{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,2,21]],"date-time":"2025-02-21T14:26:12Z","timestamp":1740147972794,"version":"3.37.3"},"reference-count":15,"publisher":"Wiley","license":[{"start":{"date-parts":[[2017,1,1]],"date-time":"2017-01-01T00:00:00Z","timestamp":1483228800000},"content-version":"unspecified","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61602065","J201608","KYTZ201610"],"award-info":[{"award-number":["61602065","J201608","KYTZ201610"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100010144","name":"Chengdu University of Information Technology","doi-asserted-by":"publisher","award":["61602065","J201608","KYTZ201610"],"award-info":[{"award-number":["61602065","J201608","KYTZ201610"]}],"id":[{"id":"10.13039\/100010144","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Scientific Research Foundation of CUIT","award":["61602065","J201608","KYTZ201610"],"award-info":[{"award-number":["61602065","J201608","KYTZ201610"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Computational and Mathematical Methods in Medicine"],"published-print":{"date-parts":[[2017]]},"abstract":"<jats:p>The spatial resolution of magnetic resonance imaging (MRI) is often limited due to several reasons, including a short data acquisition time. Several advanced interpolation-based image upsampling algorithms have been developed to increase the resolution of MR images. These methods estimate the voxel intensity in a high-resolution (HR) image by a weighted combination of voxels in the original low-resolution (LR) MR image. As these methods fall into the zero-order point estimation framework, they only include a local constant approximation of the image voxel and hence cannot fully represent the underlying image structure(s). To this end, we extend the existing zero-order point estimation to higher orders of regression, allowing us to approximate a mapping function between local LR-HR image patches by a polynomial function. Extensive experiments on open-access MR image datasets and actual clinical MR images demonstrate that our algorithm can maintain sharp edges and preserve fine details, while the current state-of-the-art algorithms remain prone to some visual artifacts such as blurring and staircasing artifacts.<\/jats:p>","DOI":"10.1155\/2017\/6462832","type":"journal-article","created":{"date-parts":[[2017,3,30]],"date-time":"2017-03-30T18:17:26Z","timestamp":1490897846000},"page":"1-12","source":"Crossref","is-referenced-by-count":3,"title":["Second-Order Regression-Based MR Image Upsampling"],"prefix":"10.1155","volume":"2017","author":[{"given":"Jing","family":"Hu","sequence":"first","affiliation":[{"name":"Department of Computer Science, Chengdu University of Information Technology, Chengdu 610225, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7659-1631","authenticated-orcid":true,"given":"Xi","family":"Wu","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Chengdu University of Information Technology, Chengdu 610225, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4659-8549","authenticated-orcid":true,"given":"Jiliu","family":"Zhou","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Chengdu University of Information Technology, Chengdu 610225, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","reference":[{"key":"1","doi-asserted-by":"publisher","DOI":"10.1002\/cmr.a.21249"},{"key":"2","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2014.2329271"},{"key":"4","doi-asserted-by":"publisher","DOI":"10.1093\/comjnl\/bxm075"},{"key":"5","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2010.05.010"},{"key":"7","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2013.09.007"},{"key":"8","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2010.04.005"},{"key":"9","doi-asserted-by":"publisher","DOI":"10.1155\/2010\/425891"},{"key":"10","doi-asserted-by":"publisher","DOI":"10.1016\/j.neuroimage.2013.06.030"},{"key":"13","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-642-15558-1_41"},{"key":"16","doi-asserted-by":"publisher","DOI":"10.1109\/tip.2012.2189576"},{"key":"17","doi-asserted-by":"publisher","DOI":"10.1117\/1.JEI.23.3.033014"},{"key":"18","doi-asserted-by":"publisher","DOI":"10.1007\/11744047_45"},{"issue":"4, article s425","key":"20","volume":"5","year":"1997","journal-title":"NeuroImage"},{"key":"21","doi-asserted-by":"publisher","DOI":"10.1109\/tip.2003.819861"},{"key":"22","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijleo.2013.04.009"}],"container-title":["Computational and Mathematical Methods in Medicine"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/downloads.hindawi.com\/journals\/cmmm\/2017\/6462832.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/downloads.hindawi.com\/journals\/cmmm\/2017\/6462832.xml","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/downloads.hindawi.com\/journals\/cmmm\/2017\/6462832.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2017,3,30]],"date-time":"2017-03-30T18:17:34Z","timestamp":1490897854000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.hindawi.com\/journals\/cmmm\/2017\/6462832\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2017]]},"references-count":15,"alternative-id":["6462832","6462832"],"URL":"https:\/\/doi.org\/10.1155\/2017\/6462832","relation":{},"ISSN":["1748-670X","1748-6718"],"issn-type":[{"type":"print","value":"1748-670X"},{"type":"electronic","value":"1748-6718"}],"subject":[],"published":{"date-parts":[[2017]]}}}