{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,26]],"date-time":"2026-09-26T03:26:41Z","timestamp":1790393201885,"version":"4.1.0"},"reference-count":41,"publisher":"Wiley","issue":"9","license":[{"start":{"date-parts":[[2025,9,10]],"date-time":"2025-09-10T00:00:00Z","timestamp":1757462400000},"content-version":"vor","delay-in-days":9,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"}],"funder":[{"DOI":"10.13039\/501100004377","name":"Hong Kong Polytechnic University","doi-asserted-by":"publisher","award":["P0035421"],"award-info":[{"award-number":["P0035421"]}],"id":[{"id":"10.13039\/501100004377","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000002","name":"National Institutes of Health","doi-asserted-by":"publisher","award":["R01 CA226899"],"award-info":[{"award-number":["R01 CA226899"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["82202941"],"award-info":[{"award-number":["82202941"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["aapm.onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Medical Physics"],"published-print":{"date-parts":[[2025,9]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Four\u2010dimensional magnetic resonance imaging (4D\u2010MRI) holds great promise for precise abdominal radiotherapy guidance. However, current 4D\u2010MRI methods are limited by an inherent trade\u2010off between spatial and temporal resolutions, resulting in compromised image quality characterized by low spatial resolution and significant motion artifacts, hindering clinical implementation. Despite recent advancements, existing methods inadequately exploit redundant frame information and struggle to restore structural details from highly undersampled acquisitions.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Purpose<\/jats:title>\n                    <jats:p>This study aims to develop a technique that leverages information across multiple frames to mitigate spatial undersampling, thereby enabling superior spatiotemporal resolution in abdominal\u00a04D\u2010MRI.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>We introduce a novel reconstruction approach for 4D\u2010MRI that leverages respiratory\u2010synchronized frames to reconstruct target frames with enhanced image quality. Specifically, we introduce a multi\u2010frame collaborative reconstruction network (MCR\u2010Net) that capitalizes on inter\u2010frame correlations and complementary information for faithful reconstruction. MCR\u2010Net integrates two key mechanisms: the Inter\u2010frame mutual\u2010attention mechanism (IMM) and the structure\u2010aware consolidation module (SaCM). IMM enhances feature extraction by exploiting correlations among neighboring respiratory\u2010synchronized frames, thereby reinforcing shared anatomical features while suppressing random artifacts and noise. SaCM consolidates structural information across frames by leveraging context\u2010aware residual learning, enhancing high\u2010frequency details, and filtering irrelevant data during multi\u2010frame fusion, thus significantly improving the clarity and anatomical\u00a0integrity.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      Experimental evaluations on clinical patient datasets (training:\n                      <jats:italic>n<\/jats:italic>\n                      \u00a0=\u00a020; validation:\n                      <jats:italic>n<\/jats:italic>\n                      \u00a0=\u00a06) demonstrate that our method significantly outperforms nine state\u2010of\u2010the\u2010art reconstruction approaches in both visual quality and quantitative accuracy. MCR\u2010Net achieves superior performance in MAE, SSIM, and PSNR, outperforming the next\u2010best methods by 3.77%, 1.03%, and 6.74%, respectively. Furthermore, our experiments validate that MCR\u2010Net enhances registration accuracy compared to original low\u2010quality 4D\u2010MRI by 10.66%, 3.60%, and 1.94% in MAE, SSIM, and NCC metrics. Additionally, simulations demonstrate that MCR\u2010Net effectively maintains high image quality even under significantly increased undersampling\u00a0ratios.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>Our findings demonstrate that MCR\u2010Net effectively suppresses artifacts and recovers missing anatomical structures from undersampled acquisitions, underscoring its potential to enhance 4D\u2010MRI's spatiotemporal resolution and advance clinical applications in abdominal\u00a0radiotherapy.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1002\/mp.18101","type":"journal-article","created":{"date-parts":[[2025,9,10]],"date-time":"2025-09-10T07:37:20Z","timestamp":1757489840000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["High spatiotemporal\u2010resolution abdominal 4D\u2010MRI through respiratory\u2010synchronized frame collaborative reconstruction"],"prefix":"10.1002","volume":"52","author":[{"given":"Yinghui","family":"Wang","sequence":"first","affiliation":[{"name":"Department of Health Technology and Informatics The Hong Kong Polytechnic University Hong Kong SAR China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lu","family":"Wang","sequence":"additional","affiliation":[{"name":"Department of Health Technology and Informatics The Hong Kong Polytechnic University Hong Kong SAR China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yidan","family":"Feng","sequence":"additional","affiliation":[{"name":"Centre for Smart Health Hong Kong Polytechnic University Hong Kong SAR China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhi","family":"Chen","sequence":"additional","affiliation":[{"name":"Department of Health Technology and Informatics The Hong Kong Polytechnic University Hong Kong SAR China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jing","family":"Qin","sequence":"additional","affiliation":[{"name":"Centre for Smart Health Hong Kong Polytechnic University Hong Kong SAR China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tian","family":"Li","sequence":"additional","affiliation":[{"name":"Department of Health Technology and Informatics The Hong Kong Polytechnic University Hong Kong SAR China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jing","family":"Cai","sequence":"additional","affiliation":[{"name":"Department of Health Technology and Informatics The Hong Kong Polytechnic University Hong Kong SAR China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2025,9,10]]},"reference":[{"key":"e_1_2_11_2_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijrobp.2013.02.021"},{"key":"e_1_2_11_3_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijrobp.2016.08.044"},{"key":"e_1_2_11_4_1","doi-asserted-by":"publisher","DOI":"10.1002\/mp.16469"},{"key":"e_1_2_11_5_1","doi-asserted-by":"publisher","DOI":"10.1118\/1.3658737"},{"key":"e_1_2_11_6_1","doi-asserted-by":"publisher","DOI":"10.1118\/1.4894726"},{"key":"e_1_2_11_7_1","doi-asserted-by":"publisher","DOI":"10.1002\/mp.12037"},{"key":"e_1_2_11_8_1","doi-asserted-by":"publisher","DOI":"10.21037\/qims.2019.12.10"},{"key":"e_1_2_11_9_1","doi-asserted-by":"publisher","DOI":"10.1118\/1.4923168"},{"key":"e_1_2_11_10_1","doi-asserted-by":"publisher","DOI":"10.1002\/mp.15217"},{"key":"e_1_2_11_11_1","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2012.2215921"},{"key":"e_1_2_11_12_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.radonc.2021.03.034"},{"key":"e_1_2_11_13_1","doi-asserted-by":"publisher","DOI":"10.1002\/mp.13717"},{"key":"e_1_2_11_14_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.mri.2022.10.010"},{"key":"e_1_2_11_15_1","doi-asserted-by":"publisher","DOI":"10.1097\/RLI.0000000000000792"},{"key":"e_1_2_11_16_1","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2023.3294245"},{"key":"e_1_2_11_17_1","doi-asserted-by":"crossref","unstructured":"LecouatB PonceJ MairalJ.Lucas\u2010kanade reloaded: End\u2010to\u2010end super\u2010resolution from raw image bursts. In:Proceedings of the IEEE\/CVF International Conference on Computer Vision.2021;2370\u20102379.","DOI":"10.1109\/ICCV48922.2021.00237"},{"key":"e_1_2_11_18_1","doi-asserted-by":"crossref","unstructured":"WangX ChanKCK YuK et\u00a0al.EDVR: video restoration with enhanced deformable convolutional networks. In:Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition Workshops.2019:0\u20100.","DOI":"10.1109\/CVPRW.2019.00247"},{"key":"e_1_2_11_19_1","doi-asserted-by":"crossref","unstructured":"DudhaneA ZamirSW KhanS et\u00a0al.Burst image restoration and enhancement. In:Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition.2022;5759\u20105768.","DOI":"10.1109\/CVPR52688.2022.00567"},{"key":"e_1_2_11_20_1","doi-asserted-by":"crossref","unstructured":"DudhaneA ZamirSW KhanS KhanFS YangMH.Burstormer: Burst image restoration and enhancement transformer. In:Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition.IEEE;2023:5703\u20105712.","DOI":"10.1109\/CVPR52729.2023.00552"},{"key":"e_1_2_11_21_1","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2024.3445969"},{"key":"e_1_2_11_22_1","doi-asserted-by":"crossref","unstructured":"DaiJ QiH XiongY LiY ZhangG HuH WeiY.Deformable convolutional networks. In:Proceedings of the IEEE International Conference on Computer Vision.2017:764\u2010773.","DOI":"10.1109\/ICCV.2017.89"},{"key":"e_1_2_11_23_1","doi-asserted-by":"crossref","unstructured":"TianY ZhangY FuY et al.TDAN: Temporally\u2010deformable alignment network for video super\u2010resolution. In:Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition.2020;3360\u20103369.","DOI":"10.1109\/CVPR42600.2020.00342"},{"key":"e_1_2_11_24_1","first-page":"317","article-title":"Multiframe image restoration and registration","volume":"1","author":"Tsai RY","year":"1984","journal-title":"Multiframe Image Restoration and Registration"},{"key":"e_1_2_11_25_1","doi-asserted-by":"crossref","unstructured":"BhatG DanelljanM Van GoolL TimofteR.Deep burst super\u2010resolution. In:Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition.2021;9209\u20109218.","DOI":"10.1109\/CVPR46437.2021.00909"},{"key":"e_1_2_11_26_1","doi-asserted-by":"crossref","unstructured":"WeiP SunY GuoX et\u00a0al.Towards real\u2010world burst image super\u2010resolution: Benchmark and method. In:Proceedings of the IEEE\/CVF International Conference on Computer Vision.2023;13233\u201013242.","DOI":"10.1109\/ICCV51070.2023.01217"},{"key":"e_1_2_11_27_1","doi-asserted-by":"publisher","DOI":"10.1088\/1361-6560\/aae56d"},{"key":"e_1_2_11_28_1","unstructured":"MirzaM OsinderoS.Conditional generative adversarial nets.arXiv preprint arXiv:1411.1784.2014."},{"key":"e_1_2_11_29_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v37i3.25440"},{"key":"e_1_2_11_30_1","unstructured":"WangZ SimoncelliEP BovikAC.Multiscale structural similarity for image quality assessment. In:The Thirty\u2010Seventh Asilomar Conference on Signals Systems & Computers. vol.2.IEEE;2003:1398\u20101402."},{"key":"e_1_2_11_31_1","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2019.2901750"},{"key":"e_1_2_11_32_1","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2009.2035616"},{"key":"e_1_2_11_33_1","doi-asserted-by":"crossref","unstructured":"LimB SonS KimH NahS Mu LeeK.Enhanced deep residual networks for single image super\u2010resolution. In:Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops.2017;136\u2010144.","DOI":"10.1109\/CVPRW.2017.151"},{"key":"e_1_2_11_34_1","doi-asserted-by":"crossref","unstructured":"ZamirSW AroraA KhanS HayatM KhanFS YangMH.Restormer: efficient transformer for high\u2010resolution image restoration. In:Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition.2022;5728\u20105739.","DOI":"10.1109\/CVPR52688.2022.00564"},{"key":"e_1_2_11_35_1","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2022.3167808"},{"key":"e_1_2_11_36_1","doi-asserted-by":"publisher","DOI":"10.1002\/mp.15806"},{"key":"e_1_2_11_37_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijrobp.2017.04.023"},{"key":"e_1_2_11_38_1","doi-asserted-by":"crossref","unstructured":"BenitezCM ChuongMD K\u00fcnzelLA ThorwarthD.MRI\u2010guided adaptive radiation therapy. In:Seminars in Radiation Oncology vol.34.Elsevier;2024:84\u201091.","DOI":"10.1016\/j.semradonc.2023.10.013"},{"key":"e_1_2_11_39_1","doi-asserted-by":"publisher","DOI":"10.1088\/0031-9155\/50\/12\/011"},{"key":"e_1_2_11_40_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.radonc.2023.109948"},{"key":"e_1_2_11_41_1","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2002.808360"},{"key":"e_1_2_11_42_1","doi-asserted-by":"publisher","DOI":"10.1002\/jmri.28187"}],"container-title":["Medical Physics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/aapm.onlinelibrary.wiley.com\/doi\/pdf\/10.1002\/mp.18101","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,12,10]],"date-time":"2025-12-10T04:02:00Z","timestamp":1765339320000},"score":1,"resource":{"primary":{"URL":"https:\/\/aapm.onlinelibrary.wiley.com\/doi\/10.1002\/mp.18101"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,9]]},"references-count":41,"journal-issue":{"issue":"9","published-print":{"date-parts":[[2025,9]]}},"alternative-id":["10.1002\/mp.18101"],"URL":"https:\/\/doi.org\/10.1002\/mp.18101","archive":["Portico"],"relation":{},"ISSN":["0094-2405","2473-4209"],"issn-type":[{"value":"0094-2405","type":"print"},{"value":"2473-4209","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,9]]},"assertion":[{"value":"2025-05-24","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2025-07-24","order":2,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2025-09-10","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}],"article-number":"e18101"}}