{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,30]],"date-time":"2026-03-30T19:48:20Z","timestamp":1774900100754,"version":"3.50.1"},"reference-count":71,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2017,9,1]],"date-time":"2017-09-01T00:00:00Z","timestamp":1504224000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/2.zoppoz.workers.dev:443\/https\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2017,9,1]],"date-time":"2017-09-01T00:00:00Z","timestamp":1504224000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/2.zoppoz.workers.dev:443\/https\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2018,7,18]],"date-time":"2018-07-18T00:00:00Z","timestamp":1531872000000},"content-version":"am","delay-in-days":320,"URL":"https:\/\/2.zoppoz.workers.dev:443\/http\/www.elsevier.com\/open-access\/userlicense\/1.0\/"},{"start":{"date-parts":[[2017,9,1]],"date-time":"2017-09-01T00:00:00Z","timestamp":1504224000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/2.zoppoz.workers.dev:443\/https\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2017,9,1]],"date-time":"2017-09-01T00:00:00Z","timestamp":1504224000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/2.zoppoz.workers.dev:443\/https\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2017,9,1]],"date-time":"2017-09-01T00:00:00Z","timestamp":1504224000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/2.zoppoz.workers.dev:443\/https\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2017,9,1]],"date-time":"2017-09-01T00:00:00Z","timestamp":1504224000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/2.zoppoz.workers.dev:443\/https\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2017,9,1]],"date-time":"2017-09-01T00:00:00Z","timestamp":1504224000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/2.zoppoz.workers.dev:443\/https\/doi.org\/10.15223\/policy-004"}],"funder":[{"DOI":"10.13039\/100000002","name":"NIH","doi-asserted-by":"publisher","award":["1 R41 NS091792-01"],"award-info":[{"award-number":["1 R41 NS091792-01"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000002","name":"NIH","doi-asserted-by":"publisher","award":["HDO55741"],"award-info":[{"award-number":["HDO55741"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000001","name":"NSF","doi-asserted-by":"publisher","award":["ECCS-1148870"],"award-info":[{"award-number":["ECCS-1148870"]}],"id":[{"id":"10.13039\/100000001","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000001","name":"NSF","doi-asserted-by":"publisher","award":["EECS-1711776"],"award-info":[{"award-number":["EECS-1711776"]}],"id":[{"id":"10.13039\/100000001","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000001","name":"National Science Foundation Major Research Instrumentation program","doi-asserted-by":"publisher","award":["ACI-1429830"],"award-info":[{"award-number":["ACI-1429830"]}],"id":[{"id":"10.13039\/100000001","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["NeuroImage"],"published-print":{"date-parts":[[2017,9]]},"DOI":"10.1016\/j.neuroimage.2017.07.008","type":"journal-article","created":{"date-parts":[[2017,7,11]],"date-time":"2017-07-11T05:08:32Z","timestamp":1499749712000},"page":"378-396","update-policy":"https:\/\/2.zoppoz.workers.dev:443\/https\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":551,"special_numbering":"C","title":["Quicksilver: Fast predictive image registration \u2013 A deep learning approach"],"prefix":"10.1016","volume":"158","author":[{"given":"Xiao","family":"Yang","sequence":"first","affiliation":[]},{"given":"Roland","family":"Kwitt","sequence":"additional","affiliation":[]},{"given":"Martin","family":"Styner","sequence":"additional","affiliation":[]},{"given":"Marc","family":"Niethammer","sequence":"additional","affiliation":[]}],"member":"78","reference":[{"issue":"1","key":"10.1016\/j.neuroimage.2017.07.008_bib1","doi-asserted-by":"crossref","first-page":"67","DOI":"10.1016\/j.jneumeth.2004.07.014","article-title":"Quantitative comparison of algorithms for inter-subject registration of 3D volumetric brain MRI scans","volume":"142","author":"Ardekani","year":"2005","journal-title":"J.\u00a0Neurosci. Methods"},{"issue":"1","key":"10.1016\/j.neuroimage.2017.07.008_bib2","doi-asserted-by":"crossref","first-page":"95","DOI":"10.1016\/j.neuroimage.2007.07.007","article-title":"A\u00a0fast diffeomorphic image registration algorithm","volume":"38","author":"Ashburner","year":"2007","journal-title":"NeuroImage"},{"issue":"3","key":"10.1016\/j.neuroimage.2017.07.008_bib3","doi-asserted-by":"crossref","first-page":"954","DOI":"10.1016\/j.neuroimage.2010.12.049","article-title":"Diffeomorphic registration using geodesic shooting and Gauss\u2013Newton optimisation","volume":"55","author":"Ashburner","year":"2011","journal-title":"NeuroImage"},{"issue":"1","key":"10.1016\/j.neuroimage.2017.07.008_bib4","first-page":"26","article-title":"Symmetric diffeomorphic image registration with cross-correlation: evaluating automated labeling of elderly and neurodegenerative brain","volume":"12","author":"Avants","year":"2008","journal-title":"MedIA"},{"issue":"2","key":"10.1016\/j.neuroimage.2017.07.008_bib5","doi-asserted-by":"crossref","first-page":"139","DOI":"10.1023\/B:VISI.0000043755.93987.aa","article-title":"Computing large deformation metric mappings via geodesic flows of diffeomorphisms","volume":"61","author":"Beg","year":"2005","journal-title":"IJCV"},{"key":"10.1016\/j.neuroimage.2017.07.008_bib6","unstructured":"Biobank website: www.ukbiobank.ac.uk."},{"issue":"3","key":"10.1016\/j.neuroimage.2017.07.008_bib7","doi-asserted-by":"crossref","first-page":"818","DOI":"10.1137\/S1064827501386481","article-title":"Optimal control formulation for determining optical flow","volume":"24","author":"Borzi","year":"2003","journal-title":"SIAM J. Sci. Comput."},{"issue":"4","key":"10.1016\/j.neuroimage.2017.07.008_bib8","doi-asserted-by":"crossref","first-page":"1344","DOI":"10.1137\/110846324","article-title":"Mixture of kernels and iterated semidirect product of diffeomorphisms groups","volume":"10","author":"Bruveris","year":"2012","journal-title":"Multiscale Model. Simul."},{"issue":"6","key":"10.1016\/j.neuroimage.2017.07.008_bib9","first-page":"914","article-title":"Multi-modal registration for correlative microscopy using image analogies","volume":"18","author":"Cao","year":"2014","journal-title":"MedIA"},{"key":"10.1016\/j.neuroimage.2017.07.008_bib10","series-title":"Semi-coupled Dictionary Learning for Deformation Prediction","first-page":"691","author":"Cao","year":"2015"},{"key":"10.1016\/j.neuroimage.2017.07.008_bib11","first-page":"1","article-title":"Deep similarity learning for multimodal medical images","author":"Cheng","year":"2015","journal-title":"Comput. Methods Biomech. Biomed. Eng. Imaging Vis."},{"issue":"9","key":"10.1016\/j.neuroimage.2017.07.008_bib12","first-page":"1095","article-title":"2D\/3D image registration using regression learning","volume":"117","author":"Chou","year":"2013","journal-title":"CVIU"},{"issue":"6","key":"10.1016\/j.neuroimage.2017.07.008_bib13","doi-asserted-by":"crossref","first-page":"508","DOI":"10.1038\/nmeth.2481","article-title":"CLARITY for mapping the nervous system","volume":"10","author":"Chung","year":"2013","journal-title":"Nat. Methods"},{"key":"10.1016\/j.neuroimage.2017.07.008_bib14","series-title":"Flownet: Learning Optical Flow with Convolutional Networks","first-page":"2758","author":"Dosovitskiy","year":"2015"},{"key":"10.1016\/j.neuroimage.2017.07.008_bib15","doi-asserted-by":"crossref","first-page":"587","DOI":"10.1090\/qam\/1632326","article-title":"Variational problems on flows of diffeomorphisms for image matching","author":"Dupuis","year":"1998","journal-title":"Q.\u00a0Appl. Math."},{"key":"10.1016\/j.neuroimage.2017.07.008_bib16","series-title":"Bayesian Convolutional Neural Networks with Bernoulli Approximate Variational Inference","author":"Gal","year":"2015"},{"key":"10.1016\/j.neuroimage.2017.07.008_bib17","series-title":"Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning","author":"Gal","year":"2016"},{"key":"10.1016\/j.neuroimage.2017.07.008_bib18","series-title":"Degrees of Freedom in Deep Neural Networks","first-page":"232","author":"Gao","year":"2016"},{"key":"10.1016\/j.neuroimage.2017.07.008_bib19","first-page":"58","article-title":"Symmetric Atlasing and Model Based Segmentation: an Application to the hippocampus in Older Adults","author":"Grabner","year":"2006"},{"key":"10.1016\/j.neuroimage.2017.07.008_bib20","series-title":"Evaluating Derivatives: Principles and Techniques of Algorithmic Differentiation","author":"Griewank","year":"2008"},{"key":"10.1016\/j.neuroimage.2017.07.008_bib21","series-title":"Learning Based Non-rigid Multi-modal Image Registration Using Kullback-leibler Divergence","first-page":"255","author":"Guetter","year":"2005"},{"key":"10.1016\/j.neuroimage.2017.07.008_bib22","article-title":"Guiding multimodal registration with learned optimization updates","author":"Gutierrez-Becker","year":"2017","journal-title":"MedIA"},{"key":"10.1016\/j.neuroimage.2017.07.008_bib23","series-title":"Learning Optimization Updates for Multimodal Registration","first-page":"19","author":"Guti\u00e9rrez-Becker","year":"2016"},{"issue":"3","key":"10.1016\/j.neuroimage.2017.07.008_bib24","doi-asserted-by":"crossref","first-page":"361","DOI":"10.1007\/s11263-006-8984-4","article-title":"Image registration with guaranteed displacement regularity","volume":"71","author":"Haber","year":"2007","journal-title":"Int. J. Comput. Vis."},{"key":"10.1016\/j.neuroimage.2017.07.008_bib25","series-title":"Computer Vision and Pattern Recognition Workshops, 2009. CVPR Workshops 2009","first-page":"9","article-title":"An optimal control approach for deformable registration","author":"Hart","year":"2009"},{"issue":"7641","key":"10.1016\/j.neuroimage.2017.07.008_bib26","doi-asserted-by":"crossref","first-page":"348","DOI":"10.1038\/nature21369","article-title":"Early brain development in infants at high risk for autism spectrum disorder","volume":"542","author":"Hazlett","year":"2017","journal-title":"Nature"},{"key":"10.1016\/j.neuroimage.2017.07.008_bib27","series-title":"Delving Deep into Rectifiers: Surpassing Human-level Performance on ImageNet Classification, CoRR abs\/1502.01852","author":"He","year":"2015"},{"issue":"3","key":"10.1016\/j.neuroimage.2017.07.008_bib28","doi-asserted-by":"crossref","first-page":"329","DOI":"10.1023\/A:1020830525823","article-title":"Variational methods for multimodal image matching","volume":"50","author":"Hermosillo","year":"2002","journal-title":"IJCV"},{"issue":"3","key":"10.1016\/j.neuroimage.2017.07.008_bib29","doi-asserted-by":"crossref","first-page":"R1","DOI":"10.1088\/0031-9155\/46\/3\/201","article-title":"Medical image registration","volume":"46","author":"Hill","year":"2001","journal-title":"Phys. Med. Biol."},{"key":"10.1016\/j.neuroimage.2017.07.008_bib30","series-title":"Simple Geodesic Regression for Image Time-series","first-page":"11","author":"Hong","year":"2012"},{"issue":"1\u20133","key":"10.1016\/j.neuroimage.2017.07.008_bib31","doi-asserted-by":"crossref","first-page":"185","DOI":"10.1016\/0004-3702(81)90024-2","article-title":"Determining optical flow","volume":"17","author":"Horn","year":"1981","journal-title":"Artif. Intell."},{"key":"10.1016\/j.neuroimage.2017.07.008_bib32","doi-asserted-by":"crossref","first-page":"151","DOI":"10.1016\/j.neuroimage.2004.07.068","article-title":"Unbiased diffeomorphic atlas construction for computational anatomy","volume":"23","author":"Joshi","year":"2004","journal-title":"NeuroImage"},{"key":"10.1016\/j.neuroimage.2017.07.008_bib33","series-title":"Adam: a Method for Stochastic Optimization","author":"Kingma","year":"2014"},{"issue":"3","key":"10.1016\/j.neuroimage.2017.07.008_bib34","doi-asserted-by":"crossref","first-page":"786","DOI":"10.1016\/j.neuroimage.2008.12.037","article-title":"Evaluation of 14 nonlinear deformation algorithms applied to human brain MRI registration","volume":"46","author":"Klein","year":"2009","journal-title":"NeuroImage"},{"key":"10.1016\/j.neuroimage.2017.07.008_bib35","unstructured":"LeCun, Y. A theoretical framework for back-propagation. In: Proceedings of the 1988 Connectionist Models Summer School, 1988."},{"key":"10.1016\/j.neuroimage.2017.07.008_bib36","series-title":"Learning Similarity Measure for Multi-modal 3D Image Registration","first-page":"186","author":"Lee","year":"2009"},{"key":"10.1016\/j.neuroimage.2017.07.008_bib37","series-title":"Fully Convolutional Networks for Semantic Segmentation","first-page":"3431","author":"Long","year":"2015"},{"issue":"3","key":"10.1016\/j.neuroimage.2017.07.008_bib38","first-page":"440","article-title":"Multi-modal image set registration and atlas formation","volume":"10","author":"Lorenzen","year":"2006","journal-title":"MedIA"},{"issue":"9","key":"10.1016\/j.neuroimage.2017.07.008_bib39","doi-asserted-by":"crossref","first-page":"1498","DOI":"10.1162\/jocn.2007.19.9.1498","article-title":"Open access series of imaging studies (OASIS): cross-sectional MRI data in young, middle aged, nondemented, and demented older adults","volume":"19","author":"Marcus","year":"2007","journal-title":"J.\u00a0Cogn. Neurosci."},{"key":"10.1016\/j.neuroimage.2017.07.008_bib40","series-title":"Evaluation of Control Point Selection in Automatic, Mutual Information Driven, 3D Warping","first-page":"944","author":"Meyer","year":"1998"},{"issue":"5","key":"10.1016\/j.neuroimage.2017.07.008_bib41","first-page":"1352","article-title":"A\u00a0CNN regression approach for real-time 2D\/3D registration","volume":"35","author":"Miao","year":"2016","journal-title":"IEEE TMI"},{"key":"10.1016\/j.neuroimage.2017.07.008_bib42","series-title":"Boosted Metric Learning for 3D Multi-modal Deformable Registration","first-page":"1209","author":"Michel","year":"2011"},{"key":"10.1016\/j.neuroimage.2017.07.008_bib43","series-title":"Numerical Methods for Image Registration","author":"Modersitzki","year":"2004"},{"key":"10.1016\/j.neuroimage.2017.07.008_bib44","series-title":"Proceedings of the 27th International Conference on Machine Learning (ICML-10)","first-page":"807","article-title":"Rectified linear units improve restricted Boltzmann machines","author":"Nair","year":"2010"},{"key":"10.1016\/j.neuroimage.2017.07.008_bib45","series-title":"Geodesic Regression for Image Time-series","first-page":"655","author":"Niethammer","year":"2011"},{"key":"10.1016\/j.neuroimage.2017.07.008_bib46","series-title":"Numerical Optimization","author":"Nocedal","year":"2006"},{"issue":"2","key":"10.1016\/j.neuroimage.2017.07.008_bib47","doi-asserted-by":"crossref","first-page":"73","DOI":"10.1080\/10255842.2012.670855","article-title":"Medical image registration: a review","volume":"17","author":"Oliveira","year":"2014","journal-title":"Comput. Methods Biomech. Biomed. Eng."},{"issue":"4","key":"10.1016\/j.neuroimage.2017.07.008_bib48","doi-asserted-by":"crossref","first-page":"1402","DOI":"10.1016\/j.neuroimage.2012.02.084","article-title":"Within-subject template estimation for unbiased longitudinal image analysis","volume":"61","author":"Reuter","year":"2012","journal-title":"NeuroImage"},{"issue":"8","key":"10.1016\/j.neuroimage.2017.07.008_bib49","doi-asserted-by":"crossref","first-page":"712","DOI":"10.1109\/42.796284","article-title":"Nonrigid registration using free-form deformations: application to breast MR images","volume":"18","author":"Rueckert","year":"1999","journal-title":"IEEE Trans. Med. Imaging"},{"issue":"2","key":"10.1016\/j.neuroimage.2017.07.008_bib50","doi-asserted-by":"crossref","first-page":"50","DOI":"10.1109\/MSP.2009.935387","article-title":"A\u00a0survey of medical image registration on multicore and the GPU","volume":"27","author":"Shams","year":"2010","journal-title":"IEEE Signal Process. Mag."},{"key":"10.1016\/j.neuroimage.2017.07.008_bib51","series-title":"A Deep Metric for Multimodal Registration","first-page":"10","author":"Simonovsky","year":"2016"},{"key":"10.1016\/j.neuroimage.2017.07.008_bib52","series-title":"Longitudinal Brain MRI Analysis with Uncertain Registration","first-page":"647","author":"Simpson","year":"2011"},{"key":"10.1016\/j.neuroimage.2017.07.008_bib53","series-title":"A\u00a0Vector Momenta Formulation of Diffeomorphisms for Improved Geodesic Regression and Atlas Construction","first-page":"1219","author":"Singh","year":"2013"},{"key":"10.1016\/j.neuroimage.2017.07.008_bib54","series-title":"A\u00a0Hierarchical Geodesic Model for Diffeomorphic Longitudinal Shape Analysis","first-page":"560","author":"Singh","year":"2013"},{"issue":"7","key":"10.1016\/j.neuroimage.2017.07.008_bib55","doi-asserted-by":"crossref","first-page":"1153","DOI":"10.1109\/TMI.2013.2265603","article-title":"Deformable medical image registration: a survey","volume":"32","author":"Sotiras","year":"2013","journal-title":"IEEE Trans. Med. Imaging"},{"key":"10.1016\/j.neuroimage.2017.07.008_bib56","series-title":"Striving for Simplicity: the All Convolutional Net, CoRR abs\/1412.6806","author":"Springenberg","year":"2014"},{"key":"10.1016\/j.neuroimage.2017.07.008_bib57","first-page":"1929","article-title":"Dropout: a simple way to prevent neural networks from overfitting","volume":"15","author":"Srivastava","year":"2014","journal-title":"JMLR"},{"issue":"Suppl. 1","key":"10.1016\/j.neuroimage.2017.07.008_bib58","doi-asserted-by":"crossref","first-page":"161","DOI":"10.1016\/j.neuroimage.2004.07.023","article-title":"Statistics on diffeomorphisms via tangent space representations","volume":"23","author":"Vaillant","year":"2004","journal-title":"NeuroImage"},{"key":"10.1016\/j.neuroimage.2017.07.008_bib59","doi-asserted-by":"crossref","first-page":"62","DOI":"10.1016\/j.neuroimage.2013.05.041","article-title":"WU-Minn HCP Consortium, the WU-Minn human connectome project: an overview","volume":"80","author":"Van Essen","year":"2013","journal-title":"NeuroImage"},{"issue":"1","key":"10.1016\/j.neuroimage.2017.07.008_bib60","doi-asserted-by":"crossref","first-page":"S61","DOI":"10.1016\/j.neuroimage.2008.10.040","article-title":"Diffeomorphic demons: efficient non-parametric image registration","volume":"45","author":"Vercauteren","year":"2009","journal-title":"NeuroImage"},{"issue":"2","key":"10.1016\/j.neuroimage.2017.07.008_bib61","doi-asserted-by":"crossref","first-page":"229","DOI":"10.1007\/s11263-011-0481-8","article-title":"Diffeomorphic 3D image registration via geodesic shooting using an efficient adjoint calculation","volume":"97","author":"Vialard","year":"2012","journal-title":"IJCV"},{"issue":"2","key":"10.1016\/j.neuroimage.2017.07.008_bib62","doi-asserted-by":"crossref","first-page":"137","DOI":"10.1023\/A:1007958904918","article-title":"Alignment by maximization of mutual information","volume":"24","author":"Viola","year":"1997","journal-title":"IJCV"},{"key":"10.1016\/j.neuroimage.2017.07.008_bib63","doi-asserted-by":"crossref","first-page":"7","DOI":"10.3389\/fninf.2014.00007","article-title":"Multi-atlas segmentation of subcortical brain structures via the autoseg software pipeline","volume":"8","author":"Wang","year":"2014","journal-title":"Front. Neuroinformatics"},{"issue":"1","key":"10.1016\/j.neuroimage.2017.07.008_bib64","first-page":"61","article-title":"Predict brain MR image registration via sparse learning of appearance & transformation","volume":"20","author":"Wang","year":"2015","journal-title":"MedIA"},{"issue":"5","key":"10.1016\/j.neuroimage.2017.07.008_bib65","first-page":"577","article-title":"Automatic CT-ultrasound registration for diagnostic imaging and image-guided intervention","volume":"12","author":"Wein","year":"2008","journal-title":"MedIA"},{"key":"10.1016\/j.neuroimage.2017.07.008_bib66","series-title":"DeepFlow: Large Displacement Optical Flow with Deep Matching","first-page":"1385","author":"Weinzaepfel","year":"2013"},{"key":"10.1016\/j.neuroimage.2017.07.008_bib67","series-title":"Testing Statistical Hypotheses of Equivalence","author":"Wellek","year":"2010"},{"key":"10.1016\/j.neuroimage.2017.07.008_bib68","series-title":"Fast Predictive Image Registration","first-page":"48","author":"Yang","year":"2016"},{"key":"10.1016\/j.neuroimage.2017.07.008_bib69","series-title":"Fast Predictive Multimodal Image Registration","first-page":"858","author":"Yang","year":"2017"},{"key":"10.1016\/j.neuroimage.2017.07.008_bib70","first-page":"214","article-title":"A\u00a0duality based approach for realtime TV-L1 optical flow","author":"Zach","year":"2007","journal-title":"Pattern Recognit."},{"key":"10.1016\/j.neuroimage.2017.07.008_bib71","series-title":"Finite-dimensional Lie Algebras for Fast Diffeomorphic Image Registration","first-page":"249","author":"Zhang","year":"2015"}],"container-title":["NeuroImage"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/2.zoppoz.workers.dev:443\/https\/api.elsevier.com\/content\/article\/PII:S1053811917305761?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/2.zoppoz.workers.dev:443\/https\/api.elsevier.com\/content\/article\/PII:S1053811917305761?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2025,10,20]],"date-time":"2025-10-20T09:57:44Z","timestamp":1760954264000},"score":1,"resource":{"primary":{"URL":"https:\/\/2.zoppoz.workers.dev:443\/https\/linkinghub.elsevier.com\/retrieve\/pii\/S1053811917305761"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2017,9]]},"references-count":71,"alternative-id":["S1053811917305761"],"URL":"https:\/\/2.zoppoz.workers.dev:443\/https\/doi.org\/10.1016\/j.neuroimage.2017.07.008","relation":{},"ISSN":["1053-8119"],"issn-type":[{"value":"1053-8119","type":"print"}],"subject":[],"published":{"date-parts":[[2017,9]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Quicksilver: Fast predictive image registration \u2013 A deep learning approach","name":"articletitle","label":"Article Title"},{"value":"NeuroImage","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/2.zoppoz.workers.dev:443\/https\/doi.org\/10.1016\/j.neuroimage.2017.07.008","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2017 Elsevier Inc. All rights reserved.","name":"copyright","label":"Copyright"}]}}