{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,16]],"date-time":"2026-06-16T03:51:57Z","timestamp":1781581917508,"version":"3.54.5"},"reference-count":72,"publisher":"Wiley","issue":"2","license":[{"start":{"date-parts":[[2026,3,24]],"date-time":"2026-03-24T00:00:00Z","timestamp":1774310400000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"},{"start":{"date-parts":[[2026,3,24]],"date-time":"2026-03-24T00:00:00Z","timestamp":1774310400000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"funder":[{"DOI":"10.13039\/501100000923","name":"Australian Research Council","doi-asserted-by":"publisher","award":["DE20101297"],"award-info":[{"award-number":["DE20101297"]}],"id":[{"id":"10.13039\/501100000923","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100000923","name":"Australian Research Council","doi-asserted-by":"publisher","award":["DP230101628"],"award-info":[{"award-number":["DP230101628"]}],"id":[{"id":"10.13039\/501100000923","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100000925","name":"National Health and Medical Research Council","doi-asserted-by":"publisher","award":["2030157"],"award-info":[{"award-number":["2030157"]}],"id":[{"id":"10.13039\/501100000925","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62301616"],"award-info":[{"award-number":["62301616"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Magnetic Resonance in Med"],"published-print":{"date-parts":[[2026,8]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Purpose<\/jats:title>\n                    <jats:p>To develop a generative diffusion model\u2010based approach for robust and efficient quantitative susceptibility mapping (QSM) reconstruction in intracranial hemorrhage (ICH), applicable to both standard gradient echo (GRE) and rapid echo planar imaging (EPI) acquisitions.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>QSMDiff, an unsupervised diffusion model for 3D QSM dipole inversion, was proposed. Three volumetric partitioning strategies including 2D slices, 3D patches, and 2.5D slabs were evaluated, and the memory\u2010efficient 2.5D slab approach was adopted to balance accuracy and efficiency while maintaining anatomical fidelity. A conditional sampling mechanism ensured consistency with measured local fields, and a three\u2010stage training data\u2010generation strategy combining public dataset, synthetic QSM, and ICH lesions simulated from in vivo patients was implemented to overcome data scarcity.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      QSMDiff achieved the best overall performance in simulation studies, with SSIM of 0.97\u2009\u00b1\u20090.07, RMSE of 0.04\u2009\u00b1\u20090.03, and HFEN of 4.49\u2009\u00b1\u20090.83, demonstrating superior structural fidelity and noise suppression. For in vivo ICH patients scanned with rapid EPI, QSMDiff showed strong agreement with SWI\u2010QSM references (\n                      <jats:italic>R<\/jats:italic>\n                      <jats:sup>2<\/jats:sup>\n                      \u2009=\u20090.83), producing susceptibility estimates with minimal bias and variance. Qualitative evaluation confirmed enhanced resolution and effective artifact suppression in conditions of low SNR, limited resolution, and motion.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>QSMDiff achieves high\u2010quality and accurate QSM reconstruction from both standard GRE and rapid EPI scans for ICH assessment. By integrating a 2.5D training strategy with synthetic ICH augmentation, it delivers accurate and reliable susceptibility maps even from lower\u2010quality acquisitions, offering a practical solution for fast and robust ICH assessment.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1002\/mrm.70358","type":"journal-article","created":{"date-parts":[[2026,3,24]],"date-time":"2026-03-24T08:45:50Z","timestamp":1774341950000},"page":"596-610","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Advancing Quantitative Susceptibility Mapping With 2.5D Diffusion Models for Rapid Intracranial Hemorrhage Quantification"],"prefix":"10.1002","volume":"96","author":[{"given":"Zhuang","family":"Xiong","sequence":"first","affiliation":[{"name":"Image X Institute, Sydney School of Health Sciences, Faculty of Medicine and Health University of Sydney  Sydney Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3846-0999","authenticated-orcid":false,"given":"Yang","family":"Gao","sequence":"additional","affiliation":[{"name":"School of Computer Science and Engineering Central South University  Changsha China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Feng","family":"Liu","sequence":"additional","affiliation":[{"name":"School of Electrical Engineering and Computer Science University of Queensland  Brisbane Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Derek","family":"Emery","sequence":"additional","affiliation":[{"name":"Department of Biomedical Engineering University of Alberta  Edmonton Canada"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ken","family":"Butcher","sequence":"additional","affiliation":[{"name":"School of Clinical Medicine University of New South Wales  Sydney Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Alan H.","family":"Wilman","sequence":"additional","affiliation":[{"name":"Department of Biomedical Engineering University of Alberta  Edmonton Canada"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hongfu","family":"Sun","sequence":"additional","affiliation":[{"name":"School of Engineering University of Newcastle  Newcastle Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2026,3,24]]},"reference":[{"key":"e_1_2_9_2_1","doi-asserted-by":"publisher","DOI":"10.1002\/mrm.25358"},{"key":"e_1_2_9_3_1","doi-asserted-by":"publisher","DOI":"10.1002\/jmri.24768"},{"key":"e_1_2_9_4_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.msard.2019.05.028"},{"key":"e_1_2_9_5_1","doi-asserted-by":"publisher","DOI":"10.1148\/radiol.12120707"},{"key":"e_1_2_9_6_1","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0081093"},{"key":"e_1_2_9_7_1","doi-asserted-by":"publisher","DOI":"10.1136\/jnnp-2019-322042"},{"key":"e_1_2_9_8_1","doi-asserted-by":"publisher","DOI":"10.3174\/ajnr.A4617"},{"key":"e_1_2_9_9_1","doi-asserted-by":"publisher","DOI":"10.1002\/mrm.24918"},{"key":"e_1_2_9_10_1","doi-asserted-by":"publisher","DOI":"10.1002\/mrm.27907"},{"key":"e_1_2_9_11_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.neuroimage.2014.06.011"},{"key":"e_1_2_9_12_1","doi-asserted-by":"publisher","DOI":"10.1002\/nbm.3619"},{"key":"e_1_2_9_13_1","doi-asserted-by":"publisher","DOI":"10.1002\/mrm.28390"},{"key":"e_1_2_9_14_1","doi-asserted-by":"publisher","DOI":"10.1002\/mrm.25463"},{"key":"e_1_2_9_15_1","doi-asserted-by":"publisher","DOI":"10.1016\/S1474-4422(09)70340-0"},{"key":"e_1_2_9_16_1","doi-asserted-by":"publisher","DOI":"10.1016\/S1474-4422(09)70025-0"},{"key":"e_1_2_9_17_1","doi-asserted-by":"publisher","DOI":"10.1002\/mrm.25919"},{"key":"e_1_2_9_18_1","doi-asserted-by":"publisher","DOI":"10.1148\/radiol.13122640"},{"key":"e_1_2_9_19_1","doi-asserted-by":"publisher","DOI":"10.1148\/radiol.2018171918"},{"key":"e_1_2_9_20_1","doi-asserted-by":"publisher","DOI":"10.1002\/mrm.25029"},{"key":"e_1_2_9_21_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.neuroimage.2012.05.067"},{"key":"e_1_2_9_22_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.neuroimage.2017.11.018"},{"key":"e_1_2_9_23_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.neuroimage.2014.11.038"},{"key":"e_1_2_9_24_1","doi-asserted-by":"publisher","DOI":"10.1002\/mrm.22816"},{"key":"e_1_2_9_25_1","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2016.2544958"},{"key":"e_1_2_9_26_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.neuroimage.2014.12.043"},{"key":"e_1_2_9_27_1","doi-asserted-by":"publisher","DOI":"10.1002\/mrm.22135"},{"key":"e_1_2_9_28_1","doi-asserted-by":"publisher","DOI":"10.1002\/jmri.26850"},{"key":"e_1_2_9_29_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.neuroimage.2015.02.041"},{"key":"e_1_2_9_30_1","doi-asserted-by":"publisher","DOI":"10.1002\/mrm.25316"},{"key":"e_1_2_9_31_1","volume-title":"Medical Image Computing and Computer\u2010Assisted Intervention\u2013MICCAI 2015: 18th International Conference, Munich, Germany, October 5\u20139, 2015, Proceedings, Part III 18","author":"Ronneberger O.","year":"2015"},{"key":"e_1_2_9_32_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.neuroimage.2019.03.060"},{"key":"e_1_2_9_33_1","doi-asserted-by":"publisher","DOI":"10.1002\/nbm.4461"},{"key":"e_1_2_9_34_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.neuroimage.2018.06.030"},{"key":"e_1_2_9_35_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.neuroimage.2019.116389"},{"key":"e_1_2_9_36_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.neuroimage.2022.119842"},{"key":"e_1_2_9_37_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.neuroimage.2021.118376"},{"key":"e_1_2_9_38_1","doi-asserted-by":"publisher","DOI":"10.1002\/nbm.4271"},{"key":"e_1_2_9_39_1","first-page":"125","article-title":"Learned Proximal Networks for Quantitative Susceptibility Mapping","volume":"12262","author":"Lai K. W.","year":"2020","journal-title":"Medical Image Computing and Computer Assisted Intervention"},{"key":"e_1_2_9_40_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2022.102477"},{"key":"e_1_2_9_41_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2024.103173"},{"key":"e_1_2_9_42_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.neuroimage.2024.120583"},{"key":"e_1_2_9_43_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.neuroimage.2022.119410"},{"key":"e_1_2_9_44_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2024.103160"},{"key":"e_1_2_9_45_1","first-page":"6840","article-title":"Denoising Diffusion Probabilistic Models","volume":"33","author":"Ho J.","year":"2020","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_2_9_46_1","unstructured":"Y.Song J.Sohl\u2010Dickstein D. P.Kingma A.Kumar S.Ermon andB.Poole \u201cScore\u2010Based Generative Modeling Through Stochastic Differential Equations \u201d arXiv Preprint arXiv:2011.13456 2020."},{"key":"e_1_2_9_47_1","doi-asserted-by":"publisher","DOI":"10.1109\/ISBI56570.2024.10635891"},{"key":"e_1_2_9_48_1","doi-asserted-by":"publisher","DOI":"10.1002\/mrm.29624"},{"key":"e_1_2_9_49_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2023.102872"},{"key":"e_1_2_9_50_1","doi-asserted-by":"publisher","DOI":"10.1109\/WACV61041.2025.00018"},{"key":"e_1_2_9_51_1","volume-title":"Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition","author":"Rombach R.","year":"2022"},{"key":"e_1_2_9_52_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-031-43999-5_42"},{"key":"e_1_2_9_53_1","doi-asserted-by":"publisher","DOI":"10.1109\/WACV57701.2024.00743"},{"key":"e_1_2_9_54_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-023-34341-2"},{"key":"e_1_2_9_55_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-031-18576-2_12"},{"key":"e_1_2_9_56_1","doi-asserted-by":"publisher","DOI":"10.1002\/advs.202307965"},{"key":"e_1_2_9_57_1","unstructured":"Z.Xiong W.Jiang Y.Gao F.Liu andH.Sun \u201cQSMDiff: Unsupervised 3D Diffusion Models for Quantitative Susceptibility Mapping \u201d arXiv Preprint arXiv:2403.14070 2024."},{"key":"e_1_2_9_58_1","volume-title":"Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition","author":"Chung H.","year":"2023"},{"key":"e_1_2_9_59_1","volume-title":"Proceedings of the IEEE\/CVF International Conference on Computer Vision","author":"Lee S.","year":"2023"},{"key":"e_1_2_9_60_1","first-page":"1","volume-title":"IEEE Transactions on Biomedical Engineering","author":"Zhang M.","year":"2025"},{"key":"e_1_2_9_61_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.bspc.2025.108217"},{"key":"e_1_2_9_62_1","doi-asserted-by":"publisher","DOI":"10.1002\/nbm.3064"},{"key":"e_1_2_9_63_1","unstructured":"H.Chung J.Kim M. T.Mccann M. L.Klasky andJ. C.Ye \u201cDiffusion Posterior Sampling for General Noisy Inverse Problems \u201d arXiv Preprint arXiv:2209.14687 2022."},{"key":"e_1_2_9_64_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.neuroimage.2022.119522"},{"key":"e_1_2_9_65_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.neuroimage.2018.06.036"},{"key":"e_1_2_9_66_1","doi-asserted-by":"publisher","DOI":"10.1002\/mrm.24765"},{"key":"e_1_2_9_67_1","first-page":"8780","article-title":"Diffusion Models Beat Gans on Image Synthesis","volume":"34","author":"Dhariwal P.","year":"2021","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_2_9_68_1","unstructured":"J.Song C.Meng andS.Ermon \u201cDenoising Diffusion Implicit Models \u201d arXiv Preprint arXiv:2010.02502 2020."},{"key":"e_1_2_9_69_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.neuroimage.2023.120419"},{"key":"e_1_2_9_70_1","doi-asserted-by":"publisher","DOI":"10.1002\/mrm.10354"},{"key":"e_1_2_9_71_1","unstructured":"M.Jazbec E.Wong\u2010Toi G.Xia D.Zhang E.Nalisnick andS.Mandt \u201cGenerative Uncertainty in Diffusion Models \u201d arXiv Preprint arXiv:2502.20946 2025."},{"key":"e_1_2_9_72_1","first-page":"1335","volume-title":"Proceedings of the 40th International Conference on Machine Learning","author":"Song Y.","year":"2023"},{"key":"e_1_2_9_73_1","article-title":"Highly Undersampled Mri Reconstruction via a Single Posterior Sampling of Diffusion Models","author":"Liu J.","year":"2026","journal-title":"IEEE Transactions on Medical Imaging"}],"container-title":["Magnetic Resonance in Medicine"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/pdf\/10.1002\/mrm.70358","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/full-xml\/10.1002\/mrm.70358","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/pdf\/10.1002\/mrm.70358","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,6,16]],"date-time":"2026-06-16T02:59:48Z","timestamp":1781578788000},"score":1,"resource":{"primary":{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/10.1002\/mrm.70358"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,3,24]]},"references-count":72,"journal-issue":{"issue":"2","published-print":{"date-parts":[[2026,8]]}},"alternative-id":["10.1002\/mrm.70358"],"URL":"https:\/\/doi.org\/10.1002\/mrm.70358","archive":["Portico"],"relation":{},"ISSN":["0740-3194","1522-2594"],"issn-type":[{"value":"0740-3194","type":"print"},{"value":"1522-2594","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,3,24]]},"assertion":[{"value":"2025-09-16","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2026-03-10","order":2,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2026-03-24","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}