{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,16]],"date-time":"2026-04-16T06:55:45Z","timestamp":1776322545845,"version":"3.50.1"},"reference-count":38,"publisher":"Wiley","issue":"5","license":[{"start":{"date-parts":[[2026,4,9]],"date-time":"2026-04-09T00:00:00Z","timestamp":1775692800000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"},{"start":{"date-parts":[[2026,4,9]],"date-time":"2026-04-09T00:00:00Z","timestamp":1775692800000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"funder":[{"DOI":"10.13039\/501100003246","name":"Nederlandse Organisatie voor Wetenschappelijk Onderzoek","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100003246","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100004320","name":"Philips","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100004320","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100016238","name":"Ministerie van Economische Zaken en Klimaat","doi-asserted-by":"publisher","award":["LTP KIC 2020\u20102023"],"award-info":[{"award-number":["LTP KIC 2020\u20102023"]}],"id":[{"id":"10.13039\/501100016238","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["analyticalsciencejournals.onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["NMR in Biomedicine"],"published-print":{"date-parts":[[2026,5]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:p>3D MR image acquisition is inherently time intensive, rendering it susceptible to patient motion during scanning. This may introduce significant blurring and artifacts, potentially necessitating reacquisition. We propose a modular framework to retrospectively correct for intrascan motion in 3D brain MRI, without active motion tracking. Serving as the backbone of our approach is an existing distributed and incoherent sampling scheme (DISORDER), combined with a fast network trained for highly undersampled reconstruction. This enables approximate reconstructions of anatomy after every few seconds, using only a tiny fraction of k\u2010space data (&lt;\u20092%). While these reconstructions are only approximate, we postulate they are sufficient to estimate motion patterns at said temporal resolution. Groupwise registration, notable for its elimination of registration bias, is utilized for estimating rigid motion parameters, which are leveraged to reconstruct the measured data with reduced motion artifacts. The approach was evaluated on 94 retrospectively and 3 prospectively motion\u2010corrupted in\u00a0vivo 3D T1\u2010weighted brain MRI acquisitions. The estimated motion parameters matched the known retrospective motion with 0.06\u2009mm and 0.13\u00b0 accuracy, resulting in an improvement in reconstruction quality from  to  SSIM for the retrospective scans. The prospective scans improved from  to SSIM after correction in the case of gradual motion and from  to  SSIM for extreme motion. In conclusion, the proposed approach, that is free of external tracking devices or navigators, successfully estimated and corrected 3D motion between small subportions of a scan. This resulted in vastly improved image quality, making volumetric MRI substantially more tolerant to motion.<\/jats:p>","DOI":"10.1002\/nbm.70286","type":"journal-article","created":{"date-parts":[[2026,4,10]],"date-time":"2026-04-10T06:31:06Z","timestamp":1775802666000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["DEEP\u2010DISORDER: Motion Correction in 3D MRI via Segment Reconstruction and Registration"],"prefix":"10.1002","volume":"39","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2434-5500","authenticated-orcid":false,"given":"Laurens","family":"Beljaards","sequence":"first","affiliation":[{"name":"Department of Radiology Leiden University Medical Center  Leiden the Netherlands"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6563-865X","authenticated-orcid":false,"given":"Martijn","family":"Nagtegaal","sequence":"additional","affiliation":[{"name":"Department of Radiology Leiden University Medical Center  Leiden the Netherlands"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chinmay","family":"Rao","sequence":"additional","affiliation":[{"name":"Department of Radiology Leiden University Medical Center  Leiden the Netherlands"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yiming","family":"Dong","sequence":"additional","affiliation":[{"name":"Department of Radiology Leiden University Medical Center  Leiden the Netherlands"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7034-8959","authenticated-orcid":false,"given":"Matthias\u00a0J.","family":"P.\u00a0van\u00a0Osch","sequence":"additional","affiliation":[{"name":"Department of Radiology Leiden University Medical Center  Leiden the Netherlands"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Nicola","family":"Pezzotti","sequence":"additional","affiliation":[{"name":"Department of Mathematics and Computer Science Eindhoven University of Technology  Eindhoven the Netherlands"},{"name":"Cardiologs  Paris France"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mariya","family":"Doneva","sequence":"additional","affiliation":[{"name":"Philips Innovative Technologies  Hamburg Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Marius","family":"Staring","sequence":"additional","affiliation":[{"name":"Department of Radiology Leiden University Medical Center  Leiden the Netherlands"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","published-online":{"date-parts":[[2026,4,9]]},"reference":[{"key":"e_1_2_10_2_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jacr.2015.03.007"},{"key":"e_1_2_10_3_1","doi-asserted-by":"publisher","DOI":"10.1002\/jmri.27112"},{"key":"e_1_2_10_4_1","doi-asserted-by":"publisher","DOI":"10.1088\/0031\u20109155\/61\/5\/R32"},{"key":"e_1_2_10_5_1","doi-asserted-by":"publisher","DOI":"10.1002\/jmri.24850"},{"key":"e_1_2_10_6_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.metrad.2023.100001"},{"key":"e_1_2_10_7_1","doi-asserted-by":"publisher","DOI":"10.1109\/tmi.2023.3323215"},{"key":"e_1_2_10_8_1","doi-asserted-by":"publisher","DOI":"10.1109\/42.650886"},{"key":"e_1_2_10_9_1","doi-asserted-by":"publisher","DOI":"10.1002\/mrm.24615"},{"key":"e_1_2_10_10_1","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2018.2791482"},{"key":"e_1_2_10_11_1","doi-asserted-by":"publisher","DOI":"10.1109\/TCI.2016.2557069"},{"key":"e_1_2_10_12_1","doi-asserted-by":"publisher","DOI":"10.1002\/mrm.28157"},{"key":"e_1_2_10_13_1","doi-asserted-by":"publisher","DOI":"10.1002\/mrm.27771"},{"key":"e_1_2_10_14_1","doi-asserted-by":"publisher","DOI":"10.1002\/mrm.28971"},{"key":"e_1_2_10_15_1","first-page":"368","article-title":"Data Consistent Deep Rigid MRI Motion Correction","volume":"227","author":"Singh N. 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