{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,15]],"date-time":"2026-05-15T00:13:20Z","timestamp":1778804000305,"version":"3.51.4"},"reference-count":53,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T00:00:00Z","timestamp":1780272000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T00:00:00Z","timestamp":1780272000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,3,15]],"date-time":"2026-03-15T00:00:00Z","timestamp":1773532800000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001659","name":"Deutsche Forschungsgemeinschaft","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100001659","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100023863","name":"Munich Center for Machine Learning","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100023863","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Medical Image Analysis"],"published-print":{"date-parts":[[2026,6]]},"DOI":"10.1016\/j.media.2026.104035","type":"journal-article","created":{"date-parts":[[2026,3,17]],"date-time":"2026-03-17T14:07:38Z","timestamp":1773756458000},"page":"104035","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"special_numbering":"C","title":["Translating MRI to PET through conditional diffusion models with enhanced pathology awareness"],"prefix":"10.1016","volume":"111","author":[{"ORCID":"https:\/\/orcid.org\/0009-0009-3874-6055","authenticated-orcid":false,"given":"Yitong","family":"Li","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4764-798X","authenticated-orcid":false,"given":"Igor","family":"Yakushev","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8994-5593","authenticated-orcid":false,"given":"Dennis M.","family":"Hedderich","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3652-1874","authenticated-orcid":false,"given":"Christian","family":"Wachinger","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"78","reference":[{"key":"10.1016\/j.media.2026.104035_bib0001","first-page":"1","article-title":"On the path to 2025: understanding the Alzheimer\u2019s disease continuum","volume":"9","author":"Aisen","year":"2017","journal-title":"Alzheimer\u2019s Res. Ther."},{"key":"10.1016\/j.media.2026.104035_bib0002","doi-asserted-by":"crossref","first-page":"627","DOI":"10.3233\/JAD-2011-110458","article-title":"Review and meta-analysis of biomarkers and diagnostic imaging in Alzheimer\u2019s disease","volume":"26","author":"Bloudek","year":"2011","journal-title":"J. Alzheimers Dis."},{"issue":"2","key":"10.1016\/j.media.2026.104035_bib0003","doi-asserted-by":"crossref","first-page":"2025","DOI":"10.1002\/alz.14421","article-title":"A multi-view learning approach with diffusion model to synthesize fdg pet from mri t1wi for diagnosis of Alzheimer\u2019s disease","volume":"21","author":"Chen","year":"2025","journal-title":"Alzheimer\u2019s Dementia"},{"key":"10.1016\/j.media.2026.104035_bib0004","series-title":"Proc. IEEE 22nd Int. Symp. Biomed. Imaging (ISBI)","first-page":"1","article-title":"Plasma-CycleGAN: plasma biomarker-guided MRI to PET cross-modality translation using conditional cyclegan","author":"Chen","year":"2025"},{"key":"10.1016\/j.media.2026.104035_bib0005","doi-asserted-by":"crossref","unstructured":"Chouliaras, L., O\u2019brien, J., 2023. The use of neuroimaging techniques in the early and differential diagnosis of dementia. Molecular Psychiatry 28, 4084\u20134097.","DOI":"10.1038\/s41380-023-02215-8"},{"key":"10.1016\/j.media.2026.104035_bib0006","doi-asserted-by":"crossref","first-page":"2598","DOI":"10.1109\/TMI.2022.3167808","article-title":"ResViT: residual vision transformers for multimodal medical image synthesis","volume":"41","author":"Dalmaz","year":"2022","journal-title":"IEEE Trans. Med. Imaging"},{"key":"10.1016\/j.media.2026.104035_bib0007","series-title":"Diffusion Models Beat GANs on Image Synthesis","author":"Dhariwal","year":"2021"},{"key":"10.1016\/j.media.2026.104035_bib0008","doi-asserted-by":"crossref","first-page":"774","DOI":"10.1016\/j.neuroimage.2012.01.021","article-title":"Freesurfer","volume":"62","author":"Fischl","year":"2012","journal-title":"Neuroimage"},{"key":"10.1016\/j.media.2026.104035_bib0009","doi-asserted-by":"crossref","first-page":"189","DOI":"10.1016\/0022-3956(75)90026-6","article-title":"Mini-mental state\u201d: a practical method for grading the cognitive state of patients for the clinician","volume":"12","author":"Folstein","year":"1975","journal-title":"J. Psychiatr. Res."},{"key":"10.1016\/j.media.2026.104035_bib0010","doi-asserted-by":"crossref","first-page":"487","DOI":"10.1212\/WNL.0b013e31829d86e8","article-title":"Imaging markers for alzheimer disease: which vs how","volume":"81","author":"Frisoni","year":"2013","journal-title":"Neurology"},{"key":"10.1016\/j.media.2026.104035_bib0011","doi-asserted-by":"crossref","first-page":"140S","DOI":"10.2967\/jnumed.120.252510","article-title":"A pioneering paper that provided a tool for accurate, observer-independent analysis of 18 F-FDG brain scans in neurodegenerative dementias","volume":"61","author":"Herscovitch","year":"2020","journal-title":"J. Nucl. Med."},{"key":"10.1016\/j.media.2026.104035_bib0012","doi-asserted-by":"crossref","first-page":"199","DOI":"10.1093\/pan\/mpl013","article-title":"Matching as nonparametric preprocessing for reducing model dependence in parametric causal inference","volume":"15","author":"Ho","year":"2007","journal-title":"Polit. Anal."},{"key":"10.1016\/j.media.2026.104035_bib0013","series-title":"Denoising Diffusion Probabilistic Models","author":"Ho","year":"2020"},{"key":"10.1016\/j.media.2026.104035_bib0014","first-page":"1920","article-title":"FDG PET imaging in patients with pathologically verified dementia","volume":"41","author":"Hoffman","year":"2000","journal-title":"J. Nucl. Med."},{"key":"10.1016\/j.media.2026.104035_bib0015","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.media.2026.104035_bib0016","doi-asserted-by":"crossref","first-page":"145","DOI":"10.1109\/TMI.2021.3107013","article-title":"Bidirectional mapping generative adversarial networks for brain MR to PET synthesis","volume":"41","author":"Hu","year":"2021","journal-title":"IEEE Trans. Med. Imaging"},{"key":"10.1016\/j.media.2026.104035_bib0017","series-title":"Image-to-Image Translation with Conditional Adversarial Networks","author":"Isola","year":"2017"},{"key":"10.1016\/j.media.2026.104035_bib0018","doi-asserted-by":"crossref","first-page":"424","DOI":"10.1109\/JBHI.2019.2929264","article-title":"Dynamic prediction in clinical survival analysis using temporal convolutional networks","volume":"24","author":"Jarrett","year":"2019","journal-title":"IEEE J. Biomed. Health Inf."},{"key":"10.1016\/j.media.2026.104035_bib0019","doi-asserted-by":"crossref","first-page":"685","DOI":"10.1002\/jmri.21049","article-title":"The Alzheimer\u2019s disease neuroimaging initiative (adni): MRI methods","volume":"27","author":"Jr","year":"2008","journal-title":"J. Magn. Reson. Imaging"},{"key":"10.1016\/j.media.2026.104035_bib0020","doi-asserted-by":"crossref","first-page":"31","DOI":"10.2214\/ajr.177.1.1770031","article-title":"A cost analysis of positron emission tomography","volume":"177","author":"Keppler","year":"2001","journal-title":"Am. J. Roentgenol."},{"key":"10.1016\/j.media.2026.104035_bib0021","series-title":"Proc. IEEE\/CVF Winter Conf. Appl. Comput. Vis. (WACV)","first-page":"7604","article-title":"Adaptive latent diffusion model for 3D medical image to image translation: multi-modal magnetic resonance imaging study","author":"Kim","year":"2024"},{"key":"10.1016\/j.media.2026.104035_bib0022","first-page":"1964","article-title":"Breaking the dilemma of medical image-to-image translation","volume":"34","author":"Kong","year":"2021","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"10.1016\/j.media.2026.104035_bib0023","series-title":"Diffusion-based Image Translation using Disentangled Style and Content Representation","author":"Kwon","year":"2023"},{"key":"10.1016\/j.media.2026.104035_bib0024","doi-asserted-by":"crossref","DOI":"10.3389\/fnins.2021.626636","article-title":"A systematic review of glucose transport alterations in Alzheimer\u2019s disease","volume":"15","author":"Kyrtata","year":"2021","journal-title":"Front. Neurosci."},{"key":"10.1016\/j.media.2026.104035_bib0025","doi-asserted-by":"crossref","first-page":"1718","DOI":"10.1002\/mrm.28819","article-title":"Three-dimensional self-attention conditional gan with spectral normalization for multimodal neuroimaging synthesis","volume":"86","author":"Lan","year":"2021","journal-title":"Magn. Reson. Med."},{"key":"10.1016\/j.media.2026.104035_bib0026","doi-asserted-by":"crossref","first-page":"1207","DOI":"10.1016\/j.neurobiolaging.2009.07.002","article-title":"Associations between cognitive, functional, and FDG-PET measures of decline in AD and MCI","volume":"32","author":"Landau","year":"2011","journal-title":"Neurobiol. Aging"},{"key":"10.1016\/j.media.2026.104035_bib0027","series-title":"BBDM: Image-to-Image Translation with Brownian Bridge Diffusion Models","author":"Li","year":"2023"},{"key":"10.1016\/j.media.2026.104035_bib0028","series-title":"Medical Image Computing and Computer Assisted Intervention - MICCAI 2024","first-page":"529","article-title":"PASTA: pathology-aware MRI to PET cross-modal translation with diffusion models","author":"Li","year":"2024"},{"key":"10.1016\/j.media.2026.104035_bib0029","doi-asserted-by":"crossref","DOI":"10.3389\/fnins.2021.646013","article-title":"Alzheimer\u2019s disease neuroimaging initiative, bidirectional mapping of brain MRI and PET with 3D reversible GAN for the diagnosis of Alzheimer\u2019s disease","volume":"15","author":"Lin","year":"2021","journal-title":"Front. Neurosci."},{"key":"10.1016\/j.media.2026.104035_bib0030","doi-asserted-by":"crossref","first-page":"413","DOI":"10.1097\/RLU.0000000000000547","article-title":"Brain pet in the diagnosis of Alzheimer\u2019s disease","volume":"39","author":"Marcus","year":"2014","journal-title":"Clin. Nucl. Med."},{"key":"10.1016\/j.media.2026.104035_bib0031","first-page":"1238","article-title":"A diagnostic approach in Alzheimer\u2019s disease using three-dimensional stereotactic surface projections of fluorine-18-FDG PET","volume":"36","author":"Minoshima","year":"1995","journal-title":"J. Nucl. Med."},{"key":"10.1016\/j.media.2026.104035_bib0032","doi-asserted-by":"crossref","first-page":"13","DOI":"10.1097\/00002093-199700112-00003","article-title":"Development of cognitive instruments for use in clinical trials of antidementia drugs: additions to the alzheimer\u2019s disease assessment scale that broaden its scope","volume":"11","author":"Mohs","year":"1997","journal-title":"Alzheimer Dis. Associated Disord."},{"key":"10.1016\/j.media.2026.104035_bib0033","series-title":"Improved Denoising Diffusion Probabilistic Models","author":"Nichol","year":"2021"},{"key":"10.1016\/j.media.2026.104035_bib0034","doi-asserted-by":"crossref","unstructured":"\u00d6zbey, M., Dalmaz, O., Dar, S. U., Bedel, H. A., \u00d6zturk, \u015e., G\u00fcng\u00f6r, A., \u00c7ukur, T., 2023. Unsupervised medical image translation with adversarial diffusion models. IEEE Trans. Med. Imaging 42, 3524\u20133539.","DOI":"10.1109\/TMI.2023.3290149"},{"key":"10.1016\/j.media.2026.104035_bib0035","series-title":"Generating Realistic Brain MRIs via a Conditional Diffusion Probabilistic Model","author":"Peng","year":"2023"},{"key":"10.1016\/j.media.2026.104035_bib0036","series-title":"High-Resolution Image Synthesis with Latent Diffusion Models","author":"Rombach","year":"2022"},{"key":"10.1016\/j.media.2026.104035_bib0037","series-title":"U-Net: Convolutional Networks for Biomedical Image Segmentation","author":"Ronneberger","year":"2015"},{"key":"10.1016\/j.media.2026.104035_bib0038","series-title":"Palette: Image-to-Image Diffusion Models","author":"Saharia","year":"2022"},{"key":"10.1016\/j.media.2026.104035_bib0039","series-title":"Progressive Distillation for Fast Sampling of Diffusion Models","author":"Salimans","year":"2022"},{"key":"10.1016\/j.media.2026.104035_bib0040","unstructured":"Shin, H.-C., Ihsani, A., Mandava, S., Sreenivas, S. T., Forster, C., Cha, J., Initiative, A. D. N., 2020a. GANBERT: Generative adversarial networks with bidirectional encoder representations from transformers for MRI to PET synthesis. arXiv: 2008.04393."},{"key":"10.1016\/j.media.2026.104035_bib0041","series-title":"GANDALF: Generative Adversarial Networks with Discriminator-Adaptive Loss Fine-Tuning for Alzheimer\u2019s Disease Diagnosis from MRI","author":"Shin","year":"2020"},{"key":"10.1016\/j.media.2026.104035_bib0042","unstructured":"Sohl-Dickstein, J., Weiss, E., Maheswaranathan, N., Ganguli, S., 2015. Deep unsupervised learning using nonequilibrium thermodynamics. In: International conference on machine learning - ICML. 2256\u20132265."},{"key":"10.1016\/j.media.2026.104035_bib0043","series-title":"Denoising Diffusion Implicit Models","author":"Song","year":"2021"},{"key":"10.1016\/j.media.2026.104035_bib0044","doi-asserted-by":"crossref","first-page":"53","DOI":"10.1146\/annurev.ne.19.030196.000413","article-title":"Apolipoprotein e and Alzheimer\u2019s disease","volume":"19","author":"Strittmatter","year":"1996","journal-title":"Annu. Rev. Neurosci."},{"key":"10.1016\/j.media.2026.104035_bib0045","series-title":"Dual Diffusion Implicit Bridges for Image-to-Image Translation","author":"Su","year":"2023"},{"key":"10.1016\/j.media.2026.104035_bib0046","doi-asserted-by":"crossref","DOI":"10.1016\/j.cmpb.2022.106818","article-title":"ClinicaDL: an open-source deep learning software for reproducible neuroimaging processing","volume":"220","author":"Thibeau-Sutre","year":"2022","journal-title":"Comput. Methods Programs Biomed."},{"key":"10.1016\/j.media.2026.104035_bib0047","series-title":"Learning myelin content in multiple sclerosis from multimodal MRI through adversarial training","author":"Wei","year":"2018"},{"key":"10.1016\/j.media.2026.104035_bib0048","doi-asserted-by":"crossref","first-page":"5250","DOI":"10.1002\/mp.17254","article-title":"Synthesizing pet images from high-field and ultra-high-field MR images using joint diffusion attention model","volume":"51","author":"Xie","year":"2024","journal-title":"Med. Phys."},{"key":"10.1016\/j.media.2026.104035_bib0049","unstructured":"Yu, M., Wu, M., Yue, L., Bozoki, A., Liu, M., 2024. Functional imaging constrained diffusion for brain PET synthesis from structural MRI, arXiv preprint arXiv: 2405.02504."},{"key":"10.1016\/j.media.2026.104035_bib0050","doi-asserted-by":"crossref","DOI":"10.1016\/j.cmpb.2022.106676","article-title":"BPGAN: brain PET synthesis from mri using generative adversarial network for multi-modal Alzheimer\u2019s disease diagnosis","volume":"217","author":"Zhang","year":"2022","journal-title":"Comput. Methods Programs Biomed."},{"key":"10.1016\/j.media.2026.104035_bib0051","series-title":"Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networkss","author":"Zhu","year":"2017"},{"key":"10.1016\/j.media.2026.104035_bib0052","series-title":"Make-a-Volume: Leveraging Latent Diffusion Models for Cross-Modality 3D Brain MRI Synthesis","author":"Zhu","year":"2023"},{"key":"10.1016\/j.media.2026.104035_bib0053","doi-asserted-by":"crossref","DOI":"10.1016\/j.cmpb.2025.108727","article-title":"GAN-based synthetic FDG PET images from T1 brain MRI can serve to improve performance of deep unsupervised anomaly detection models","volume":"265","author":"Zotova","year":"2025","journal-title":"Comput. Methods Programs Biomed."}],"container-title":["Medical Image Analysis"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S1361841526001040?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S1361841526001040?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,5,15]],"date-time":"2026-05-15T00:05:41Z","timestamp":1778803541000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S1361841526001040"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,6]]},"references-count":53,"alternative-id":["S1361841526001040"],"URL":"https:\/\/doi.org\/10.1016\/j.media.2026.104035","relation":{},"ISSN":["1361-8415"],"issn-type":[{"value":"1361-8415","type":"print"}],"subject":[],"published":{"date-parts":[[2026,6]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Translating MRI to PET through conditional diffusion models with enhanced pathology awareness","name":"articletitle","label":"Article Title"},{"value":"Medical Image Analysis","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.media.2026.104035","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2026 The Author(s). Published by Elsevier B.V.","name":"copyright","label":"Copyright"}],"article-number":"104035"}}