{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,15]],"date-time":"2026-06-15T03:53:39Z","timestamp":1781495619188,"version":"3.54.1"},"reference-count":42,"publisher":"Springer Science and Business Media LLC","issue":"6","license":[{"start":{"date-parts":[[2026,5,19]],"date-time":"2026-05-19T00:00:00Z","timestamp":1779148800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2026,5,19]],"date-time":"2026-05-19T00:00:00Z","timestamp":1779148800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Med Biol Eng Comput"],"published-print":{"date-parts":[[2026,6]]},"DOI":"10.1007\/s11517-026-03589-x","type":"journal-article","created":{"date-parts":[[2026,5,19]],"date-time":"2026-05-19T07:55:33Z","timestamp":1779177333000},"page":"2363-2376","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Multi-channel spatial attention network for MRI super-resolution"],"prefix":"10.1007","volume":"64","author":[{"given":"Haohuai","family":"Gui","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4501-2684","authenticated-orcid":false,"given":"Yu","family":"Ma","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,5,19]]},"reference":[{"issue":"4","key":"3589_CR1","first-page":"343","volume":"91","author":"J Franco","year":"2020","unstructured":"Franco J (2020) Magnetic resonance imaging safety. Radiol Technol 91(4):343\u2013356","journal-title":"Radiol Technol"},{"key":"3589_CR2","doi-asserted-by":"crossref","unstructured":"Guo S, Yan Z, Zhang K, Zuo W, Zhang L (2019) Toward convolutional blind denoising of real photographs. Proc IEEE\/CVF Conf Comput Vis Pattern Recognit pp.1712\u20131722","DOI":"10.1109\/CVPR.2019.00181"},{"issue":"7","key":"3589_CR3","doi-asserted-by":"publisher","first-page":"3441","DOI":"10.1109\/TCYB.2019.2933633","volume":"51","author":"Z Li","year":"2019","unstructured":"Li Z, Yu J, Wang Y, Zhou H, Yang H, Qiao Z (2019) Deepvolume: brain structure and spatial connection-aware network for brain MRI super-resolution. IEEE Trans Cybern 51(7):3441\u20133454","journal-title":"IEEE Trans Cybern"},{"issue":"12","key":"3589_CR4","doi-asserted-by":"publisher","first-page":"5897","DOI":"10.1109\/TIP.2019.2920510","volume":"28","author":"L Chen","year":"2019","unstructured":"Chen L, Pan J, Li Q (2019) Robust face image super-resolution via joint learning of subdivided contextual model. IEEE Trans Image Process 28(12):5897\u20135909","journal-title":"IEEE Trans Image Process"},{"issue":"5","key":"3589_CR5","doi-asserted-by":"publisher","first-page":"3006","DOI":"10.1109\/TCYB.2020.3023127","volume":"52","author":"X Wang","year":"2020","unstructured":"Wang X, Li T, Cheng Y, Chen CP (2020) Inference-based posteriori parameter distribution optimization. IEEE Trans Cybern 52(5):3006\u20133017","journal-title":"IEEE Trans Cybern"},{"key":"3589_CR6","doi-asserted-by":"publisher","DOI":"10.1016\/j.cmpb.2021.106330","volume":"209","author":"D Zhu","year":"2021","unstructured":"Zhu D, Qiu D (2021) Residual dense network for medical magnetic resonance images super-resolution. Comput Methods Programs Biomed 209:106330","journal-title":"Comput Methods Programs Biomed"},{"issue":"6","key":"3589_CR7","doi-asserted-by":"publisher","first-page":"1153","DOI":"10.1109\/TASSP.1981.1163711","volume":"29","author":"R Keys","year":"1981","unstructured":"Keys R (1981) Cubic convolution interpolation for digital image processing. IEEE Trans Acoust Speech Signal Process 29(6):1153\u20131160","journal-title":"IEEE Trans Acoust Speech Signal Process"},{"issue":"6","key":"3589_CR8","doi-asserted-by":"publisher","first-page":"996","DOI":"10.1109\/83.503915","volume":"5","author":"RR Schultz","year":"1996","unstructured":"Schultz RR, Stevenson RL (1996) Extraction of high-resolution frames from video sequences. IEEE Trans Image Process 5(6):996\u20131011","journal-title":"IEEE Trans Image Process"},{"issue":"4","key":"3589_CR9","doi-asserted-by":"publisher","first-page":"594","DOI":"10.1016\/j.media.2010.04.005","volume":"14","author":"F Rousseau","year":"2010","unstructured":"Rousseau F, Alzheimer\u2019s Disease Neuroimaging Initiative (2010) A non-local approach for image super-resolution using intermodality priors. Med Image Anal 14(4):594\u2013605","journal-title":"Med Image Anal"},{"key":"3589_CR10","doi-asserted-by":"crossref","unstructured":"Huang JB, Singh A, Ahuja N (2015) Single image super-resolution from transformed self-exemplars. Proc IEEE Conf Comput Vis Pattern Recognit pp. 5197\u20135206","DOI":"10.1109\/CVPR.2015.7299156"},{"key":"3589_CR11","doi-asserted-by":"crossref","unstructured":"Timofte R, De Smet V, Van Gool L (2013) Anchored neighborhood regression for fast example-based super-resolution. Proc IEEE Int Conf Comput Vis pp.1920\u20131927","DOI":"10.1109\/ICCV.2013.241"},{"key":"3589_CR12","doi-asserted-by":"crossref","unstructured":"Kim J, Lee JK, Lee KM (2016) Deeply-recursive convolutional network for image super-resolution. Proc IEEE Conf Comput Vis Pattern Recognit pp.1637\u20131645","DOI":"10.1109\/CVPR.2016.181"},{"key":"3589_CR13","doi-asserted-by":"crossref","unstructured":"Bevilacqua M, Roumy A, Guillemot C, Morel MLA (2012) Neighbor embedding based single-image super-resolution using semi-nonnegative matrix factorization. Proc IEEE Int Conf Acoust Speech Signal Process pp.1289\u20131292","DOI":"10.1109\/ICASSP.2012.6288125"},{"issue":"1","key":"3589_CR14","doi-asserted-by":"publisher","first-page":"79","DOI":"10.1109\/LSP.2013.2286417","volume":"21","author":"X Chen","year":"2013","unstructured":"Chen X, Qi C (2013) Low-rank neighbor embedding for single image super-resolution. IEEE Signal Process Lett 21(1):79\u201382","journal-title":"IEEE Signal Process Lett"},{"issue":"11","key":"3589_CR15","doi-asserted-by":"publisher","first-page":"2861","DOI":"10.1109\/TIP.2010.2050625","volume":"19","author":"J Yang","year":"2010","unstructured":"Yang J, Wright J, Huang TS, Ma Y (2010) Image super-resolution via sparse representation. IEEE Trans Image Process 19(11):2861\u20132873","journal-title":"IEEE Trans Image Process"},{"key":"3589_CR16","doi-asserted-by":"publisher","first-page":"184","DOI":"10.1007\/978-3-319-10593-2_13","volume":"8692","author":"C Dong","year":"2014","unstructured":"Dong C, Loy CC, He K, Tang X (2014) Learning a deep convolutional network for image super-resolution. Lect Notes Comput Sci 8692:184\u2013199","journal-title":"Lect Notes Comput Sci"},{"key":"3589_CR17","doi-asserted-by":"publisher","first-page":"391","DOI":"10.1007\/978-3-319-46475-6_25","volume":"9906","author":"C Dong","year":"2016","unstructured":"Dong C, Loy CC, Tang X (2016) Accelerating the super-resolution convolutional neural network. Lect Notes Comput Sci 9906:391\u2013407","journal-title":"Lect Notes Comput Sci"},{"key":"3589_CR18","doi-asserted-by":"crossref","unstructured":"He K, Zhang X, Ren S, Sun J (2016) Deep residual learning for image recognition. Proc IEEE Conf Comput Vis Pattern Recognit pp. 770\u2013778","DOI":"10.1109\/CVPR.2016.90"},{"key":"3589_CR19","doi-asserted-by":"crossref","unstructured":"Tai Y, Yang J, Liu X (2017) Image super-resolution via deep recursive residual network. Proc IEEE Conf Comput Vis Pattern Recognit pp. 3147\u20133155","DOI":"10.1109\/CVPR.2017.298"},{"key":"3589_CR20","doi-asserted-by":"publisher","first-page":"286","DOI":"10.1007\/978-3-319-96890-2_24","volume":"11211","author":"Y Zhang","year":"2018","unstructured":"Zhang Y, Li K, Li K, Wang L, Zhong B, Fu Y (2018) Image super-resolution using very deep residual channel attention networks. Lect Notes Comput Sci 11211:286\u2013301","journal-title":"Lect Notes Comput Sci"},{"key":"3589_CR21","doi-asserted-by":"crossref","unstructured":"Shi W, Caballero J, Huszar F, Totz J, Aitken AP, Bishop R, Rueckert D, Wang Z (2016) Real-time single image and video super-resolution using an efficient sub-pixel convolutional neural network. Proc IEEE Conf Comput Vis Pattern Recognit pp. 1874\u20131883","DOI":"10.1109\/CVPR.2016.207"},{"key":"3589_CR22","doi-asserted-by":"publisher","first-page":"56","DOI":"10.1007\/978-3-030-67070-2_3","volume":"12536","author":"H Zhao","year":"2020","unstructured":"Zhao H, Kong X, He J, Qiao Y, Dong C (2020) Efficient image super-resolution using pixel attention. Lect Notes Comput Sci 12536:56\u201372","journal-title":"Lect Notes Comput Sci"},{"key":"3589_CR23","doi-asserted-by":"publisher","first-page":"424","DOI":"10.1016\/j.ins.2022.11.140","volume":"622","author":"C Wang","year":"2023","unstructured":"Wang C, Lv X, Shao M, Qian Y, Zhang Y (2023) A novel fuzzy hierarchical fusion attention convolution neural network for medical image super-resolution reconstruction. Inf Sci 622:424\u2013436","journal-title":"Inf Sci"},{"key":"3589_CR24","doi-asserted-by":"crossref","unstructured":"Liang J, Cao J, Sun G, Zhang K, Van Gool L, Timofte R (2021) Swinir: Image restoration using swin transformer. Proc IEEE\/CVF Int Conf Comput Vis pp. 1833\u20131844","DOI":"10.1109\/ICCVW54120.2021.00210"},{"key":"3589_CR25","doi-asserted-by":"crossref","unstructured":"Chen X, Wang X, Zhou J, Qiao Y, Dong C (2023) Activating more pixels in image super-resolution transformer. Proc IEEE\/CVF Conf Comput Vis Pattern Recognit pp. 22367\u201322377","DOI":"10.1109\/CVPR52729.2023.02142"},{"key":"3589_CR26","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1007\/978-3-030-01234-2_1","volume":"11211","author":"S Woo","year":"2018","unstructured":"Woo S, Park J, Lee JY, Kweon IS (2018) Cbam: convolutional block attention module. Lect Notes Comput Sci 11211:3\u201319","journal-title":"Lect Notes Comput Sci"},{"key":"3589_CR27","doi-asserted-by":"crossref","unstructured":"Seif G, Androutsos D (2018) Large receptive field networks for high-scale image super-resolution. Proc IEEE Conf Comput Vis Pattern Recognit Worksh pp. 763\u2013772","DOI":"10.1109\/CVPRW.2018.00120"},{"key":"3589_CR28","first-page":"256","volume":"13682","author":"L Zhou","year":"2022","unstructured":"Zhou L, Cai H, Gu J, Li Z, Liu Y, Chen X, Qiao Y, Dong C (2022) Efficient image super-resolution using vast-receptive-field attention. Lect Notes Comput Sci 13682:256\u2013272","journal-title":"Lect Notes Comput Sci"},{"key":"3589_CR29","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2023.121352","volume":"236","author":"KW Lau","year":"2024","unstructured":"Lau KW, Po LM, Rehman YAU (2024) Large separable kernel attention: rethinking the large kernel attention design in CNN. Expert Syst Appl 236:121352","journal-title":"Expert Syst Appl"},{"key":"3589_CR30","doi-asserted-by":"publisher","first-page":"113932","DOI":"10.1109\/ACCESS.2020.3003778","volume":"8","author":"L Guo","year":"2020","unstructured":"Guo L, Wang S, Chen H, Shi Q (2020) A load identification method based on active deep learning and discrete wavelet transform. IEEE Access 8:113932\u2013113942","journal-title":"IEEE Access"},{"key":"3589_CR31","doi-asserted-by":"crossref","unstructured":"Lin T, Ma Z, Li F, He D, Li X, Ding E, Li J, Gao X (2021) Drafting and revision: Laplacian pyramid network for fast high-quality artistic style transfer. Proc IEEE\/CVF Conf Comput Vis Pattern Recognit pp. 5141\u20135150","DOI":"10.1109\/CVPR46437.2021.00510"},{"key":"3589_CR32","doi-asserted-by":"publisher","DOI":"10.1016\/j.bspc.2021.103412","volume":"73","author":"Y Chen","year":"2022","unstructured":"Chen Y, Zheng Q, Chen J (2022) Double paths network with residual information distillation for improving lung CT image super resolution. Biomed Signal Process Control 73:103412","journal-title":"Biomed Signal Process Control"},{"key":"3589_CR33","doi-asserted-by":"crossref","unstructured":"Timofte R, Agustsson E, Van Gool L, Yang MH, Zhang L (2017) Ntire 2017 challenge on single image super-resolution: Methods and results. Proc IEEE Conf Comput Vis Pattern Recognit pp. 1110\u20131121","DOI":"10.1109\/CVPRW.2017.150"},{"issue":"10","key":"3589_CR34","doi-asserted-by":"publisher","first-page":"1993","DOI":"10.1109\/TMI.2014.2377694","volume":"34","author":"BH Menze","year":"2015","unstructured":"Menze BH, Jakab A, Bauer S et al (2015) The multimodal brain tumor image segmentation benchmark (BRATS). IEEE Trans Med Imaging 34(10):1993\u20132024","journal-title":"IEEE Trans Med Imaging"},{"issue":"11","key":"3589_CR35","doi-asserted-by":"publisher","first-page":"2514","DOI":"10.1109\/TMI.2018.2837502","volume":"37","author":"O Bernard","year":"2018","unstructured":"Bernard O, Lalande A, Zotti C et al (2018) Deep learning techniques for automatic MRI cardiac multi-structures segmentation and diagnosis: is the problem solved? IEEE Trans Med Imaging 37(11):2514\u20132525","journal-title":"IEEE Trans Med Imaging"},{"issue":"1","key":"3589_CR36","doi-asserted-by":"publisher","first-page":"262","DOI":"10.1109\/TMI.2020.3025087","volume":"40","author":"S Pang","year":"2021","unstructured":"Pang S, Pang C, Zhao L, Chen Y, Su Z, Zhou Y, Huang M, Yang W, Lu H, Feng Q (2021) SpineParseNet: spine parsing for volumetric MR image by a two-stage segmentation framework with semantic image representation. IEEE Trans Med Imaging 40(1):262\u2013273","journal-title":"IEEE Trans Med Imaging"},{"issue":"4","key":"3589_CR37","first-page":"604","volume":"13","author":"Z Wang","year":"2004","unstructured":"Wang Z (2004) Image quality assessment: from error visibility to structural similarity. IEEE Trans Image Process 13(4):604\u2013606","journal-title":"IEEE Trans Image Process"},{"key":"3589_CR38","doi-asserted-by":"crossref","unstructured":"Zhang K, Zuo W, Zhang L (2018) Learning a single convolutional super-resolution network for multiple degradations. Proc IEEE Conf Comput Vis Pattern Recognit pp. 3262\u20133271","DOI":"10.1109\/CVPR.2018.00344"},{"key":"3589_CR39","doi-asserted-by":"crossref","unstructured":"Zhang R, Isola P, Efros AA, Shechtman E, Wang O (2018) The unreasonable effectiveness of deep features as a perceptual metric. Proc IEEE Conf Comput Vis Pattern Recognit pp. 586\u2013595","DOI":"10.1109\/CVPR.2018.00068"},{"key":"3589_CR40","doi-asserted-by":"crossref","unstructured":"Lim B, Son S, Kim H, Nah S, Mu Lee K (2017) Enhanced deep residual networks for single image super-resolution. Proc IEEE Conf Comput Vis Pattern Recognit Worksh pp. 136\u2013144","DOI":"10.1109\/CVPRW.2017.151"},{"key":"3589_CR41","doi-asserted-by":"crossref","unstructured":"Hui Z, Gao X, Yang Y, Wang X (2019) Lightweight image super-resolution with information multi-distillation network. Proc 27th ACM Int Conf Multimedia pp. 2024\u20132032","DOI":"10.1145\/3343031.3351084"},{"issue":"4","key":"3589_CR42","first-page":"4713","volume":"45","author":"C Saharia","year":"2022","unstructured":"Saharia C, Ho J, Chan W, Salimans T, Fleet DJ, Norouzi M (2022) Image super-resolution via iterative refinement. IEEE Trans Pattern Anal Mach Intell 45(4):4713\u20134726","journal-title":"IEEE Trans Pattern Anal Mach Intell"}],"container-title":["Medical & Biological Engineering & Computing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11517-026-03589-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11517-026-03589-x","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11517-026-03589-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,6,15]],"date-time":"2026-06-15T03:17:08Z","timestamp":1781493428000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11517-026-03589-x"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,5,19]]},"references-count":42,"journal-issue":{"issue":"6","published-print":{"date-parts":[[2026,6]]}},"alternative-id":["3589"],"URL":"https:\/\/doi.org\/10.1007\/s11517-026-03589-x","relation":{},"ISSN":["0140-0118","1741-0444"],"issn-type":[{"value":"0140-0118","type":"print"},{"value":"1741-0444","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,5,19]]},"assertion":[{"value":"16 July 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"25 April 2026","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"19 May 2026","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare no competing interests.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflicts of interests"}}]}}