-slide foundation model for digital pathology from real-world data","volume":"630","author":"Xu","year":"2024","journal-title":"Nature"},{"key":"10.1016\/j.xcrm.2025.102207_bib21","doi-asserted-by":"crossref","first-page":"1399","DOI":"10.1038\/s41551-022-00936-9","article-title":"Expert-level detection of pathologies from unannotated chest X-ray images via self-supervised learning","volume":"6","author":"Tiu","year":"2022","journal-title":"Nat. Biomed. Eng."},{"key":"10.1016\/j.xcrm.2025.102207_bib22","doi-asserted-by":"crossref","first-page":"102051","DOI":"10.1016\/j.media.2021.102051","article-title":"Longitudinal self-supervised learning","volume":"71","author":"Zhao","year":"2021","journal-title":"Med. Image Anal."},{"key":"10.1016\/j.xcrm.2025.102207_bib23","first-page":"8780","article-title":"Diffusion models beat gans on image synthesis","volume":"34","author":"Dhariwal","year":"2021","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"10.1016\/j.xcrm.2025.102207_bib24","first-page":"4195","article-title":"Scalable diffusion models with transformers","author":"Peebles","year":"2023","journal-title":"Proceedings of the IEEE\/CVF International Conference on Computer Vision"},{"key":"10.1016\/j.xcrm.2025.102207_bib25","first-page":"26565","article-title":"Elucidating the design space of diffusion-based generative models","volume":"35","author":"Karras","year":"2022","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"10.1016\/j.xcrm.2025.102207_bib26","article-title":"Hierarchical text-conditional image generation with clip latents","author":"Ramesh","year":"2022","journal-title":"arXiv."},{"key":"10.1016\/j.xcrm.2025.102207_bib27","doi-asserted-by":"crossref","unstructured":"Rombach, R., Blattmann, A., Lorenz, D., Esser, P., Ommer, B. (2022). High-resolution image synthesis with latent diffusion models. Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition. p. 10684\u201310695.","DOI":"10.1109\/CVPR52688.2022.01042"},{"key":"10.1016\/j.xcrm.2025.102207_bib28","first-page":"36479","article-title":"Photorealistic text-to-image diffusion models with deep language understanding","volume":"35","author":"Saharia","year":"2022","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"10.1016\/j.xcrm.2025.102207_bib29","article-title":"Prompt-to-prompt image editing with cross attention control","author":"Hertz","year":"2022","journal-title":"Preprint at arXiv"},{"key":"10.1016\/j.xcrm.2025.102207_bib30","first-page":"15802","article-title":"Denoising diffusion autoencoders are unified self-supervised learners","author":"Xiang","year":"2023","journal-title":"Proceedings of the IEEE\/CVF International Conference on Computer Vision"},{"key":"10.1016\/j.xcrm.2025.102207_bib31","doi-asserted-by":"crossref","first-page":"101713","DOI":"10.1016\/j.xcrm.2024.101713","article-title":"Reconstruction of patient-specific confounders in AI-based radiologic image interpretation using generative pretraining","volume":"5","author":"Han","year":"2024","journal-title":"Cell Rep. Med."},{"key":"10.1016\/j.xcrm.2025.102207_bib32","article-title":"Interpretable Alzheimer\u2019s Disease Classification Via a Contrastive Diffusion Autoencoder","author":"Ijishakin","year":"2023","journal-title":"Preprint at arXiv"},{"key":"10.1016\/j.xcrm.2025.102207_bib33","first-page":"5374","article-title":"Hierarchical diffusion autoencoders and disentangled image manipulation","author":"Lu","year":"2024","journal-title":"Proceedings of the IEEE\/CVF Winter Conference on Applications of Computer Vision"},{"key":"10.1016\/j.xcrm.2025.102207_bib34","doi-asserted-by":"crossref","first-page":"662","DOI":"10.1007\/s00330-023-10003-9","article-title":"Identification of Parkinson's disease and multiple system atrophy using multimodal PET\/MRI radiomics","volume":"34","author":"Sun","year":"2024","journal-title":"Eur. Radiol."},{"key":"10.1016\/j.xcrm.2025.102207_bib35","doi-asserted-by":"crossref","first-page":"7003","DOI":"10.1007\/s00330-021-07779-z","article-title":"The role of the deep convolutional neural network as an aid to interpreting brain [(18)F]DOPA PET\/CT in the diagnosis of Parkinson's disease","volume":"31","author":"Piccardo","year":"2021","journal-title":"Eur. Radiol."},{"key":"10.1016\/j.xcrm.2025.102207_bib36","doi-asserted-by":"crossref","first-page":"674","DOI":"10.1109\/34.192463","article-title":"A theory for multiresolution signal decomposition: the wavelet representation","volume":"11","author":"Mallat","year":"1989","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"10.1016\/j.xcrm.2025.102207_bib37","series-title":"Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition","first-page":"9653","article-title":"Simmim: A simple framework for masked image modeling","author":"Xie","year":"2022"},{"key":"10.1016\/j.xcrm.2025.102207_bib38","article-title":"Disruptive Autoencoders: Leveraging Low-level features for 3D Medical Image Pre-training","author":"Valanarasu","year":"2023","journal-title":"arXiv"},{"key":"10.1016\/j.xcrm.2025.102207_bib39","series-title":"Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition","first-page":"2287","article-title":"Encoding in style: a stylegan encoder for image-to-image translation","author":"Richardson","year":"2021"},{"key":"10.1016\/j.xcrm.2025.102207_bib40","doi-asserted-by":"crossref","first-page":"336","DOI":"10.1007\/s11263-019-01228-7","article-title":"Grad-CAM: Visual Explanations from Deep Networks via Gradient-Based Localization","volume":"128","author":"Selvaraju","year":"2019","journal-title":"Int. J. Comput. Vis."},{"key":"10.1016\/j.xcrm.2025.102207_bib41","doi-asserted-by":"crossref","DOI":"10.7717\/peerj-cs.623","article-title":"The coefficient of determination R-squared is more informative than SMAPE, MAE, MAPE, MSE and RMSE in regression analysis evaluation","volume":"7","author":"Chicco","year":"2021","journal-title":"PeerJ Comput. Sci."},{"key":"10.1016\/j.xcrm.2025.102207_bib42","doi-asserted-by":"crossref","first-page":"255","DOI":"10.2307\/2532051","article-title":"A concordance correlation coefficient to evaluate reproducibility","volume":"45","author":"Lawrence","year":"1989","journal-title":"Biometrics"},{"key":"10.1016\/j.xcrm.2025.102207_bib43","doi-asserted-by":"crossref","first-page":"463","DOI":"10.1097\/COH.0b013e32833ed177","article-title":"What are biomarkers?","volume":"5","author":"Strimbu","year":"2010","journal-title":"Curr. Opin. HIV AIDS"},{"key":"10.1016\/j.xcrm.2025.102207_bib44","doi-asserted-by":"crossref","first-page":"63","DOI":"10.1186\/s13550-019-0537-2","article-title":"Reliability of visual assessment by non-expert nuclear medicine physicians and appropriateness of indications of [(123)I]FP-CIT SPECT imaging by neurologists in patients with early drug-naive Parkinson's disease","volume":"9","author":"Suwijn","year":"2019","journal-title":"EJNMMI Res."},{"key":"10.1016\/j.xcrm.2025.102207_bib45","doi-asserted-by":"crossref","first-page":"27","DOI":"10.1186\/1471-2377-7-27","article-title":"Meta-analysis of the literature on diagnostic accuracy of SPECT in parkinsonian syndromes","volume":"7","author":"Vlaar","year":"2007","journal-title":"BMC Neurol."},{"key":"10.1016\/j.xcrm.2025.102207_bib46","doi-asserted-by":"crossref","first-page":"586","DOI":"10.1016\/j.nicl.2017.09.010","article-title":"Refining diagnosis of Parkinson's disease with deep learning-based interpretation of dopamine transporter imaging","volume":"16","author":"Choi","year":"2017","journal-title":"Neuroimage. Clin."},{"key":"10.1016\/j.xcrm.2025.102207_bib47","doi-asserted-by":"crossref","first-page":"861","DOI":"10.1093\/brain\/awf080","article-title":"The accuracy of diagnosis of parkinsonian syndromes in a specialist movement disorder service","volume":"125","author":"Hughes","year":"2002","journal-title":"Brain"},{"key":"10.1016\/j.xcrm.2025.102207_bib48","doi-asserted-by":"crossref","first-page":"27S","DOI":"10.2967\/jnumed.121.263197","article-title":"Imaging Dopaminergic Neurotransmission in Neurodegenerative Disorders","volume":"63","author":"Wallert","year":"2022","journal-title":"J. Nucl. Med."},{"key":"10.1016\/j.xcrm.2025.102207_bib49","doi-asserted-by":"crossref","first-page":"170","DOI":"10.1111\/ane.12932","article-title":"Patterns of dopamine transporter imaging in subtypes of multiple system atrophy","volume":"138","author":"Bu","year":"2018","journal-title":"Acta Neurol. Scand."},{"key":"10.1016\/j.xcrm.2025.102207_bib50","doi-asserted-by":"crossref","first-page":"93","DOI":"10.4103\/1673-5374.391180","article-title":"Recent progress in the applications of presynaptic dopaminergic positron emission tomography imaging in parkinsonism","volume":"20","author":"Yang","year":"2025","journal-title":"Neural Regen. Res."},{"key":"10.1016\/j.xcrm.2025.102207_bib51","doi-asserted-by":"crossref","first-page":"1009","DOI":"10.1002\/mds.22484","article-title":"A follow-up study on 6-[18F]fluoro-L-dopa uptake in early Parkinson's disease shows nonlinear progression in the putamen","volume":"24","author":"Bruck","year":"2009","journal-title":"Mov. Disord."},{"key":"10.1016\/j.xcrm.2025.102207_bib52","doi-asserted-by":"crossref","first-page":"3290","DOI":"10.1093\/brain\/awr233","article-title":"Longitudinal evolution of compensatory changes in striatal dopamine processing in Parkinson's disease","volume":"134","author":"Nandhagopal","year":"2011","journal-title":"Brain"},{"key":"10.1016\/j.xcrm.2025.102207_bib53","doi-asserted-by":"crossref","DOI":"10.1016\/j.media.2022.102571","article-title":"Self-supervised learning of neighborhood embedding for longitudinal MRI","volume":"82","author":"Ouyang","year":"2022","journal-title":"Med. Image Anal."},{"key":"10.1016\/j.xcrm.2025.102207_bib54","article-title":"Anatomical Foundation Models for Brain MRIs","author":"Barbano","year":"2024","journal-title":"arXiv"},{"key":"10.1016\/j.xcrm.2025.102207_bib55","article-title":"Few-shot image generation with diffusion models","author":"Zhu","year":"2022","journal-title":"arXiv"},{"key":"10.1016\/j.xcrm.2025.102207_bib56","series-title":"International Conference on Machine Learning","first-page":"13213","article-title":"simple diffusion: End-to-end diffusion for high resolution images","author":"Hoogeboom","year":"2023"},{"key":"10.1016\/j.xcrm.2025.102207_bib57","doi-asserted-by":"crossref","first-page":"745","DOI":"10.1136\/jnnp.51.6.745","article-title":"The relevance of the Lewy body to the pathogenesis of idiopathic Parkinson's disease","volume":"51","author":"Gibb","year":"1988","journal-title":"J. Neurol. Neurosurg. Psychiatry"},{"key":"10.1016\/j.xcrm.2025.102207_bib58","doi-asserted-by":"crossref","first-page":"670","DOI":"10.1212\/01.wnl.0000324625.00404.15","article-title":"Second consensus statement on the diagnosis of multiple system atrophy","volume":"71","author":"Gilman","year":"2008","journal-title":"Neurology"},{"key":"10.1016\/j.xcrm.2025.102207_bib59","doi-asserted-by":"crossref","first-page":"853","DOI":"10.1002\/mds.26987","article-title":"Clinical diagnosis of progressive supranuclear palsy: The movement disorder society criteria","volume":"32","author":"Hoglinger","year":"2017","journal-title":"Mov. Disord."},{"key":"10.1016\/j.xcrm.2025.102207_bib60","first-page":"S2","article-title":"Diagnostic criteria for essential tremor and differential diagnosis","volume":"54","author":"Elble","year":"2000","journal-title":"Neurology"},{"key":"10.1016\/j.xcrm.2025.102207_bib61","doi-asserted-by":"crossref","first-page":"319","DOI":"10.1136\/jnnp-2014-310256","article-title":"What do patients with scans without evidence of dopaminergic deficit (SWEDD) have? New evidence and continuing controversies","volume":"87","author":"Erro","year":"2016","journal-title":"J. Neurol. Neurosurg. Psychiatry"},{"key":"10.1016\/j.xcrm.2025.102207_bib62","doi-asserted-by":"crossref","first-page":"345","DOI":"10.1016\/j.nucmedbio.2007.02.007","article-title":"One-step high-radiochemical-yield synthesis of [18F]FP-CIT using a protic solvent system","volume":"34","author":"Lee","year":"2007","journal-title":"Nucl. Med. Biol."},{"key":"10.1016\/j.xcrm.2025.102207_bib63","doi-asserted-by":"crossref","DOI":"10.1016\/j.neuroimage.2022.119474","article-title":"SynthStrip: skull-stripping for any brain image","volume":"260","author":"Hoopes","year":"2022","journal-title":"Neuroimage"},{"key":"10.1016\/j.xcrm.2025.102207_bib64","doi-asserted-by":"crossref","first-page":"4952","DOI":"10.1002\/hbm.24750","article-title":"Automated brain extraction of multisequence MRI using artificial neural networks","volume":"40","author":"Isensee","year":"2019","journal-title":"Hum. Brain Mapp."},{"key":"10.1016\/j.xcrm.2025.102207_bib65","doi-asserted-by":"crossref","first-page":"264","DOI":"10.1016\/j.neuroimage.2010.06.044","article-title":"Imaging dopamine receptors in humans with [11C]-(+)-PHNO: dissection of D3 signal and anatomy","volume":"54","author":"Tziortzi","year":"2011","journal-title":"Neuroimage"},{"key":"10.1016\/j.xcrm.2025.102207_bib66","doi-asserted-by":"crossref","first-page":"1165","DOI":"10.1093\/cercor\/bhs397","article-title":"Connectivity-based functional analysis of dopamine release in the striatum using diffusion-weighted MRI and positron emission tomography","volume":"24","author":"Tziortzi","year":"2014","journal-title":"Cereb. Cortex"},{"key":"10.1016\/j.xcrm.2025.102207_bib67","doi-asserted-by":"crossref","DOI":"10.1016\/j.neuroimage.2019.116189","article-title":"Automated anatomical labelling atlas 3","volume":"206","author":"Rolls","year":"2020","journal-title":"Neuroimage"},{"key":"10.1016\/j.xcrm.2025.102207_bib68","doi-asserted-by":"crossref","first-page":"50","DOI":"10.1109\/99.388960","article-title":"An introduction to wavelets","volume":"2","author":"Graps","year":"1995","journal-title":"IEEE Comput. Sci. Eng."},{"key":"10.1016\/j.xcrm.2025.102207_bib69","first-page":"6840","article-title":"Denoising diffusion probabilistic models","volume":"33","author":"Ho","year":"2020","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"10.1016\/j.xcrm.2025.102207_bib70","series-title":"Proceedings of the IEEE\/CVF International Conference on Computer Vision","first-page":"7441","article-title":"Efficient diffusion training via min-snr weighting strategy","author":"Hang","year":"2023"},{"key":"10.1016\/j.xcrm.2025.102207_bib71","article-title":"Decoupled weight decay regularization","author":"Loshchilov","year":"2017","journal-title":"arXiv"},{"key":"10.1016\/j.xcrm.2025.102207_bib72","article-title":"Denoising diffusion implicit models","author":"Song","year":"2020","journal-title":"arXiv"},{"key":"10.1016\/j.xcrm.2025.102207_bib73","article-title":"SGDR: Stochastic gradient descent with warm restarts","author":"Loshchilov","year":"2016","journal-title":"arXiv"},{"key":"10.1016\/j.xcrm.2025.102207_bib74","series-title":"Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition","first-page":"8110","article-title":"Analyzing and improving the image quality of stylegan","author":"Karras","year":"2020"},{"key":"10.1016\/j.xcrm.2025.102207_bib75","article-title":"Geometric GAN","author":"Lim","year":"2017","journal-title":"arXiv"},{"key":"10.1016\/j.xcrm.2025.102207_bib76","series-title":"International MICCAI brainlesion workshop","first-page":"272","article-title":"Swin unetr: Swin transformers for semantic segmentation of brain tumors in mri images","author":"Hatamizadeh","year":"2021"},{"key":"10.1016\/j.xcrm.2025.102207_bib77","article-title":"Generative AI for medical imaging: extending the MONAI framework","author":"Pinaya","year":"2023","journal-title":"arXiv"}],"container-title":["Cell Reports Medicine"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S2666379125002800?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S2666379125002800?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2025,7,23]],"date-time":"2025-07-23T13:57:16Z","timestamp":1753279036000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S2666379125002800"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,7]]},"references-count":77,"journal-issue":{"issue":"7","published-print":{"date-parts":[[2025,7]]}},"alternative-id":["S2666379125002800"],"URL":"https:\/\/doi.org\/10.1016\/j.xcrm.2025.102207","relation":{},"ISSN":["2666-3791"],"issn-type":[{"value":"2666-3791","type":"print"}],"subject":[],"published":{"date-parts":[[2025,7]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Enhancing 3D dopamine transporter imaging as a biomarker for Parkinson\u2019s disease via self-supervised learning with diffusion models","name":"articletitle","label":"Article Title"},{"value":"Cell Reports Medicine","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.xcrm.2025.102207","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2025 The Author(s). Published by Elsevier Inc.","name":"copyright","label":"Copyright"}],"article-number":"102207"}}