{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,19]],"date-time":"2026-03-19T15:24:06Z","timestamp":1773933846767,"version":"3.50.1"},"reference-count":44,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,5,1]],"date-time":"2026-05-01T00:00:00Z","timestamp":1777593600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,5,1]],"date-time":"2026-05-01T00:00:00Z","timestamp":1777593600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,5,1]],"date-time":"2026-05-01T00:00:00Z","timestamp":1777593600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2026,5,1]],"date-time":"2026-05-01T00:00:00Z","timestamp":1777593600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2026,5,1]],"date-time":"2026-05-01T00:00:00Z","timestamp":1777593600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2026,5,1]],"date-time":"2026-05-01T00:00:00Z","timestamp":1777593600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2026,5,1]],"date-time":"2026-05-01T00:00:00Z","timestamp":1777593600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["82102002"],"award-info":[{"award-number":["82102002"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Pattern Recognition"],"published-print":{"date-parts":[[2026,5]]},"DOI":"10.1016\/j.patcog.2025.112894","type":"journal-article","created":{"date-parts":[[2025,12,13]],"date-time":"2025-12-13T23:30:23Z","timestamp":1765668623000},"page":"112894","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"special_numbering":"C","title":["End-to-end susceptibility-induced distortion correction for diffusion MRI with unsupervised deep learning"],"prefix":"10.1016","volume":"173","author":[{"ORCID":"https:\/\/orcid.org\/0009-0004-6993-3957","authenticated-orcid":false,"given":"Jianhui","family":"Feng","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yonggang","family":"Shi","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9213-991X","authenticated-orcid":false,"given":"Yuchuan","family":"Qiao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"78","reference":[{"issue":"4","key":"10.1016\/j.patcog.2025.112894_bib0001","doi-asserted-by":"crossref","first-page":"2222","DOI":"10.1016\/j.neuroimage.2012.02.018","article-title":"The human connectome project: a data acquisition perspective","volume":"62","author":"Van Essen","year":"2012","journal-title":"Neuroimage"},{"key":"10.1016\/j.patcog.2025.112894_bib0002","doi-asserted-by":"crossref","first-page":"593","DOI":"10.1016\/j.patcog.2016.09.020","article-title":"BundleMAP: anatomically localized classification, regression, and hypothesis testing in diffusion MRI","volume":"63","author":"Khatami","year":"2017","journal-title":"Pattern Recognit."},{"issue":"1","key":"10.1016\/j.patcog.2025.112894_bib0003","doi-asserted-by":"crossref","first-page":"65","DOI":"10.1002\/mrm.1910340111","article-title":"Correction for geometric distortion in echo planar images from b0 field variations","volume":"34","author":"Jezzard","year":"1995","journal-title":"Magn. Reson. Med."},{"key":"10.1016\/j.patcog.2025.112894_bib0004","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.mri.2022.05.016","article-title":"EPI Susceptibility correction introduces significant differences far from local areas of high distortion","volume":"92","author":"Begnoche","year":"2022","journal-title":"Magn. Reson. Imag."},{"issue":"2","key":"10.1016\/j.patcog.2025.112894_bib0005","doi-asserted-by":"crossref","first-page":"870","DOI":"10.1016\/S1053-8119(03)00336-7","article-title":"How to correct susceptibility distortions in spin-echo echo-planar images: application to diffusion tensor imaging","volume":"20","author":"Andersson","year":"2003","journal-title":"Neuroimage"},{"issue":"3","key":"10.1016\/j.patcog.2025.112894_bib0006","doi-asserted-by":"crossref","first-page":"319","DOI":"10.1109\/42.158935","article-title":"A technique for accurate magnetic resonance imaging in the presence of field inhomogeneities","volume":"11","author":"Chang","year":"1992","journal-title":"IEEE Trans. Med. Imag."},{"issue":"9","key":"10.1016\/j.patcog.2025.112894_bib0007","doi-asserted-by":"crossref","DOI":"10.1002\/nbm.4124","article-title":"Distortion correction for high-resolution single-shot EPI DTI using a modified field-mapping method","volume":"32","author":"Xiong","year":"2019","journal-title":"NMR Biomed."},{"issue":"6","key":"10.1016\/j.patcog.2025.112894_bib0008","doi-asserted-by":"crossref","first-page":"588","DOI":"10.1016\/j.media.2007.05.004","article-title":"Correction of susceptibility artifacts in diffusion tensor data using non-linear registration","volume":"11","author":"Merhof","year":"2007","journal-title":"Med. Image Anal."},{"key":"10.1016\/j.patcog.2025.112894_bib0009","doi-asserted-by":"crossref","first-page":"62","DOI":"10.1016\/j.mri.2019.05.008","article-title":"Synthesized b0 for diffusion distortion correction (synb0-Disco)","volume":"64","author":"Schilling","year":"2019","journal-title":"Magn. Reson. Imag."},{"key":"10.1016\/j.patcog.2025.112894_bib0010","doi-asserted-by":"crossref","DOI":"10.1016\/j.neuroimage.2022.119571","article-title":"DACO: Distortion\/artefact correction for diffusion MRI data","volume":"262","author":"Hsu","year":"2022","journal-title":"Neuroimage"},{"key":"10.1016\/j.patcog.2025.112894_bib0011","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.mri.2020.04.004","article-title":"An unsupervised deep learning technique for susceptibility artifact correction in reversed phase-encoding EPI images","volume":"71","author":"Duong","year":"2020","journal-title":"Magn. Reson. Imag."},{"key":"10.1016\/j.patcog.2025.112894_bib0012","doi-asserted-by":"crossref","DOI":"10.1016\/j.neuroimage.2020.116886","article-title":"Deep flow-net for EPI distortion estimation","volume":"217","author":"Zahneisen","year":"2020","journal-title":"Neuroimage"},{"key":"10.1016\/j.patcog.2025.112894_bib0013","series-title":"Computational Diffusion MRI","first-page":"38","article-title":"Correction of susceptibility distortion in EPI: a semi-supervised approach with deep learning","author":"Legouhy","year":"2022"},{"issue":"7","key":"10.1016\/j.patcog.2025.112894_bib0014","doi-asserted-by":"crossref","first-page":"2314","DOI":"10.3390\/s21072314","article-title":"Correcting susceptibility artifacts of MRI sensors in brain scanning: a 3d anatomy-guided deep learning approach","volume":"21","author":"Duong","year":"2021","journal-title":"Sensors"},{"key":"10.1016\/j.patcog.2025.112894_bib0015","doi-asserted-by":"crossref","DOI":"10.1002\/mrm.29653","article-title":"Unsupervised cycle-consistent network using restricted subspace field map for removing susceptibility artifacts in EPI","author":"Bao","year":"2023","journal-title":"Magn. Reson. Med."},{"issue":"1","key":"10.1016\/j.patcog.2025.112894_bib0016","doi-asserted-by":"crossref","first-page":"280","DOI":"10.1002\/mrm.29851","article-title":"FD-NEt: an unsupervised deep forward-distortion model for susceptibility artifact correction in EPI","volume":"91","author":"Zaid Alkilani","year":"2024","journal-title":"Magn. Reson. Med."},{"issue":"11","key":"10.1016\/j.patcog.2025.112894_bib0017","doi-asserted-by":"crossref","first-page":"2320","DOI":"10.1109\/TMI.2015.2430850","article-title":"Fiber orientation and compartment parameter estimation from multi-shell diffusion imaging","volume":"34","author":"Tran","year":"2015","journal-title":"IEEE Trans. Med. Imag."},{"key":"10.1016\/j.patcog.2025.112894_bib0018","doi-asserted-by":"crossref","DOI":"10.1016\/j.neuroimage.2019.116164","article-title":"FOD-Based registration for susceptibility distortion correction in brainstem connectome imaging","volume":"202","author":"Qiao","year":"2019","journal-title":"Neuroimage"},{"issue":"5","key":"10.1016\/j.patcog.2025.112894_bib0019","doi-asserted-by":"crossref","first-page":"1165","DOI":"10.1109\/TMI.2021.3134496","article-title":"Unsupervised deep learning for FOD-based susceptibility distortion correction in diffusion MRI","volume":"41","author":"Qiao","year":"2022","journal-title":"IEEE Trans. Med. Imag."},{"issue":"1","key":"10.1016\/j.patcog.2025.112894_bib0020","doi-asserted-by":"crossref","first-page":"175","DOI":"10.1016\/j.neuroimage.2009.11.044","article-title":"Efficient correction of inhomogeneous static magnetic field-induced distortion in echo planar imaging","volume":"50","author":"Holland","year":"2010","journal-title":"Neuroimage"},{"issue":"10","key":"10.1016\/j.patcog.2025.112894_bib0021","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1371\/journal.pone.0185647","article-title":"Quantitative assessment of the susceptibility artefact and its interaction with motion in diffusion MRI","volume":"12","author":"Graham","year":"2017","journal-title":"PLoS ONE"},{"key":"10.1016\/j.patcog.2025.112894_bib0022","series-title":"Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition","first-page":"1125","article-title":"Image-to-image translation with conditional adversarial networks","author":"Isola","year":"2017"},{"issue":"7","key":"10.1016\/j.patcog.2025.112894_bib0023","doi-asserted-by":"crossref","DOI":"10.1371\/journal.pone.0236418","article-title":"Distortion correction of diffusion weighted MRI without reverse phase-encoding scans or field-maps","volume":"15","author":"Schilling","year":"2020","journal-title":"PLoS ONE"},{"key":"10.1016\/j.patcog.2025.112894_bib0024","doi-asserted-by":"crossref","first-page":"1063","DOI":"10.1016\/j.neuroimage.2015.10.019","article-title":"An integrated approach to correction for off-resonance effects and subject movement in diffusion MR imaging","volume":"125","author":"Andersson","year":"2016","journal-title":"Neuroimage"},{"key":"10.1016\/j.patcog.2025.112894_bib0025","article-title":"Spatial transformer networks","volume":"28","author":"Jaderberg","year":"2015","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"10.1016\/j.patcog.2025.112894_bib0026","series-title":"Proceedings of the IEEE\/CVF International Conference on Computer Vision","first-page":"10012","article-title":"Swin transformer: hierarchical vision transformer using shifted windows","author":"Liu","year":"2021"},{"key":"10.1016\/j.patcog.2025.112894_bib0027","doi-asserted-by":"crossref","DOI":"10.1016\/j.media.2022.102615","article-title":"Transmorph: transformer for unsupervised medical image registration","volume":"82","author":"Chen","year":"2022","journal-title":"Med. Image Anal."},{"key":"10.1016\/j.patcog.2025.112894_bib0028","series-title":"International Conference on Information Processing in Medical Imaging","first-page":"64","article-title":"GSSD: A self-distillation paradigm with gradient surgery for end-to-End deformable image registration","author":"Zheng","year":"2025"},{"issue":"8","key":"10.1016\/j.patcog.2025.112894_bib0029","doi-asserted-by":"crossref","first-page":"1788","DOI":"10.1109\/TMI.2019.2897538","article-title":"Voxelmorph: a learning framework for deformable medical image registration","volume":"38","author":"Balakrishnan","year":"2019","journal-title":"IEEE Trans. Med. Imag."},{"key":"10.1016\/j.patcog.2025.112894_bib0030","series-title":"International Conference on Medical Image Computing and Computer-Assisted Intervention","first-page":"750","article-title":"Non-iterative coarse-to-fine transformer networks for joint affine and deformable image registration","author":"Meng","year":"2023"},{"key":"10.1016\/j.patcog.2025.112894_bib0031","article-title":"Recursive deformable pyramid network for unsupervised medical image registration","author":"Wang","year":"2024","journal-title":"IEEE Trans. Med. Imag."},{"key":"10.1016\/j.patcog.2025.112894_bib0032","article-title":"Efficient large-Deformation medical image registration via recurrent dynamic correlation","author":"Li","year":"2025","journal-title":"IEEE Trans. Med. Imag."},{"key":"10.1016\/j.patcog.2025.112894_bib0033","article-title":"A boundary-guided needle localization approach for MRI-guided robotic interventions","author":"Li","year":"2025","journal-title":"IEEE Trans. Instrum. Meas."},{"issue":"4","key":"10.1016\/j.patcog.2025.112894_bib0034","doi-asserted-by":"crossref","first-page":"5355","DOI":"10.1109\/TNNLS.2022.3204090","article-title":"SwinPA-Net: swin transformer-based multiscale feature pyramid aggregation network for medical image segmentation","volume":"35","author":"Du","year":"2022","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"10.1016\/j.patcog.2025.112894_bib0035","doi-asserted-by":"crossref","first-page":"335","DOI":"10.1016\/j.neuroimage.2018.10.009","article-title":"The lifespan human connectome project in aging: an overview","volume":"185","author":"Bookheimer","year":"2019","journal-title":"Neuroimage"},{"issue":"11","key":"10.1016\/j.patcog.2025.112894_bib0036","doi-asserted-by":"crossref","first-page":"960","DOI":"10.1097\/NEN.0b013e318232a379","article-title":"Stages of the pathologic process in alzheimer disease: age categories from 1 to 100 years","volume":"70","author":"Braak","year":"2011","journal-title":"J. Neuropathol. Exp. Neurol."},{"issue":"1","key":"10.1016\/j.patcog.2025.112894_bib0037","doi-asserted-by":"crossref","first-page":"275","DOI":"10.1016\/j.neuroimage.2012.02.054","article-title":"Effects of image distortions originating from susceptibility variations and concomitant fields on diffusion MRI tractography results","volume":"61","author":"Irfanoglu","year":"2012","journal-title":"Neuroimage"},{"key":"10.1016\/j.patcog.2025.112894_bib0038","doi-asserted-by":"crossref","first-page":"184","DOI":"10.1016\/j.neuroimage.2015.02.065","article-title":"Bayesian segmentation of brainstem structures in MRI","volume":"113","author":"Iglesias","year":"2015","journal-title":"Neuroimage"},{"key":"10.1016\/j.patcog.2025.112894_bib0039","unstructured":"D.P. Kingma, Adam: a method for stochastic optimization, arXiv preprint arXiv: 1412.6980(2014)."},{"issue":"1","key":"10.1016\/j.patcog.2025.112894_bib0040","doi-asserted-by":"crossref","first-page":"204","DOI":"10.1007\/s10278-022-00721-9","article-title":"Deep learning for image enhancement and correction in magnetic resonance imaging-state-of-the-art and challenges","volume":"36","author":"Chen","year":"2023","journal-title":"J. Digit. Imag."},{"key":"10.1016\/j.patcog.2025.112894_bib0041","doi-asserted-by":"crossref","DOI":"10.1016\/j.compbiomed.2021.105111","article-title":"Transparency of deep neural networks for medical image analysis: a review of interpretability methods","volume":"140","author":"Salahuddin","year":"2022","journal-title":"Comput. Biol. Med."},{"key":"10.1016\/j.patcog.2025.112894_bib0042","doi-asserted-by":"crossref","DOI":"10.1016\/j.patcog.2022.108737","article-title":"Improving the facial expression recognition and its interpretability via generating expression pattern-map","volume":"129","author":"Zhang","year":"2022","journal-title":"Pattern Recognit."},{"key":"10.1016\/j.patcog.2025.112894_bib0043","doi-asserted-by":"crossref","DOI":"10.1016\/j.patcog.2024.110424","article-title":"Federated learning for medical image analysis: a survey","author":"Guan","year":"2024","journal-title":"Pattern Recognit."},{"key":"10.1016\/j.patcog.2025.112894_bib0044","series-title":"International Conference on Medical Image Computing and Computer-Assisted Intervention","first-page":"152","article-title":"Eddeep: fast eddy-current distortion correction for diffusion MRI with deep learning","author":"Legouhy","year":"2024"}],"container-title":["Pattern Recognition"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0031320325015572?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0031320325015572?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,3,19]],"date-time":"2026-03-19T13:05:35Z","timestamp":1773925535000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0031320325015572"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,5]]},"references-count":44,"alternative-id":["S0031320325015572"],"URL":"https:\/\/doi.org\/10.1016\/j.patcog.2025.112894","relation":{},"ISSN":["0031-3203"],"issn-type":[{"value":"0031-3203","type":"print"}],"subject":[],"published":{"date-parts":[[2026,5]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"End-to-end susceptibility-induced distortion correction for diffusion MRI with unsupervised deep learning","name":"articletitle","label":"Article Title"},{"value":"Pattern Recognition","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.patcog.2025.112894","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2025 Elsevier Ltd. All rights are reserved, including those for text and data mining, AI training, and similar technologies.","name":"copyright","label":"Copyright"}],"article-number":"112894"}}