{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,12]],"date-time":"2026-06-12T21:56:45Z","timestamp":1781301405947,"version":"3.54.1"},"reference-count":45,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,11,1]],"date-time":"2026-11-01T00:00:00Z","timestamp":1793491200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,11,1]],"date-time":"2026-11-01T00:00:00Z","timestamp":1793491200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,11,1]],"date-time":"2026-11-01T00:00:00Z","timestamp":1793491200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2026,11,1]],"date-time":"2026-11-01T00:00:00Z","timestamp":1793491200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2026,11,1]],"date-time":"2026-11-01T00:00:00Z","timestamp":1793491200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2026,11,1]],"date-time":"2026-11-01T00:00:00Z","timestamp":1793491200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2026,11,1]],"date-time":"2026-11-01T00:00:00Z","timestamp":1793491200000},"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":["82302295"],"award-info":[{"award-number":["82302295"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100012543","name":"Shanghai Science and Technology Development Foundation","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100012543","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012600","name":"ShanghaiTech University","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100012600","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":[[2026,11]]},"DOI":"10.1016\/j.patcog.2026.113699","type":"journal-article","created":{"date-parts":[[2026,4,10]],"date-time":"2026-04-10T17:03:04Z","timestamp":1775840584000},"page":"113699","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"special_numbering":"PB","title":["FLEX-MoCo: Flexible MRI motion correction using motion recognition and adaptive routing"],"prefix":"10.1016","volume":"179","author":[{"ORCID":"https:\/\/orcid.org\/0009-0009-7721-6647","authenticated-orcid":false,"given":"Feng","family":"Li","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhenrong","family":"Shen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jiangdong","family":"Cai","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Rongrong","family":"Xie","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zijian","family":"Zhou","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Han","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Dinggang","family":"Shen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Feng","family":"Shi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3490-3836","authenticated-orcid":false,"given":"Qian","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"issue":"4","key":"10.1016\/j.patcog.2026.113699_b1","doi-asserted-by":"crossref","first-page":"887","DOI":"10.1002\/jmri.24850","article-title":"Motion artifacts in MRI: A complex problem with many partial solutions","volume":"42","author":"Zaitsev","year":"2015","journal-title":"J. Magn. Reson. Imaging"},{"issue":"2","key":"10.1016\/j.patcog.2026.113699_b2","doi-asserted-by":"crossref","first-page":"846","DOI":"10.1109\/TMI.2023.3323215","article-title":"Deep learning for retrospective motion correction in MRI: A comprehensive review","volume":"43","author":"Spieker","year":"2024","journal-title":"IEEE Trans. Med. Imaging"},{"issue":"1","key":"10.1016\/j.patcog.2026.113699_b3","doi-asserted-by":"crossref","first-page":"68","DOI":"10.1109\/TMI.2006.885337","article-title":"An optimal radial profile order based on the golden ratio for time-resolved MRI","volume":"26","author":"Winkelmann","year":"2006","journal-title":"IEEE Trans. Med. Imaging"},{"issue":"6","key":"10.1016\/j.patcog.2026.113699_b4","doi-asserted-by":"crossref","first-page":"1182","DOI":"10.1002\/mrm.21391","article-title":"Sparse MRI: The application of compressed sensing for rapid mr imaging","volume":"58","author":"Lustig","year":"2007","journal-title":"Magn. Reson. Med."},{"key":"10.1016\/j.patcog.2026.113699_b5","series-title":"MICCAI","first-page":"293","article-title":"Dual domain motion artifacts correction for mr imaging under guidance of k-space uncertainty","author":"Wang","year":"2023"},{"issue":"12","key":"10.1016\/j.patcog.2026.113699_b6","doi-asserted-by":"crossref","first-page":"12550","DOI":"10.1109\/TCSVT.2024.3432751","article-title":"Generalizable MRI motion correction via compressed sensing equivariant imaging prior","volume":"34","author":"Wang","year":"2024","journal-title":"IEEE Trans. Circuits Syst. Video Technol."},{"issue":"5","key":"10.1016\/j.patcog.2026.113699_b7","doi-asserted-by":"crossref","first-page":"1423","DOI":"10.1002\/jmri.70027","article-title":"A deep learning-based de-artifact diffusion model for removing motion artifacts in knee MRI","volume":"62","author":"Li","year":"2025","journal-title":"J. Magn. Reson. Imaging"},{"key":"10.1016\/j.patcog.2026.113699_b8","series-title":"CVPR","first-page":"2472","article-title":"Residual dense network for image super-resolution","author":"Zhang","year":"2018"},{"key":"10.1016\/j.patcog.2026.113699_b9","series-title":"CVPR","first-page":"14821","article-title":"Multi-stage progressive image restoration","author":"Zamir","year":"2021"},{"key":"10.1016\/j.patcog.2026.113699_b10","doi-asserted-by":"crossref","DOI":"10.1016\/j.patcog.2025.112944","article-title":"Dsasformer: Dynamic scale-aware sparse transformer for image restoration","volume":"174","author":"Wang","year":"2026","journal-title":"Pattern Recognit."},{"key":"10.1016\/j.patcog.2026.113699_b11","series-title":"CVPR","first-page":"12731","article-title":"A universal scale-adaptive deformable transformer for image restoration across diverse artifacts","author":"He","year":"2025"},{"key":"10.1016\/j.patcog.2026.113699_b12","series-title":"ECCV","first-page":"246","article-title":"Seeing the unseen: A frequency prompt guided transformer for image restoration","author":"Zhou","year":"2024"},{"issue":"7","key":"10.1016\/j.patcog.2026.113699_b13","doi-asserted-by":"crossref","first-page":"6259","DOI":"10.1109\/TCSVT.2025.3538772","article-title":"SSP-IR: Semantic and structure priors for diffusion-based realistic image restoration","volume":"35","author":"Zhang","year":"2025","journal-title":"IEEE Trans. Circuits Syst. Video Technol."},{"key":"10.1016\/j.patcog.2026.113699_b14","series-title":"NeurIPS","first-page":"13294","article-title":"Resshift: Efficient diffusion model for image super-resolution by residual shifting","author":"Yue","year":"2023"},{"key":"10.1016\/j.patcog.2026.113699_b15","doi-asserted-by":"crossref","DOI":"10.1016\/j.patcog.2025.111473","article-title":"Learning physical-aware diffusion priors for zero-shot restoration of scattering-affected images","volume":"163","author":"Qiao","year":"2025","journal-title":"Pattern Recognit."},{"issue":"3","key":"10.1016\/j.patcog.2026.113699_b16","doi-asserted-by":"crossref","first-page":"266","DOI":"10.1109\/TCI.2016.2557069","article-title":"Sensitivity encoding for aligned multishot magnetic resonance reconstruction","volume":"2","author":"Cordero-Grande","year":"2016","journal-title":"IEEE Trans. Comput. Imaging"},{"issue":"5","key":"10.1016\/j.patcog.2026.113699_b17","doi-asserted-by":"crossref","first-page":"1253","DOI":"10.1109\/TMI.2018.2791482","article-title":"Targeted motion estimation and reduction (TAMER): data consistency based motion mitigation for MRI using a reduced model joint optimization","volume":"37","author":"Haskell","year":"2018","journal-title":"IEEE Trans. Med. Imaging"},{"key":"10.1016\/j.patcog.2026.113699_b18","series-title":"fastMRI: An open dataset and benchmarks for accelerated MRI","author":"Zbontar","year":"2018"},{"key":"10.1016\/j.patcog.2026.113699_b19","article-title":"SISMIK for brain MRI: Deep-learning-based motion estimation and model-based motion correction in k-space","author":"Dabrowski","year":"2024","journal-title":"IEEE Trans. Med. Imaging"},{"issue":"2","key":"10.1016\/j.patcog.2026.113699_b20","doi-asserted-by":"crossref","first-page":"846","DOI":"10.1109\/TMI.2023.3323215","article-title":"Deep learning for retrospective motion correction in MRI: a comprehensive review","volume":"43","author":"Spieker","year":"2023","journal-title":"IEEE Trans. Med. Imaging"},{"key":"10.1016\/j.patcog.2026.113699_b21","series-title":"ICIP","first-page":"3165","article-title":"Reducing motion artifacts in brain MRI using vision transformers and self-supervised learning","author":"Zhang","year":"2024"},{"key":"10.1016\/j.patcog.2026.113699_b22","doi-asserted-by":"crossref","DOI":"10.1016\/j.media.2025.103502","article-title":"Joint coil sensitivity and motion correction in parallel MRI with a self-calibrating score-based diffusion model","volume":"102","author":"Chen","year":"2025","journal-title":"Med. Image Anal."},{"key":"10.1016\/j.patcog.2026.113699_b23","doi-asserted-by":"crossref","unstructured":"P. Angella, V.P. Pastore, M. Santacesaria, Assessing the use of diffusion models for motion artifact correction in brain mri, in: International Symposium on Biomedical Imaging, ISBI, 2025, pp. 1\u20134.","DOI":"10.1109\/ISBI60581.2025.10981000"},{"key":"10.1016\/j.patcog.2026.113699_b24","series-title":"MICCAI","first-page":"411","article-title":"MoCo-Diff: Adaptive conditional prior on diffusion network for mri motion correction","author":"Li","year":"2024"},{"key":"10.1016\/j.patcog.2026.113699_b25","series-title":"CVPR","first-page":"766","article-title":"Residual local feature network for efficient super-resolution","author":"Kong","year":"2022"},{"key":"10.1016\/j.patcog.2026.113699_b26","series-title":"CVPR","first-page":"25837","article-title":"CAMixerSR: Only details need more \u201dattention\u201d","author":"Wang","year":"2024"},{"issue":"5","key":"10.1016\/j.patcog.2026.113699_b27","doi-asserted-by":"crossref","first-page":"1273","DOI":"10.1002\/mrm.20656","article-title":"Matrix description of general motion correction applied to multishot images","volume":"54","author":"Batchelor","year":"2005","journal-title":"Magn. Reson. Med."},{"issue":"3","key":"10.1016\/j.patcog.2026.113699_b28","doi-asserted-by":"crossref","first-page":"901","DOI":"10.1002\/mrm.27772","article-title":"Conditional generative adversarial network for 3D rigid-body motion correction in MRI","volume":"82","author":"Johnson","year":"2019","journal-title":"Magn. Reson. Med."},{"key":"10.1016\/j.patcog.2026.113699_b29","series-title":"CVPR","first-page":"770","article-title":"Deep residual learning for image recognition","author":"He","year":"2016"},{"key":"10.1016\/j.patcog.2026.113699_b30","series-title":"CVPR","first-page":"618","article-title":"Grad-cam: Visual explanations from deep networks via gradient-based localization","author":"Selvaraju","year":"2017"},{"key":"10.1016\/j.patcog.2026.113699_b31","series-title":"NeurIPS","first-page":"22243","article-title":"Big self-supervised models are strong semi-supervised learners","author":"Chen","year":"2020"},{"key":"10.1016\/j.patcog.2026.113699_b32","series-title":"ICLR","article-title":"Categorical reparameterization with Gumbel-Softmax","author":"Jang","year":"2017"},{"key":"10.1016\/j.patcog.2026.113699_b33","series-title":"CVPR","first-page":"7132","article-title":"Squeeze-and-excitation networks","author":"Hu","year":"2018"},{"key":"10.1016\/j.patcog.2026.113699_b34","series-title":"MICCAI","first-page":"234","article-title":"U-net: Convolutional networks for biomedical image segmentation","author":"Ronneberger","year":"2015"},{"issue":"4","key":"10.1016\/j.patcog.2026.113699_b35","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.2026.113699_b36","doi-asserted-by":"crossref","first-page":"891","DOI":"10.1016\/j.neuroimage.2018.03.049","article-title":"The UNC\/UMN baby connectome project (BCP): An overview of the study design and protocol development","volume":"185","author":"Howell","year":"2019","journal-title":"NeuroImage"},{"issue":"1","key":"10.1016\/j.patcog.2026.113699_b37","doi-asserted-by":"crossref","first-page":"630","DOI":"10.1038\/s41597-022-01694-8","article-title":"Movement-related artefacts (MR-ART) dataset of matched motion-corrupted and clean structural MRI brain scans","volume":"9","author":"N\u00e1rai","year":"2022","journal-title":"Sci. Data"},{"key":"10.1016\/j.patcog.2026.113699_b38","series-title":"NeurIPS","article-title":"Pytorch: An imperative style, high-performance deep learning library","author":"Paszke","year":"2019"},{"key":"10.1016\/j.patcog.2026.113699_b39","unstructured":"K.D.B.J. Adam, et al. A method for stochastic optimization 1412 (6) (2014) arXiv preprint arXiv:1412.6980."},{"key":"10.1016\/j.patcog.2026.113699_b40","series-title":"CVPR","first-page":"5728","article-title":"Restormer: Efficient transformer for high-resolution image restoration","author":"Zamir","year":"2022"},{"key":"10.1016\/j.patcog.2026.113699_b41","series-title":"CVPR","first-page":"1833","article-title":"Swinir: Image restoration using swin transformer","author":"Liang","year":"2021"},{"key":"10.1016\/j.patcog.2026.113699_b42","series-title":"CVPR","first-page":"2773","article-title":"Residual denoising diffusion models","author":"Liu","year":"2024"},{"issue":"1","key":"10.1016\/j.patcog.2026.113699_b43","doi-asserted-by":"crossref","first-page":"45","DOI":"10.1109\/42.906424","article-title":"Segmentation of brain MR images through a hidden Markov random field model and the expectation-maximization algorithm","volume":"20","author":"Zhang","year":"2001","journal-title":"IEEE Trans. Med. Imaging"},{"issue":"Nov","key":"10.1016\/j.patcog.2026.113699_b44","first-page":"2579","article-title":"Visualizing data using t-SNE","volume":"9","author":"Maaten","year":"2008","journal-title":"J. Mach. Learn. Res."},{"key":"10.1016\/j.patcog.2026.113699_b45","doi-asserted-by":"crossref","DOI":"10.1007\/978-1-4757-1904-8","article-title":"Principal component analysis","author":"Jolliffe","year":"1986"}],"container-title":["Pattern Recognition"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0031320326006643?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0031320326006643?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,6,12]],"date-time":"2026-06-12T21:12:25Z","timestamp":1781298745000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0031320326006643"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,11]]},"references-count":45,"alternative-id":["S0031320326006643"],"URL":"https:\/\/doi.org\/10.1016\/j.patcog.2026.113699","relation":{},"ISSN":["0031-3203"],"issn-type":[{"value":"0031-3203","type":"print"}],"subject":[],"published":{"date-parts":[[2026,11]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"FLEX-MoCo: Flexible MRI motion correction using motion recognition and adaptive routing","name":"articletitle","label":"Article Title"},{"value":"Pattern Recognition","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.patcog.2026.113699","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2026 Elsevier Ltd. All rights are reserved, including those for text and data mining, AI training, and similar technologies.","name":"copyright","label":"Copyright"}],"article-number":"113699"}}