{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,19]],"date-time":"2026-10-19T18:39:57Z","timestamp":1792435197513,"version":"4.5.0"},"reference-count":62,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2026,7,15]],"date-time":"2026-07-15T00:00:00Z","timestamp":1784073600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"},{"start":{"date-parts":[[2026,10,19]],"date-time":"2026-10-19T00:00:00Z","timestamp":1792368000000},"content-version":"vor","delay-in-days":96,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"}],"funder":[{"name":"Beta.Health Denmark","award":["2024-1792"],"award-info":[{"award-number":["2024-1792"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["EJNMMI Phys"],"DOI":"10.1186\/s40658-026-00917-w","type":"journal-article","created":{"date-parts":[[2026,7,15]],"date-time":"2026-07-15T03:47:23Z","timestamp":1784087243000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["MlPET: a localized neural network approach for probabilistic post-reconstruction PET image analysis using informed priors"],"prefix":"10.1186","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4529-0112","authenticated-orcid":false,"given":"Thomas Mejer","family":"Hansen","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9936-1636","authenticated-orcid":false,"given":"Nana Louise","family":"Christensen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0431-2522","authenticated-orcid":false,"given":"Mikkel Holm","family":"Vendelbo","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,7,15]]},"reference":[{"issue":"1","key":"917_CR1","doi-asserted-by":"publisher","first-page":"36","DOI":"10.1186\/s40658-022-00465-z","volume":"9","author":"G Bonardel","year":"2022","unstructured":"Bonardel G, Dupont A, Decazes P, Queneau M, Modzelewski R, Coulot J, et al. Clinical and phantom validation of a deep learning based denoising algorithm for F-18-FDG PET images from lower detection counting in comparison with the standard acquisition. EJNMMI Phys. 2022;9(1):36.","journal-title":"EJNMMI Phys"},{"issue":"1","key":"917_CR2","doi-asserted-by":"publisher","first-page":"88","DOI":"10.2967\/jnumed.108.054205","volume":"50","author":"S Ben-Haim","year":"2009","unstructured":"Ben-Haim S, Ell P. 18F-FDG PET and PET\/CT in the evaluation of cancer treatment response. J Nucl Med. 2009;50(1):88\u201399.","journal-title":"J Nucl Med"},{"issue":"4","key":"917_CR3","doi-asserted-by":"publisher","first-page":"333","DOI":"10.1109\/TRPMS.2023.3349194","volume":"8","author":"A Bousse","year":"2024","unstructured":"Bousse A, Kandarpa VSS, Shi K, Gong K, Lee JS, Liu C, et al. A review on low-dose emission tomography post-reconstruction denoising with neural network approaches. IEEE Trans Rad Plasma Med Sci. 2024;8(4):333\u201347.","journal-title":"IEEE Trans Rad Plasma Med Sci"},{"issue":"20","key":"917_CR4","doi-asserted-by":"publisher","first-page":"6681","DOI":"10.1088\/0031-9155\/57\/20\/6681","volume":"57","author":"A Bousse","year":"2012","unstructured":"Bousse A, Pedemonte S, Thomas BA, Erlandsson K, Ourselin S, Arridge S, et al. Markov random field and gaussian mixture for segmented MRI-based partial volume correction in pet. Phys Med Biol. 2012;57(20):6681.","journal-title":"Phys Med Biol"},{"key":"917_CR5","doi-asserted-by":"publisher","unstructured":"Bailey DL, Townsend DW, Valk PE, Maisey MN. (eds.): Positron Emission Tomography: Basic Sciences. Springer, London,2005. https:\/\/doi.org\/10.1007\/b136169","DOI":"10.1007\/b136169"},{"issue":"5","key":"917_CR6","doi-asserted-by":"publisher","first-page":"833","DOI":"10.1088\/0031-9155\/39\/5\/004","volume":"39","author":"HH Barrett","year":"1994","unstructured":"Barrett HH, Wilson DW, Tsui BM. Noise properties of the em. I. algorithm theory. Phys Med Biol. 1994;39(5):833.","journal-title":"Phys Med Biol"},{"issue":"13","key":"917_CR7","doi-asserted-by":"publisher","first-page":"2780","DOI":"10.1007\/s00259-019-04468-4","volume":"46","author":"J Cui","year":"2019","unstructured":"Cui J, Gong K, Guo N, Wu C, Meng X, Kim K, et al. Pet image denoising using unsupervised deep learning. Eur J Nucl Med Mol Imag. 2019;46(13):2780\u20139.","journal-title":"Eur J Nucl Med Mol Imag"},{"key":"917_CR8","unstructured":"Carrillo H, Millardet M, Carlier T, Mateus D. Low-count pet image reconstruction with bayesian inference over a deep prior. In: Medical Imaging 2021: Image Processing, 2021:11596;227\u201335 (SPIE)."},{"issue":"12","key":"917_CR9","doi-asserted-by":"publisher","first-page":"81390","DOI":"10.1371\/journal.pone.0081390","volume":"8","author":"J Dutta","year":"2013","unstructured":"Dutta J, Leahy RM, Li Q. Non-local means denoising of dynamic PET images. PLoS ONE. 2013;8(12):81390.","journal-title":"PLoS ONE"},{"issue":"7","key":"917_CR10","doi-asserted-by":"publisher","first-page":"1643","DOI":"10.1109\/TMI.2018.2886050","volume":"38","author":"M Filipovi\u0107","year":"2018","unstructured":"Filipovi\u0107 M, Barat E, Dautremer T, Comtat C, Stute S. Pet reconstruction of the posterior image probability, including multimodal images. IEEE Trans Med Imag. 2018;38(7):1643\u201354.","journal-title":"IEEE Trans Med Imag"},{"issue":"3","key":"917_CR11","doi-asserted-by":"publisher","first-page":"480","DOI":"10.2967\/jnumed.107.047787","volume":"49","author":"JW Fletcher","year":"2008","unstructured":"Fletcher JW, Djulbegovic B, Soares HP, Siegel BA, Lowe VJ, Lyman GH, et al. Recommendations on the use of 18F-FDG PET in oncology. J Nucl Med. 2008;49(3):480\u2013508.","journal-title":"J Nucl Med"},{"issue":"2","key":"917_CR12","doi-asserted-by":"publisher","first-page":"153","DOI":"10.1109\/TRPMS.2018.2877644","volume":"3","author":"K Gong","year":"2019","unstructured":"Gong K, Guan J, Liu C-C, Qi J. PET image denoising using a deep neural network through fine tuning. IEEE Trans Radiat Plasma Med Sci. 2019;3(2):153\u201361. https:\/\/doi.org\/10.1109\/TRPMS.2018.2877644.","journal-title":"IEEE Trans Radiat Plasma Med Sci"},{"key":"917_CR13","doi-asserted-by":"publisher","first-page":"496","DOI":"10.1093\/oso\/9780195115383.001.0001","volume-title":"Geostatistics for natural resources evaluation. Applied geostatistics series","author":"P Goovaerts","year":"1997","unstructured":"Goovaerts P. Geostatistics for natural resources evaluation. Applied geostatistics series. New York: Oxford University Press; 1997. p. 496."},{"issue":"1","key":"917_CR14","doi-asserted-by":"publisher","first-page":"543","DOI":"10.1093\/gji\/ggaa491","volume":"224","author":"TM Hansen","year":"2021","unstructured":"Hansen TM. Efficient probabilistic inversion using the rejection sampler\u2013exemplified on airborne EM data. Geophys J Int. 2021;224(1):543\u201357. https:\/\/doi.org\/10.1093\/gji\/ggaa491.","journal-title":"Geophys J Int"},{"issue":"11","key":"917_CR15","doi-asserted-by":"publisher","first-page":"2022","DOI":"10.1029\/2022JB024703","volume":"127","author":"TM Hansen","year":"2022","unstructured":"Hansen TM, Finlay CC. Use of machine learning to estimate statistics of the posterior distribution in probabilistic inverse problems \u2013 an application to airborne EM data. J Geophys Res Solid Earth. 2022;127(11):2022\u2013024703. https:\/\/doi.org\/10.1029\/2022JB024703.","journal-title":"J Geophys Res Solid Earth"},{"key":"917_CR16","doi-asserted-by":"crossref","unstructured":"Hashimoto F, Gong K. PET image reconstruction using deep diffusion image prior; 2025. arXiv:2507.15078.","DOI":"10.1109\/NSS\/MIC\/RTSD57106.2025.11287215"},{"issue":"3","key":"917_CR17","doi-asserted-by":"publisher","first-page":"299","DOI":"10.1097\/00004728-197906000-00001","volume":"3","author":"EJ Hoffman","year":"1979","unstructured":"Hoffman EJ, Huang S-C, Phelps ME. Quantitation in positron emission computed tomography: 1. effect of object size. J Comput Assist Tomogr. 1979;3(3):299\u2013308.","journal-title":"J Comput Assist Tomogr"},{"issue":"4","key":"917_CR18","doi-asserted-by":"publisher","first-page":"601","DOI":"10.1109\/42.363108","volume":"13","author":"HM Hudson","year":"1994","unstructured":"Hudson HM, Larkin RS. Accelerated image reconstruction using ordered subsets of projection data. IEEE Trans Med Imag. 1994;13(4):601\u20139.","journal-title":"IEEE Trans Med Imag"},{"issue":"1","key":"917_CR19","doi-asserted-by":"publisher","first-page":"2","DOI":"10.1186\/s41824-021-00096-0","volume":"5","author":"C Hornnes","year":"2021","unstructured":"Hornnes C, Loft A, H\u00f8jgaard L, Andersen FL. The effect of reduced scan time on response assessment fdg-pet\/CT imaging using deauville score in patients with lymphoma. European J Hybrid Imag. 2021;5(1):2.","journal-title":"European J Hybrid Imag"},{"key":"917_CR20","doi-asserted-by":"publisher","first-page":"1028928","DOI":"10.3389\/fnume.2022.1028928","volume":"2","author":"TM Hansen","year":"2023","unstructured":"Hansen TM, Mosegaard K, Holm S, Andersen FL, Fischer BM, Hansen AE. Probabilistic deconvolution of PET images using informed priors. Front Nucl Med. 2023;2:1028928. https:\/\/doi.org\/10.3389\/fnume.2022.1028928.","journal-title":"Front Nucl Med"},{"key":"917_CR21","doi-asserted-by":"publisher","first-page":"96594","DOI":"10.1109\/ACCESS.2019.2929230","volume":"7","author":"F Hashimoto","year":"2019","unstructured":"Hashimoto F, Ohba H, Ote K, Teramoto A, Tsukada H. Dynamic PET image denoising using deep convolutional neural networks without prior training datasets. IEEE Access. 2019;7:96594\u2013603.","journal-title":"IEEE Access"},{"issue":"1","key":"917_CR22","doi-asserted-by":"publisher","first-page":"24","DOI":"10.1007\/s12194-024-00780-3","volume":"17","author":"F Hashimoto","year":"2024","unstructured":"Hashimoto F, Onishi Y, Ote K, Tashima H, Reader AJ, Yamaya T. Deep learning-based PET image denoising and reconstruction: a review. Radiol Phys Technol. 2024;17(1):24\u201346.","journal-title":"Radiol Phys Technol"},{"issue":"18","key":"917_CR23","doi-asserted-by":"publisher","first-page":"6668","DOI":"10.1088\/0031-9155\/61\/18\/6668","volume":"61","author":"W Hutchcroft","year":"2016","unstructured":"Hutchcroft W, Wang G, Chen KT, Catana C, Qi J. Anatomically-aided PET reconstruction using the kernel method. Phys Med Biol. 2016;61(18):6668.","journal-title":"Phys Med Biol"},{"issue":"1","key":"917_CR24","doi-asserted-by":"publisher","first-page":"7","DOI":"10.1186\/s40644-025-00823-x","volume":"25","author":"J Ingbritsen","year":"2025","unstructured":"Ingbritsen J, Callahan J, Morgan H, Munro M, Ware RE, Hicks RJ. Optimisation of low and ultra-low dose scanning protocols for ultra-extended field of view PET in a real-world clinical setting. Cancer Imag. 2025;25(1):7.","journal-title":"Cancer Imag"},{"key":"917_CR25","unstructured":"Kingma DP, Ba J. Adam: A method for stochastic optimization. 2017, arXiv:https:\/\/arxiv.org\/abs\/1412.6980"},{"key":"917_CR26","doi-asserted-by":"publisher","first-page":"1415058","DOI":"10.3389\/fmed.2024.1415058","volume":"11","author":"I Kruzhilov","year":"2024","unstructured":"Kruzhilov I, Kudin S, Vetoshkin L, Sokolova E, Kokh V. Whole-body PET image denoising for reduced acquisition time. Front Med. 2024;11:1415058.","journal-title":"Front Med"},{"issue":"3","key":"917_CR27","doi-asserted-by":"publisher","first-page":"462","DOI":"10.2967\/jnumed.107.044834","volume":"49","author":"JS Karp","year":"2008","unstructured":"Karp JS, Surti S, Daube-Witherspoon ME, Muehllehner G. Benefit of time-of-flight in pet: experimental and clinical results. J Nucl Med. 2008;49(3):462\u201370.","journal-title":"J Nucl Med"},{"issue":"11","key":"917_CR28","doi-asserted-by":"publisher","first-page":"3512","DOI":"10.1109\/TMI.2020.2998480","volume":"39","author":"H Lim","year":"2020","unstructured":"Lim H, Chun IY, Dewaraja YK, Fessler JA. Improved low-count quantitative pet reconstruction with an iterative neural network. IEEE Trans Med Imag. 2020;39(11):3512\u201322.","journal-title":"IEEE Trans Med Imag"},{"issue":"6","key":"917_CR29","doi-asserted-by":"publisher","first-page":"1715","DOI":"10.1007\/s11517-025-03296-z","volume":"63","author":"MF Levital","year":"2025","unstructured":"Levital MF, Khawaled S, Kennedy JA, Freiman M. Non-parametric Bayesian deep learning approach for whole-body low-dose pet reconstruction and uncertainty assessment. Med Biol Eng Comput. 2025;63(6):1715\u201330.","journal-title":"Med Biol Eng Comput"},{"issue":"4","key":"917_CR30","doi-asserted-by":"publisher","first-page":"553","DOI":"10.1016\/j.cpet.2021.06.005","volume":"16","author":"J Liu","year":"2021","unstructured":"Liu J, Malekzadeh M, Mirian N, Song T-A, Liu C, Dutta J. Artificial intelligence-based image enhancement in pet imaging: noise reduction and resolution enhancement. PET Clinics. 2021;16(4):553.","journal-title":"PET Clinics"},{"issue":"16","key":"917_CR31","doi-asserted-by":"publisher","first-page":"165019","DOI":"10.1088\/1361-6560\/ab3242","volume":"64","author":"W Lu","year":"2019","unstructured":"Lu W, Onofrey JA, Lu Y, Shi L, Ma T, Liu Y, et al. An investigation of quantitative accuracy for deep learning based denoising in oncological pet. Phys Med Biol. 2019;64(16):165019.","journal-title":"Phys Med Biol"},{"issue":"B7","key":"917_CR32","doi-asserted-by":"publisher","first-page":"12431","DOI":"10.1029\/94JB03097","volume":"100","author":"K Mosegaard","year":"1995","unstructured":"Mosegaard K, Tarantola A. Monte Carlo sampling of solutions to inverse problems. J Geophys Res Solid Earth. 1995;100(B7):12431\u201347.","journal-title":"J Geophys Res Solid Earth"},{"issue":"1","key":"917_CR33","doi-asserted-by":"publisher","first-page":"5","DOI":"10.1186\/s40658-016-0169-9","volume":"4","author":"O Munk","year":"2017","unstructured":"Munk O, Tolbod L, Hansen S, Bogsrud T. Point-spread function reconstructed PET images of sub-centimeter lesions are not quantitative. EJNMMI Phys. 2017;4(1):5.","journal-title":"EJNMMI Phys"},{"issue":"247","key":"917_CR34","doi-asserted-by":"publisher","first-page":"335","DOI":"10.1080\/01621459.1949.10483310","volume":"44","author":"N Metropolis","year":"1949","unstructured":"Metropolis N, Ulam S. The Monte Carlo Method. J Am Stat Assoc. 1949;44(247):335\u201341.","journal-title":"J Am Stat Assoc"},{"key":"917_CR35","unstructured":"National Electrical Manufacturers Association: Performance Measurements of Positron Emission Tomographs (PETs). In: NEMA. NEMA Standards Publication NU 2. NEMA, Rosslyn, VA; 2018."},{"issue":"1","key":"917_CR36","doi-asserted-by":"publisher","first-page":"98","DOI":"10.1186\/s40658-014-0098-4","volume":"1","author":"J Nuyts","year":"2014","unstructured":"Nuyts J. Unconstrained image reconstruction with resolution modelling does not have a unique solution. EJNMMI Phys. 2014;1(1):98. https:\/\/doi.org\/10.1186\/s40658-014-0098-4.","journal-title":"EJNMMI Phys"},{"issue":"8","key":"917_CR37","doi-asserted-by":"publisher","first-page":"3555","DOI":"10.1002\/mp.13626","volume":"46","author":"J Ouyang","year":"2019","unstructured":"Ouyang J, Chen KT, Gong E, Pauly J, Zaharchuk G. Ultra-low-dose PET reconstruction using generative adversarial network with feature matching and task-specific perceptual loss. Med Phys. 2019;46(8):3555\u201364.","journal-title":"Med Phys"},{"key":"917_CR38","doi-asserted-by":"publisher","first-page":"102226","DOI":"10.1016\/j.media.2021.102226","volume":"74","author":"Y Onishi","year":"2021","unstructured":"Onishi Y, Hashimoto F, Ote K, Ohba H, Ota R, Yoshikawa E, et al. Anatomical-guided attention enhances unsupervised PET image denoising performance. Med Image Anal. 2021;74:102226.","journal-title":"Med Image Anal"},{"issue":"7","key":"917_CR39","doi-asserted-by":"publisher","first-page":"907","DOI":"10.1109\/TMI.2006.876171","volume":"25","author":"VY Panin","year":"2006","unstructured":"Panin VY, Kehren F, Michel C, Casey M. Fully 3-D PET reconstruction with system matrix derived from point source measurements. IEEE Trans Med Imag. 2006;25(7):907\u201321.","journal-title":"IEEE Trans Med Imag"},{"issue":"7","key":"917_CR40","doi-asserted-by":"publisher","first-page":"629","DOI":"10.1109\/34.56205","volume":"12","author":"P Perona","year":"1990","unstructured":"Perona P, Malik J. Scale-space and edge detection using anisotropic diffusion. IEEE Trans Patt Anal Mach Intell. 1990;12(7):629\u201339.","journal-title":"IEEE Trans Patt Anal Mach Intell"},{"issue":"3","key":"917_CR41","doi-asserted-by":"publisher","first-page":"476","DOI":"10.2967\/jnumed.121.261972","volume":"63","author":"GA Prenosil","year":"2022","unstructured":"Prenosil GA, Sari H, F\u00fcrstner M, Afshar-Oromieh A, Shi K, Rominger A, et al. Performance characteristics of the Biograph Vision Quadra PET\/CT system with a long axial field of view using the NEMA NU 2\u20132018 standard. J Nucl Med. 2022;63(3):476\u201384.","journal-title":"J Nucl Med"},{"issue":"21","key":"917_CR42","doi-asserted-by":"publisher","first-page":"3505","DOI":"10.1088\/0031-9155\/48\/21\/004","volume":"48","author":"J Qi","year":"2003","unstructured":"Qi J. A unified noise analysis for iterative image estimation. Phys Med Biol. 2003;48(21):3505.","journal-title":"Phys Med Biol"},{"issue":"6Part1","key":"917_CR43","doi-asserted-by":"publisher","first-page":"064301","DOI":"10.1118\/1.4800806","volume":"40","author":"A Rahmim","year":"2013","unstructured":"Rahmim A, Qi J, Sossi V. Resolution modeling in PET imaging: theory, practice, benefits, and pitfalls. Med Phys. 2013;40(6Part1):064301.","journal-title":"Med Phys"},{"issue":"11","key":"917_CR44","doi-asserted-by":"publisher","first-page":"3849","DOI":"10.1088\/0031-9155\/58\/11\/3849","volume":"58","author":"S Stute","year":"2013","unstructured":"Stute S, Comtat C. Practical considerations for image-based PSF and blobs reconstruction in pet. Phys Med Biol. 2013;58(11):3849.","journal-title":"Phys Med Biol"},{"key":"917_CR45","doi-asserted-by":"publisher","first-page":"83","DOI":"10.1016\/j.neunet.2020.01.029","volume":"125","author":"T-A Song","year":"2020","unstructured":"Song T-A, Chowdhury SR, Yang F, Dutta J. Pet image super-resolution using generative adversarial networks. Neural Netw. 2020;125:83\u201391.","journal-title":"Neural Netw"},{"key":"917_CR46","doi-asserted-by":"crossref","unstructured":"Singh I, Denker A, Barbano R, Kereta Z, Jin B, Thielemans K, et al. Score-based generative models for pet image reconstruction. Mach Learn Biomed Imag. 2024; https:\/\/doi.org\/10.59275\/j.melba.2024-5d51","DOI":"10.59275\/j.melba.2024-5d51"},{"key":"917_CR47","unstructured":"Sun Y, Mawlawi O. Posterior-mean denoising diffusion model for realistic pet image reconstruction. 2025, 2503."},{"issue":"10","key":"917_CR48","doi-asserted-by":"publisher","first-page":"4928","DOI":"10.1002\/mp.14402","volume":"47","author":"K Spuhler","year":"2020","unstructured":"Spuhler K, Serrano-Sosa M, Cattell R, DeLorenzo C, Huang C. Full-count pet recovery from low-count image using a dilated convolutional neural network. Med Phys. 2020;47(10):4928\u201338.","journal-title":"Med Phys"},{"issue":"2","key":"917_CR49","doi-asserted-by":"publisher","first-page":"113","DOI":"10.1109\/TMI.1982.4307558","volume":"1","author":"LA Shepp","year":"1982","unstructured":"Shepp LA, Vardi Y. Maximum likelihood reconstruction for emission tomography. IEEE Trans Med Imag. 1982;1(2):113\u201322.","journal-title":"IEEE Trans Med Imag"},{"issue":"21","key":"917_CR50","doi-asserted-by":"publisher","first-page":"214002","DOI":"10.1088\/1361-6560\/ac30a0","volume":"66","author":"T-A Song","year":"2021","unstructured":"Song T-A, Yang F, Dutta J. Noise2void: unsupervised denoising of PET images. Phys Med Biol. 2021;66(21):214002.","journal-title":"Phys Med Biol"},{"key":"917_CR51","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s13550-020-00695-1","volume":"10","author":"J Schaefferkoetter","year":"2020","unstructured":"Schaefferkoetter J, Yan J, Ortega C, Sertic A, Lechtman E, Eshet Y, et al. Convolutional neural networks for improving image quality with noisy PET data. EJNMMI Res. 2020;10:1\u201311.","journal-title":"EJNMMI Res"},{"issue":"2","key":"917_CR52","first-page":"134","volume":"5","author":"Y Tsutsui","year":"2017","unstructured":"Tsutsui Y, Awamoto S, Himuro K, Umezu Y, Baba S, Sasaki M. Edge artifacts in point spread function-based pet reconstruction in relation to object size and reconstruction parameters. Asia Oceania J Nucl Med Biol. 2017;5(2):134.","journal-title":"Asia Oceania J Nucl Med Biol"},{"issue":"5","key":"917_CR53","doi-asserted-by":"publisher","first-page":"1453","DOI":"10.1088\/0031-9155\/55\/5\/013","volume":"55","author":"S Tong","year":"2010","unstructured":"Tong S, Alessio A, Kinahan P. Noise and signal properties in PSF-based fully 3d PET image reconstruction: an experimental evaluation. Phys Med Biol. 2010;55(5):1453.","journal-title":"Phys Med Biol"},{"key":"917_CR54","doi-asserted-by":"publisher","DOI":"10.1137\/1.9780898717921","volume-title":"Inverse problem theory and methods for model parameter estimation","author":"A Tarantola","year":"2005","unstructured":"Tarantola A. Inverse problem theory and methods for model parameter estimation. Philadelphia, PA: SIAM; 2005."},{"issue":"9","key":"917_CR55","doi-asserted-by":"publisher","first-page":"1447","DOI":"10.2967\/jnumed.115.159301","volume":"56","author":"EJ Teoh","year":"2015","unstructured":"Teoh EJ, McGowan DR, Macpherson RE, Bradley KM, Gleeson FV. Phantom and clinical evaluation of the Bayesian penalized likelihood reconstruction algorithm q. clear on an lyso PET\/CT system. J Nucl Med. 2015;56(9):1447\u201352.","journal-title":"J Nucl Med"},{"key":"917_CR56","unstructured":"Vla\u0161i\u0107 T, Matuli\u0107 T, Ser\u0161i\u0107 D. Estimating uncertainty in pet image reconstruction via deep posterior sampling. 2023, arXiv:2306.04664."},{"key":"917_CR57","doi-asserted-by":"crossref","unstructured":"Webber G, Hammers A, King AP, Reader AJ. Supervised diffusion-model-based PETimage reconstruction. In: International Conference on Medical Image Computing and Computer-Assisted Intervention. Springer; 2025. p. 562\u201371.","DOI":"10.1007\/978-3-032-04965-0_53"},{"issue":"4","key":"917_CR58","doi-asserted-by":"publisher","first-page":"1198","DOI":"10.1109\/23.256736","volume":"40","author":"DW Wilson","year":"1993","unstructured":"Wilson DW, Tsui BM. Noise properties of filtered-backprojection and ML-EM reconstructed emission tomographic images. IEEE Trans Nucl Sci. 1993;40(4):1198\u2013203.","journal-title":"IEEE Trans Nucl Sci"},{"issue":"5","key":"917_CR59","doi-asserted-by":"publisher","first-page":"847","DOI":"10.1088\/0031-9155\/39\/5\/005","volume":"39","author":"DW Wilson","year":"1994","unstructured":"Wilson DW, Tsui BM, Barrett HH. Noise properties of the em algorithm. II. monte carlo simulations. Phys Med Biol. 1994;39(5):847.","journal-title":"Phys Med Biol"},{"key":"917_CR60","doi-asserted-by":"publisher","first-page":"550","DOI":"10.1016\/j.neuroimage.2018.03.045","volume":"174","author":"Y Wang","year":"2018","unstructured":"Wang Y, Yu B, Wang L, Zu C, Lalush DS, Lin W, et al. 3d conditional generative adversarial networks for high-quality PET image estimation at low dose. Neuroimage. 2018;174:550\u201362.","journal-title":"Neuroimage"},{"key":"917_CR61","unstructured":"Xie H, Gan W, Zhou B, Chen X, Liu Q, Guo X, Guo L, An H, Kamilov US, Wang G, et al. Ddpet-3d: Dose-aware diffusion model for 3d ultra low-dose pet imaging, 2023. arXiv:2311.04248"},{"key":"917_CR62","doi-asserted-by":"publisher","first-page":"101770","DOI":"10.1016\/j.media.2020.101770","volume":"65","author":"L Zhou","year":"2020","unstructured":"Zhou L, Schaefferkoetter JD, Tham IW, Huang G, Yan J. Supervised learning with cyclegan for low-dose fdg pet image denoising. Med Image Anal. 2020;65:101770.","journal-title":"Med Image Anal"}],"container-title":["EJNMMI Physics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/article\/10.1186\/s40658-026-00917-w","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s40658-026-00917-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s40658-026-00917-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,10,19]],"date-time":"2026-10-19T18:02:25Z","timestamp":1792432945000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/article\/10.1186\/s40658-026-00917-w"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,7,15]]},"references-count":62,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2026,12]]}},"alternative-id":["917"],"URL":"https:\/\/doi.org\/10.1186\/s40658-026-00917-w","relation":{},"ISSN":["2197-7364"],"issn-type":[{"value":"2197-7364","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,7,15]]},"assertion":[{"value":"23 December 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"1 July 2026","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"15 July 2026","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"The study was approved by the Danish National Committee on Health Research Ethics (Case no. 2313915).","order":1,"name":"Ethics","label":"Ethics approval and consent to participate","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"Thomas Mejer Hansen is the CEO of NoloSight ApS, a company developing AI-based solutions for medical imaging. The other authors declare that they have no conflict of interest.","order":2,"name":"Ethics","label":"Competing interests","group":{"name":"EthicsHeading","label":"Declarations"}}],"article-number":"103"}}