{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,21]],"date-time":"2026-01-21T17:51:26Z","timestamp":1769017886742,"version":"3.49.0"},"reference-count":33,"publisher":"MDPI AG","issue":"20","license":[{"start":{"date-parts":[[2021,10,16]],"date-time":"2021-10-16T00:00:00Z","timestamp":1634342400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Key R&amp;D Program of China","award":["2020YFC0811004"],"award-info":[{"award-number":["2020YFC0811004"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>The CT image is an important reference for clinical diagnosis. However, due to the external influence and equipment limitation in the imaging, the CT image often has problems such as blurring, a lack of detail and unclear edges, which affect the subsequent diagnosis. In order to obtain high-quality medical CT images, we propose an information distillation and multi-scale attention network (IDMAN) for medical CT image super-resolution reconstruction. In a deep residual network, instead of only adding the convolution layer repeatedly, we introduce information distillation to make full use of the feature information. In addition, in order to better capture information and focus on more important features, we use a multi-scale attention block with multiple branches, which can automatically generate weights to adjust the network. Through these improvements, our model effectively solves the problems of insufficient feature utilization and single attention source, improves the learning ability and expression ability, and thus can reconstruct the higher quality medical CT image. We conduct a series of experiments; the results show that our method outperforms the previous algorithms and has a better performance of medical CT image reconstruction in the objective evaluation and visual effect.<\/jats:p>","DOI":"10.3390\/s21206870","type":"journal-article","created":{"date-parts":[[2021,10,17]],"date-time":"2021-10-17T23:25:15Z","timestamp":1634513115000},"page":"6870","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["Super-Resolution Network with Information Distillation and Multi-Scale Attention for Medical CT Image"],"prefix":"10.3390","volume":"21","author":[{"given":"Tianliu","family":"Zhao","sequence":"first","affiliation":[{"name":"School of Computer Information Engineering, Jiangxi Normal University, Nanchang 330022, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9481-4176","authenticated-orcid":false,"given":"Lei","family":"Hu","sequence":"additional","affiliation":[{"name":"School of Computer Information Engineering, Jiangxi Normal University, Nanchang 330022, China"}]},{"given":"Yongmei","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Information Science and Technology, North China University of Technology, Beijing 100144, China"}]},{"given":"Jianying","family":"Fang","sequence":"additional","affiliation":[{"name":"School of Computer Information Engineering, Jiangxi Normal University, Nanchang 330022, China"}]}],"member":"1968","published-online":{"date-parts":[[2021,10,16]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1153","DOI":"10.1109\/TASSP.1981.1163711","article-title":"Cubic Convolution Interpolation for Digital Image Processing","volume":"29","author":"Keys","year":"1981","journal-title":"IEEE Trans. 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