{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,2]],"date-time":"2026-06-02T09:16:18Z","timestamp":1780391778424,"version":"3.54.1"},"reference-count":43,"publisher":"Springer Science and Business Media LLC","issue":"8","license":[{"start":{"date-parts":[[2023,7,4]],"date-time":"2023-07-04T00:00:00Z","timestamp":1688428800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,7,4]],"date-time":"2023-07-04T00:00:00Z","timestamp":1688428800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"name":"Clinical Special Program of Shanghai Municipal Health Commission","award":["2022404"],"award-info":[{"award-number":["2022404"]}]},{"DOI":"10.13039\/501100020756","name":"Shanghai Professional Technology Service Platform on Cold Chain Equipment Performance and Energy Saving Evaluation","doi-asserted-by":"publisher","award":["22PJ1406800"],"award-info":[{"award-number":["22PJ1406800"]}],"id":[{"id":"10.13039\/501100020756","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Vis Comput"],"published-print":{"date-parts":[[2023,8]]},"DOI":"10.1007\/s00371-023-02938-3","type":"journal-article","created":{"date-parts":[[2023,7,4]],"date-time":"2023-07-04T11:38:19Z","timestamp":1688470699000},"page":"3647-3659","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":90,"title":["TransMRSR: transformer-based self-distilled generative prior for brain MRI super-resolution"],"prefix":"10.1007","volume":"39","author":[{"given":"Shan","family":"Huang","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaohong","family":"Liu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tao","family":"Tan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Menghan","family":"Hu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaoer","family":"Wei","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tingli","family":"Chen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Bin","family":"Sheng","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2023,7,4]]},"reference":[{"key":"2938_CR1","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2021.102037","volume":"71","author":"Y Xia","year":"2021","unstructured":"Xia, Y., Ravikumar, N., Greenwood, J.P., Neubauer, S., Petersen, S.E., Frangi, A.F.: Super-resolution of cardiac MR cine imaging using conditional GANs and unsupervised transfer learning. Med. Image Anal. 71, 102037 (2021)","journal-title":"Med. Image Anal."},{"issue":"3","key":"2938_CR2","doi-asserted-by":"publisher","first-page":"805","DOI":"10.1109\/TMI.2020.3037187","volume":"40","author":"C Zhao","year":"2020","unstructured":"Zhao, C., Dewey, B.E., Pham, D.L., Calabresi, P.A., Reich, D.S., Prince, J.L.: SMORE: a self-supervised anti-aliasing and super-resolution algorithm for MRI using deep learning. IEEE Trans. Med. Imaging 40(3), 805\u2013817 (2020)","journal-title":"IEEE Trans. Med. Imaging"},{"key":"2938_CR3","doi-asserted-by":"publisher","DOI":"10.1016\/j.neuroimage.2021.118687","volume":"245","author":"G Liu","year":"2021","unstructured":"Liu, G., Cao, Z., Xu, Q., Zhang, Q., Yang, F., Xie, X., Hao, J., Shi, Y., Bernhardt, B.C., He, Y., et al.: Recycling diagnostic MRI for empowering brain morphometric research-critical & practical assessment on learning-based image super-resolution. Neuroimage 245, 118687 (2021)","journal-title":"Neuroimage"},{"key":"2938_CR4","doi-asserted-by":"crossref","unstructured":"Peng, C., Lin, W.-A., Liao, H., Chellappa, R., Zhou, S.K.: Saint: spatially aware interpolation network for medical slice synthesis. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 7750\u20137759 (2020)","DOI":"10.1109\/CVPR42600.2020.00777"},{"key":"2938_CR5","doi-asserted-by":"crossref","unstructured":"Zhang, Y., Li, K., Li, K., Fu, Y.: MR image super-resolution with squeeze and excitation reasoning attention network. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 13425\u201313434 (2021)","DOI":"10.1109\/CVPR46437.2021.01322"},{"key":"2938_CR6","doi-asserted-by":"crossref","unstructured":"Chen, Y., Xie, Y., Zhou, Z., Shi, F., Christodoulou, A.G., Li, D.: Brain MRI super resolution using 3D deep densely connected neural networks. In: IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018), vol. 2018, pp. 739\u2013742. IEEE (2018)","DOI":"10.1109\/ISBI.2018.8363679"},{"key":"2938_CR7","doi-asserted-by":"publisher","first-page":"18938","DOI":"10.1109\/ACCESS.2020.2968395","volume":"8","author":"J Du","year":"2020","unstructured":"Du, J., Wang, L., Liu, Y., Zhou, Z., He, Z., Jia, Y.: Brain MRI super-resolution using 3D dilated convolutional encoder\u2013decoder network. IEEE Access 8, 18938\u201318950 (2020)","journal-title":"IEEE Access"},{"key":"2938_CR8","doi-asserted-by":"crossref","unstructured":"Wang, J., Chen, Y., Wu, Y., Shi, J., Gee, J.: Enhanced generative adversarial network for 3D brain MRI super-resolution. In: Proceedings of the IEEE\/CVF Winter Conference on Applications of Computer Vision, pp. 3627\u20133636 (2020)","DOI":"10.1109\/WACV45572.2020.9093603"},{"key":"2938_CR9","doi-asserted-by":"crossref","unstructured":"Chen, Y., Shi, F., Christodoulou, A.G., Xie, Y., Zhou, Z., Li, D.: Efficient and accurate MRI super-resolution using a generative adversarial network and 3D multi-level densely connected network. In: 21st International Conference on Medical Image Computing and Computer Assisted Intervention-MICCAI, Granada, Spain, September 16\u201320, 2018, Proceedings. Part I, vol. 2018, pp. 91\u201399. Springer (2018)","DOI":"10.1007\/978-3-030-00928-1_11"},{"key":"2938_CR10","doi-asserted-by":"crossref","unstructured":"Li, G., Lv, J., Tian, Y., Dou, Q., Wang, C., Xu, C., Qin, J.: Transformer-empowered multi-scale contextual matching and aggregation for multi-contrast MRI super-resolution. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 20636\u201320645 (2022)","DOI":"10.1109\/CVPR52688.2022.01998"},{"key":"2938_CR11","doi-asserted-by":"crossref","unstructured":"Zhang, B., Gu, S., Zhang, B., Bao, J., Chen, D., Wen, F., Wang, Y., Guo, B.: Styleswin: transformer-based GAN for high-resolution image generation. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 11304\u201311314 (2022)","DOI":"10.1109\/CVPR52688.2022.01102"},{"issue":"2","key":"2938_CR12","doi-asserted-by":"publisher","first-page":"295","DOI":"10.1109\/TPAMI.2015.2439281","volume":"38","author":"C Dong","year":"2015","unstructured":"Dong, C., Loy, C.C., He, K., Tang, X.: Image super-resolution using deep convolutional networks. IEEE Trans. Pattern Anal. Mach. Intell. 38(2), 295\u2013307 (2015)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"issue":"10","key":"2938_CR13","doi-asserted-by":"publisher","first-page":"4971","DOI":"10.1109\/TIP.2018.2848113","volume":"27","author":"X Liu","year":"2018","unstructured":"Liu, X., Chen, L., Wang, W., Zhao, J.: Robust multi-frame super-resolution based on spatially weighted half-quadratic estimation and adaptive BTV regularization. IEEE Trans. Image Process. 27(10), 4971\u20134986 (2018)","journal-title":"IEEE Trans. Image Process."},{"key":"2938_CR14","doi-asserted-by":"crossref","unstructured":"Liu, X., Kong, L., Zhou, Y., Zhao, J., Chen, J.: End-to-end trainable video super-resolution based on a new mechanism for implicit motion estimation and compensation. In: Proceedings of the IEEE\/CVF Winter Conference on Applications of Computer Vision, pp. 2416\u20132425 (2020)","DOI":"10.1109\/WACV45572.2020.9093552"},{"issue":"1","key":"2938_CR15","doi-asserted-by":"publisher","first-page":"16","DOI":"10.1186\/s13640-021-00552-8","volume":"2021","author":"W Wang","year":"2021","unstructured":"Wang, W., Hu, J., Liu, X., Zhao, J., Chen, J.: Single image super resolution based on multi-scale structure and non-local smoothing. EURASIP J. Image Video Process. 2021(1), 16 (2021)","journal-title":"EURASIP J. Image Video Process."},{"issue":"2","key":"2938_CR16","doi-asserted-by":"publisher","first-page":"345","DOI":"10.1109\/TBC.2021.3131875","volume":"68","author":"Z Shi","year":"2021","unstructured":"Shi, Z., Liu, X., Li, C., Dai, L., Chen, J., Davidson, T.N., Zhao, J.: Learning for unconstrained space-time video super-resolution. IEEE Trans. Broadcast. 68(2), 345\u2013358 (2021)","journal-title":"IEEE Trans. Broadcast."},{"key":"2938_CR17","doi-asserted-by":"publisher","first-page":"2127","DOI":"10.1109\/TIP.2021.3049974","volume":"30","author":"X Liu","year":"2021","unstructured":"Liu, X., Shi, K., Wang, Z., Chen, J.: Exploit camera raw data for video super-resolution via hidden Markov model inference. IEEE Trans. Image Process. 30, 2127\u20132140 (2021)","journal-title":"IEEE Trans. Image Process."},{"key":"2938_CR18","doi-asserted-by":"crossref","unstructured":"Chu, X., Chen, L., Yu, W.: NAFSSR: stereo image super-resolution using NAFNet. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 1239\u20131248 (2022)","DOI":"10.1109\/CVPRW56347.2022.00130"},{"key":"2938_CR19","doi-asserted-by":"crossref","unstructured":"Kim, J., Lee, J.K., Lee, K.M.: Accurate image super-resolution using very deep convolutional networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1646\u20131654 (2016)","DOI":"10.1109\/CVPR.2016.182"},{"key":"2938_CR20","doi-asserted-by":"crossref","unstructured":"Shi, W., Caballero, J., Husz\u00e1r, F., Totz, J., Aitken, A.P., Bishop, R., Rueckert, D., Wang, Z.: Real-time single image and video super-resolution using an efficient sub-pixel convolutional neural network. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1874\u20131883 (2016)","DOI":"10.1109\/CVPR.2016.207"},{"key":"2938_CR21","doi-asserted-by":"crossref","unstructured":"Ledig, C., Theis, L., Husz\u00e1r, F., Caballero, J., Cunningham, A., Acosta, A., Aitken, A., Tejani, A., Totz, J., Wang, Z., et al.: Photo-realistic single image super-resolution using a generative adversarial network. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4681\u20134690 (2017)","DOI":"10.1109\/CVPR.2017.19"},{"key":"2938_CR22","doi-asserted-by":"crossref","unstructured":"Feng, C.-M., Fu, H., Yuan, S., Xu, Y.: Multi-contrast MRI super-resolution via a multi-stage integration network. In: 24th International Conference on Medical Image Computing and Computer Assisted Intervention-MICCAI, Strasbourg, France, September 27\u2013October 1, 2021, Proceedings, Part VI 24, vol. 2021, pp. 140\u2013149. Springer (2021)","DOI":"10.1007\/978-3-030-87231-1_14"},{"key":"2938_CR23","doi-asserted-by":"publisher","first-page":"615","DOI":"10.1109\/TCI.2020.2964201","volume":"6","author":"Q Lyu","year":"2020","unstructured":"Lyu, Q., Shan, H., Wang, G.: MRI super-resolution with ensemble learning and complementary priors. IEEE Trans. Comput. Imaging 6, 615\u2013624 (2020)","journal-title":"IEEE Trans. Comput. Imaging"},{"issue":"1","key":"2938_CR24","doi-asserted-by":"crossref","first-page":"167","DOI":"10.1109\/TMI.2018.2858752","volume":"38","author":"H Zhang","year":"2019","unstructured":"Zhang, H., Li, H., Zhang, D., Zhang, Y., Wang, X., Xia, Y., Shi, Y., Wang, W.: MRI super-resolution using 3D deeply residual and densely convolutional neural networks. IEEE Trans. Med. Imaging 38(1), 167\u2013179 (2019)","journal-title":"IEEE Trans. Med. Imaging"},{"key":"2938_CR25","unstructured":"Chartier, S., Khairy, A.M., Reisert, M., Meriaux, S., Montagnat, J., Liebgott, H.: Multi-scale 3D generative adversarial networks for MR image synthesis. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, pp. 198\u2013206. Springer (2018)"},{"key":"2938_CR26","unstructured":"Jin, Z., Li, Y., Chen, W., Liu, H., Zhang, Y., Zhang, Q.: Deep learning-based 3D MRI super-resolution with multiple inference paths. IEEE J. Biomed. Health Inform. (2022)"},{"key":"2938_CR27","doi-asserted-by":"crossref","unstructured":"Bau, D., Strobelt, H., Peebles, W., Wulff, J., Zhou, B., Zhu, J.-Y., Torralba, A.: Semantic photo manipulation with a generative image prior. arXiv preprint arXiv:2005.07727 (2020)","DOI":"10.1145\/3306346.3323023"},{"key":"2938_CR28","doi-asserted-by":"crossref","unstructured":"Goetschalckx, L., Andonian, A., Oliva, A., Isola, P.: Ganalyze: toward visual definitions of cognitive image properties. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 5744\u20135753 (2019)","DOI":"10.1109\/ICCV.2019.00584"},{"key":"2938_CR29","doi-asserted-by":"crossref","unstructured":"Xia, W., Zhang, Y., Yang, Y., Xue, J.-H., Zhou, B., Yang, M.-H.: GAN inversion: a survey. IEEE Trans. Pattern Anal. Mach. Intell. (2022)","DOI":"10.1109\/TPAMI.2022.3181070"},{"key":"2938_CR30","doi-asserted-by":"crossref","unstructured":"Menon, S., Damian, A., Hu, S., Ravi, N., Rudin, C.: Pulse: self-supervised photo upsampling via latent space exploration of generative models. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 2437\u20132445 (2020)","DOI":"10.1109\/CVPR42600.2020.00251"},{"key":"2938_CR31","doi-asserted-by":"crossref","unstructured":"Gu, J., Shen, Y., Zhou, B.: Image processing using multi-code GAN prior. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 3012\u20133021 (2020)","DOI":"10.1109\/CVPR42600.2020.00308"},{"key":"2938_CR32","doi-asserted-by":"crossref","unstructured":"Zhu, J., Shen, Y., Zhao, D., Zhou, B.: In-domain GAN inversion for real image editing. In: 16th European Conference on Computer Vision-ECCV, Glasgow, UK, August 23\u201328, 2020, Proceedings, Part XVII 16, vol. 2020, pp. 592\u2013608. Springer (2020)","DOI":"10.1007\/978-3-030-58520-4_35"},{"key":"2938_CR33","doi-asserted-by":"crossref","unstructured":"Chan, K.C., Wang, X., Xu, X., Gu, J., Loy, C.C.: Glean: generative latent bank for large-factor image super-resolution. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 14245\u201314254 (2021)","DOI":"10.1109\/CVPR46437.2021.01402"},{"key":"2938_CR34","doi-asserted-by":"crossref","unstructured":"Wang, X., Li, Y., Zhang, H., Shan, Y.: Towards real-world blind face restoration with generative facial prior. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 9168\u20139178 (2021)","DOI":"10.1109\/CVPR46437.2021.00905"},{"issue":"7","key":"2938_CR35","doi-asserted-by":"publisher","first-page":"1747","DOI":"10.1109\/TMI.2022.3147426","volume":"41","author":"Y Korkmaz","year":"2022","unstructured":"Korkmaz, Y., Dar, S.U., Yurt, M., \u00d6zbey, M., Cukur, T.: Unsupervised MRI reconstruction via zero-shot learned adversarial transformers. IEEE Trans. Med. Imaging 41(7), 1747\u20131763 (2022)","journal-title":"IEEE Trans. Med. Imaging"},{"key":"2938_CR36","doi-asserted-by":"crossref","unstructured":"Karras, T., Laine, S., Aila, T.: A style-based generator architecture for generative adversarial networks. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 4401\u20134410 (2019)","DOI":"10.1109\/CVPR.2019.00453"},{"key":"2938_CR37","doi-asserted-by":"crossref","unstructured":"Mokady, R., Tov, O., Yarom, M., Lang, O., Mosseri, I., Dekel, T., Cohen-Or, D., Irani, M.: Self-distilled stylegan: towards generation from internet photos. In: ACM SIGGRAPH 2022 Conference Proceedings, pp. 1\u20139 (2022)","DOI":"10.1145\/3528233.3530708"},{"key":"2938_CR38","doi-asserted-by":"crossref","unstructured":"Liang, J., Cao, J., Sun, G., Zhang, K., Van\u00a0Gool, L., Timofte, R.: SwinIR: image restoration using swin transformer. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 1833\u20131844 (2021)","DOI":"10.1109\/ICCVW54120.2021.00210"},{"key":"2938_CR39","doi-asserted-by":"crossref","unstructured":"Johnson, J., Alahi, A., Fei-Fei, L.: Perceptual losses for real-time style transfer and super-resolution. In: 14th European Conference on Computer Vision-ECCV, Amsterdam, The Netherlands, October 11\u201314, 2016, Proceedings, Part II 14, vol. 2016, pp. 694\u2013711. Springer (2016)","DOI":"10.1007\/978-3-319-46475-6_43"},{"key":"2938_CR40","doi-asserted-by":"crossref","unstructured":"Gatys, L.A., Ecker, A.S., Bethge, M.: Image style transfer using convolutional neural networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2414\u20132423 (2016)","DOI":"10.1109\/CVPR.2016.265"},{"key":"2938_CR41","doi-asserted-by":"crossref","unstructured":"Liu, Z., Lin, Y., Cao, Y., Hu, H., Wei, Y., Zhang, Z., Lin, S., Guo, B.: Swin transformer: hierarchical vision transformer using shifted windows. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 10012\u201310022 (2021)","DOI":"10.1109\/ICCV48922.2021.00986"},{"key":"2938_CR42","doi-asserted-by":"crossref","unstructured":"Lim, B., Son, S., Kim, H., Nah, S., Mu Lee, K.: Enhanced deep residual networks for single image super-resolution. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, pp. 136\u2013144 (2017)","DOI":"10.1109\/CVPRW.2017.151"},{"key":"2938_CR43","doi-asserted-by":"crossref","unstructured":"Zamir, S.W., Arora, A., Khan, S., Hayat, M., Khan, F.S., Yang, M.-H.: Restormer: efficient transformer for high-resolution image restoration. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 5728\u20135739 (2022)","DOI":"10.1109\/CVPR52688.2022.00564"}],"container-title":["The Visual Computer"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00371-023-02938-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00371-023-02938-3\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00371-023-02938-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,8,18]],"date-time":"2023-08-18T11:05:31Z","timestamp":1692356731000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00371-023-02938-3"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,7,4]]},"references-count":43,"journal-issue":{"issue":"8","published-print":{"date-parts":[[2023,8]]}},"alternative-id":["2938"],"URL":"https:\/\/doi.org\/10.1007\/s00371-023-02938-3","relation":{},"ISSN":["0178-2789","1432-2315"],"issn-type":[{"value":"0178-2789","type":"print"},{"value":"1432-2315","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,7,4]]},"assertion":[{"value":"28 May 2023","order":1,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"4 July 2023","order":2,"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 that they have no conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}