{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,19]],"date-time":"2026-03-19T09:17:13Z","timestamp":1773911833635,"version":"3.50.1"},"reference-count":52,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,3,1]],"date-time":"2026-03-01T00:00:00Z","timestamp":1772323200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,3,1]],"date-time":"2026-03-01T00:00:00Z","timestamp":1772323200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2025,11,28]],"date-time":"2025-11-28T00:00:00Z","timestamp":1764288000000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100017636","name":"Cancer Centre Amsterdam","doi-asserted-by":"publisher","award":["CCA 2020-7-01"],"award-info":[{"award-number":["CCA 2020-7-01"]}],"id":[{"id":"10.13039\/501100017636","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003246","name":"Dutch Research Council","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100003246","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":[[2026,3]]},"DOI":"10.1016\/j.media.2025.103881","type":"journal-article","created":{"date-parts":[[2025,12,3]],"date-time":"2025-12-03T17:14:25Z","timestamp":1764782065000},"page":"103881","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"special_numbering":"C","title":["Uncertainty estimates in pharmacokinetic modelling of DCE-MRI"],"prefix":"10.1016","volume":"109","author":[{"ORCID":"https:\/\/orcid.org\/0009-0009-6917-8679","authenticated-orcid":false,"given":"Jonas M.","family":"Van Elburg","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0009-0008-6601-7135","authenticated-orcid":false,"given":"Natalia V.","family":"Korobova","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0888-0906","authenticated-orcid":false,"given":"Mohammad M.","family":"Islam","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6748-4810","authenticated-orcid":false,"given":"Marian A.","family":"Troelstra","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1750-6617","authenticated-orcid":false,"given":"Oliver J.","family":"Gurney-Champion","sequence":"additional","affiliation":[]}],"member":"78","reference":[{"issue":"9","key":"10.1016\/j.media.2025.103881_bib0001","doi-asserted-by":"crossref","DOI":"10.1088\/0031-9155\/50\/9\/N02","article-title":"The use of the levenberg\u2013marquardt curve-fitting algorithm in pharmacokinetic modelling of DCE-MRI data","volume":"50","author":"Ahearn","year":"2005","journal-title":"Phys. Med. Biol."},{"issue":"1","key":"10.1016\/j.media.2025.103881_bib0002","doi-asserted-by":"crossref","first-page":"1786","DOI":"10.1038\/s41598-024-83306-6","article-title":"Probabilistic nested model selection in pharmacokinetic analysis of DCE-MRI data in animal model of cerebral tumor","volume":"15","author":"Bagher-Ebadian","year":"2025","journal-title":"Sci. Rep."},{"key":"10.1016\/j.media.2025.103881_bib0003","series-title":"Handbook of MRI pulse sequences","author":"Bernstein","year":"2004"},{"issue":"5","key":"10.1016\/j.media.2025.103881_bib0004","doi-asserted-by":"crossref","first-page":"1712","DOI":"10.1109\/TMI.2019.2953901","article-title":"Efficient DCE-MRI parameter and uncertainty estimation using a neural network","volume":"39","author":"Bliesener","year":"2019","journal-title":"IEEE Trans. Med. Imag."},{"issue":"4","key":"10.1016\/j.media.2025.103881_bib0005","doi-asserted-by":"crossref","first-page":"597","DOI":"10.1016\/j.mric.2008.07.001","article-title":"Assessment of renal function with dynamic contrast-enhanced MR imaging","volume":"16","author":"Bokacheva","year":"2008","journal-title":"Magn. Reson. Imag. Clin. N Am."},{"issue":"3","key":"10.1016\/j.media.2025.103881_bib0006","doi-asserted-by":"crossref","first-page":"601","DOI":"10.1002\/mrm.10080","article-title":"Uncertainty in the analysis of tracer kinetics using dynamic contrast-enhanced t1-weighted MRI","volume":"47","author":"Buckley","year":"2002","journal-title":"Magnetic Resonance Med.: Off. J. Int. Soc. Magnetic Resonance Med."},{"issue":"3","key":"10.1016\/j.media.2025.103881_bib0007","doi-asserted-by":"crossref","first-page":"652","DOI":"10.1148\/radiol.2303021331","article-title":"Mr imaging relaxation times of abdominal and pelvic tissues measured in vivo at 3.0 t: preliminary results","volume":"230","author":"De Bazelaire","year":"2004","journal-title":"Radiology"},{"issue":"21","key":"10.1016\/j.media.2025.103881_bib0008","doi-asserted-by":"crossref","DOI":"10.1088\/1361-6560\/ad0284","article-title":"Unified bayesian network for uncertainty quantification of physiological parameters in dynamic contrast enhanced (DCE) MRI of the liver","volume":"68","author":"Dejene","year":"2023","journal-title":"Phys. Med. Biol."},{"issue":"2","key":"10.1016\/j.media.2025.103881_bib0009","doi-asserted-by":"crossref","first-page":"105","DOI":"10.1016\/j.strusafe.2008.06.020","article-title":"Aleatory or epistemic? does it matter?","volume":"31","author":"Der Kiureghian","year":"2009","journal-title":"Struct. Saf."},{"issue":"3","key":"10.1016\/j.media.2025.103881_bib0010","doi-asserted-by":"crossref","first-page":"1329","DOI":"10.1002\/mrm.26189","article-title":"Are complex DCE-MRI models supported by clinical data?","volume":"77","author":"Duan","year":"2017","journal-title":"Magn. Reson. Med."},{"issue":"2","key":"10.1016\/j.media.2025.103881_bib0011","doi-asserted-by":"crossref","first-page":"775","DOI":"10.1002\/mrm.25665","article-title":"Xd-grasp: golden-angle radial mri with reconstruction of extra motion-state dimensions using compressed sensing","volume":"75","author":"Feng","year":"2016","journal-title":"Magn. Reson. Med."},{"key":"10.1016\/j.media.2025.103881_bib0012","series-title":"International Conference on Machine Learning","first-page":"1050","article-title":"Dropout as a bayesian approximation: representing model uncertainty in deep learning","author":"Gal","year":"2016"},{"issue":"1","key":"10.1016\/j.media.2025.103881_bib0013","doi-asserted-by":"crossref","first-page":"450","DOI":"10.1002\/mrm.28117","article-title":"DeepCEST 3t: robust MRI parameter determination and uncertainty quantification with neural networks-application to CEST imaging of the human brain at 3t","volume":"84","author":"Glang","year":"2020","journal-title":"Magn. Reson. Med."},{"issue":"5","key":"10.1016\/j.media.2025.103881_bib0014","doi-asserted-by":"crossref","first-page":"2804","DOI":"10.1002\/mrm.26904","article-title":"Joint arterial input function and tracer kinetic parameter estimation from undersampled dynamic contrast-enhanced MRI using a model consistency constraint","volume":"79","author":"Guo","year":"2018","journal-title":"Magn. Reson. Med."},{"issue":"2","key":"10.1016\/j.media.2025.103881_bib0015","doi-asserted-by":"crossref","first-page":"932","DOI":"10.1002\/mrm.29014","article-title":"Training data distribution significantly impacts the estimation of tissue microstructure with machine learning","volume":"87","author":"Gyori","year":"2022","journal-title":"Magn. Reson. Med."},{"issue":"6","key":"10.1016\/j.media.2025.103881_bib0016","doi-asserted-by":"crossref","first-page":"991","DOI":"10.1002\/1522-2586(200012)12:6<991::AID-JMRI26>3.0.CO;2-1","article-title":"Simultaneous MRI measurement of blood flow, blood volume, and capillary permeability in mammary tumors using two different contrast agents","volume":"12","author":"Henderson","year":"2000","journal-title":"J. Magn. Reson. Imag."},{"issue":"1","key":"10.1016\/j.media.2025.103881_bib0017","doi-asserted-by":"crossref","first-page":"271","DOI":"10.1002\/mp.15372","article-title":"Disposable point-of-care portable perfusion phantom for quantitative DCE-MRI","volume":"49","author":"Holland","year":"2022","journal-title":"Med. Phys."},{"issue":"5","key":"10.1016\/j.media.2025.103881_bib0018","doi-asserted-by":"crossref","first-page":"1774","DOI":"10.1002\/mrm.29826","article-title":"Contrast-agent-based perfusion MRI code repository and testing framework: ISMRM open science initiative for perfusion imaging (OSIPI)","volume":"91","author":"van Houdt","year":"2024","journal-title":"Magn. Reson. Med."},{"issue":"3","key":"10.1016\/j.media.2025.103881_bib0019","doi-asserted-by":"crossref","first-page":"457","DOI":"10.1007\/s10994-021-05946-3","article-title":"Aleatoric and epistemic uncertainty in machine learning: an introduction to concepts and methods","volume":"110","author":"H\u00fcllermeier","year":"2021","journal-title":"Mach. Learn."},{"key":"10.1016\/j.media.2025.103881_bib0020","unstructured":"Isensee, F., Rokuss, M., Kr\u00e4mer, L., Dinkelacker, S., Ravindran, A., Stritzke, F., Hamm, B., Wald, T., Langenberg, M., Ulrich, C., et al., 2025. nninteractive: Redefining 3d promptable segmentation. arXiv preprint arXiv::2503.08373."},{"key":"10.1016\/j.media.2025.103881_bib0021","doi-asserted-by":"crossref","DOI":"10.7554\/eLife.101069.3","article-title":"Introducing \u03bcGUIDE for quantitative imaging via generalized uncertainty-driven inference using deep learning","volume":"13","author":"Jallais","year":"2024","journal-title":"Elife"},{"key":"10.1016\/j.media.2025.103881_bib0022","series-title":"2020IEEE 17Th International Symposium on Biomedical Imaging (ISBI)","first-page":"1450","article-title":"Arterial input function and tracer kinetic model-driven network for rapid inference of kinetic maps in dynamic contrast-enhanced MRI (AIF-TK-net)","author":"Kettelkamp","year":"2020"},{"key":"10.1016\/j.media.2025.103881_bib0023","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.mri.2018.02.005","article-title":"Repeatability and correlations of dynamic contrast enhanced and t2* MRI in patients with advanced pancreatic ductal adenocarcinoma","volume":"50","author":"Klaassen","year":"2018","journal-title":"Magn. Reson. Imag."},{"issue":"4","key":"10.1016\/j.media.2025.103881_bib0024","article-title":"Notes on the use of propagation of error formulas","volume":"70","author":"Ku","year":"1966","journal-title":"J. Res. Natl. Bur. Stand. (1934)"},{"key":"10.1016\/j.media.2025.103881_bib0025","unstructured":"Kuppens, D., Barbieri, S., van den, B. D., Schouten, P., Thoeny, H. C., Wennen, M., Gurney-Champion, O. J., 2024. Acquisition-independent deep learning for quantitative MRI parameter estimation using neural controlled differential equations. arXiv preprint arXiv::2412.20844."},{"key":"10.1016\/j.media.2025.103881_bib0026","article-title":"Simple and scalable predictive uncertainty estimation using deep ensembles","volume":"30","author":"Lakshminarayanan","year":"2017","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"10.1016\/j.media.2025.103881_bib0027","doi-asserted-by":"crossref","unstructured":"Laves, M.-H., Ihler, S., Fast, J. F., Kahrs, L. A., Ortmaier, T., 2021. Recalibration of aleatoric and epistemic regression uncertainty in medical imaging. arXiv preprint arXiv::2104.12376.","DOI":"10.59275\/j.melba.2021-a6fd"},{"key":"10.1016\/j.media.2025.103881_bib0028","doi-asserted-by":"crossref","first-page":"1451","DOI":"10.1007\/s00330-012-2446-x","article-title":"Imaging vascular function for early stage clinical trials using dynamic contrast-enhanced magnetic resonance imaging","volume":"22","author":"Leach","year":"2012","journal-title":"Eur. Radiol."},{"key":"10.1016\/j.media.2025.103881_bib0029","first-page":"50972","article-title":"Benchmarking uncertainty disentanglement: specialized uncertainties for specialized tasks","volume":"37","author":"Mucs\u00e1nyi","year":"2024","journal-title":"Adv. Neural Inf. Process. Syst."},{"issue":"4","key":"10.1016\/j.media.2025.103881_bib0030","doi-asserted-by":"crossref","first-page":"858","DOI":"10.1002\/mrm.20022","article-title":"Efficient method for calculating kinetic parameters using t1-weighted dynamic contrast-enhanced magnetic resonance imaging","volume":"51","author":"Murase","year":"2004","journal-title":"Magnet. Reson. Med.: Off. J. Int. Soc. Magnet. Resonance Med."},{"issue":"2","key":"10.1016\/j.media.2025.103881_bib0031","doi-asserted-by":"crossref","first-page":"906","DOI":"10.1021\/acs.jctc.8b00959","article-title":"Fast and accurate uncertainty estimation in chemical machine learning","volume":"15","author":"Musil","year":"2019","journal-title":"J. Chem. Theory Comput."},{"key":"10.1016\/j.media.2025.103881_bib0032","series-title":"Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition","first-page":"427","article-title":"Deep neural networks are easily fooled: high confidence predictions for unrecognizable images","author":"Nguyen","year":"2015"},{"key":"10.1016\/j.media.2025.103881_bib0033","series-title":"Proceedings of 1994 Ieee International Conference on Neural Networks (ICNN\u201994)","first-page":"55","article-title":"Estimating the mean and variance of the target probability distribution","volume":"Vol. 1","author":"Nix","year":"1994"},{"issue":"5","key":"10.1016\/j.media.2025.103881_bib0034","doi-asserted-by":"crossref","first-page":"1225","DOI":"10.1088\/0031-9155\/53\/5\/005","article-title":"Computationally efficient vascular input function models for quantitative kinetic modelling using DCE-MRI","volume":"53","author":"Orton","year":"2008","journal-title":"Phys. Med. Biol."},{"key":"10.1016\/j.media.2025.103881_bib0035","doi-asserted-by":"crossref","DOI":"10.1016\/j.media.2022.102512","article-title":"Deep learning DCE-MRI parameter estimation: application in pancreatic cancer","volume":"80","author":"Ottens","year":"2022","journal-title":"Med. Image Anal."},{"issue":"5","key":"10.1016\/j.media.2025.103881_bib0036","doi-asserted-by":"crossref","first-page":"993","DOI":"10.1002\/mrm.21066","article-title":"Experimentally-derived functional form for a population-averaged high-temporal-resolution arterial input function for dynamic contrast-enhanced MRI","volume":"56","author":"Parker","year":"2006","journal-title":"Magnet. Reson. Med.: Off. J. Int. Soc. Magnet. Resonance Med."},{"key":"10.1016\/j.media.2025.103881_bib0037","doi-asserted-by":"crossref","first-page":"259","DOI":"10.1016\/j.neuropharm.2017.10.034","article-title":"Mri measurements of blood-brain barrier function in dementia: a review of recent studies","volume":"134","author":"Raja","year":"2018","journal-title":"Neuropharmacology"},{"key":"10.1016\/j.media.2025.103881_bib0038","doi-asserted-by":"crossref","first-page":"1991","DOI":"10.1007\/s00330-015-4012-9","article-title":"Assessment of repeatability and treatment response in early phase clinical trials using DCE-MRI: comparison of parametric analysis using MR-and CT-derived arterial input functions","volume":"26","author":"Rata","year":"2016","journal-title":"Eur. Radiol."},{"issue":"5","key":"10.1016\/j.media.2025.103881_bib0039","doi-asserted-by":"crossref","first-page":"1803","DOI":"10.1002\/mrm.29909","article-title":"The ISMRM open science initiative for perfusion imaging (OSIPI): results from the OSIPI\u2013dynamic contrast-enhanced challenge","volume":"91","author":"Shalom","year":"2024","journal-title":"Magn. Reson. Med."},{"key":"10.1016\/j.media.2025.103881_bib0040","series-title":"2021IEEE 18Th International Symposium on Biomedical Imaging (ISBI)","first-page":"433","article-title":"Deep learning-based parameter mapping with uncertainty estimation for fat quantification using accelerated free-breathing radial MRI","author":"Shih","year":"2021"},{"key":"10.1016\/j.media.2025.103881_bib0041","doi-asserted-by":"crossref","unstructured":"Sluijterman, L., Cator, E., Heskes, T., 2023. Optimal training of mean variance estimation neural networks. arXiv preprint arXiv::2302.08875.","DOI":"10.1016\/j.neucom.2024.127929"},{"issue":"8","key":"10.1016\/j.media.2025.103881_bib0042","doi-asserted-by":"crossref","first-page":"1004","DOI":"10.1002\/nbm.2940","article-title":"Classic models for dynamic contrast-enhanced MRI","volume":"26","author":"Sourbron","year":"2013","journal-title":"NMR Biomed."},{"issue":"1","key":"10.1016\/j.media.2025.103881_bib0043","doi-asserted-by":"crossref","first-page":"91","DOI":"10.1002\/jmri.1880070113","article-title":"Modeling tracer kinetics in dynamic gd-DTPA MR imaging","volume":"7","author":"Tofts","year":"1997","journal-title":"J. Magn. Reson. Imag."},{"issue":"3","key":"10.1016\/j.media.2025.103881_bib0044","doi-asserted-by":"crossref","first-page":"223","DOI":"10.1002\/(SICI)1522-2586(199909)10:3<223::AID-JMRI2>3.0.CO;2-S","article-title":"Estimating kinetic parameters from dynamic contrast-enhanced t1-weighted MRI of a diffusable tracer: standardized quantities and symbols","volume":"10","author":"Tofts","year":"1999","journal-title":"J. Magn. Reson. Imag.: Off. J. Int. Soc. Magnet. Reson. Med."},{"key":"10.1016\/j.media.2025.103881_bib0045","doi-asserted-by":"crossref","first-page":"1147","DOI":"10.3389\/fneur.2018.01147","article-title":"Convolutional neural networks for direct inference of pharmacokinetic parameters: application to stroke dynamic contrast-enhanced MRI","volume":"9","author":"Ulas","year":"2019","journal-title":"Front. Neurol."},{"key":"10.1016\/j.media.2025.103881_bib0046","unstructured":"Verdoja, F., Kyrki, V., 2020. Notes on the behavior of mc dropout. arXiv preprint arXiv::2008.02627."},{"issue":"5","key":"10.1016\/j.media.2025.103881_bib0047","doi-asserted-by":"crossref","first-page":"433","DOI":"10.1212\/WNL.0000000000003566","article-title":"Dce-mri blood\u2013brain barrier assessment in acute ischemic stroke","volume":"88","author":"Villringer","year":"2017","journal-title":"Neurology"},{"issue":"3","key":"10.1016\/j.media.2025.103881_bib0048","doi-asserted-by":"crossref","DOI":"10.1029\/2005WR004804","article-title":"Confidence region estimation techniques for nonlinear regression in groundwater flow: three case studies","volume":"43","author":"Vugrin","year":"2007","journal-title":"Water Resour. Res."},{"issue":"1","key":"10.1016\/j.media.2025.103881_bib0049","doi-asserted-by":"crossref","DOI":"10.1155\/2011\/732848","article-title":"Quantifying tumor vascular heterogeneity with dynamic contrast-enhanced magnetic resonance imaging: a review","volume":"2011","author":"Yang","year":"2011","journal-title":"Biomed. Res. Int."},{"issue":"3","key":"10.1016\/j.media.2025.103881_bib0050","doi-asserted-by":"crossref","first-page":"356","DOI":"10.1097\/00004728-199205000-00004","article-title":"Spir mri in spinal diseases","volume":"16","author":"Zee","year":"1992","journal-title":"J. Comput. Assist. Tomogr."},{"key":"10.1016\/j.media.2025.103881_bib0051","series-title":"2019IEEE 16Th International Symposium on Biomedical Imaging (ISBI 2019)","first-page":"1003","article-title":"Implicit modeling with uncertainty estimation for intravoxel incoherent motion imaging","author":"Zhang","year":"2019"},{"issue":"8","key":"10.1016\/j.media.2025.103881_bib0052","doi-asserted-by":"crossref","first-page":"3447","DOI":"10.1002\/mp.14222","article-title":"Estimation of pharmacokinetic parameters from DCE-MRI by extracting long and short time-dependent features using an LSTM network","volume":"47","author":"Zou","year":"2020","journal-title":"Med. Phys."}],"container-title":["Medical Image Analysis"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S136184152500427X?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S136184152500427X?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,3,19]],"date-time":"2026-03-19T04:50:35Z","timestamp":1773895835000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S136184152500427X"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,3]]},"references-count":52,"alternative-id":["S136184152500427X"],"URL":"https:\/\/doi.org\/10.1016\/j.media.2025.103881","relation":{},"ISSN":["1361-8415"],"issn-type":[{"value":"1361-8415","type":"print"}],"subject":[],"published":{"date-parts":[[2026,3]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Uncertainty estimates in pharmacokinetic modelling of DCE-MRI","name":"articletitle","label":"Article Title"},{"value":"Medical Image Analysis","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.media.2025.103881","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 B.V.","name":"copyright","label":"Copyright"}],"article-number":"103881"}}