Feasibility of deep learning-based super-resolution reconstruction for endometrial cancer T2-weighted imaging

Feasibility of deep learning-based super-resolution reconstruction for endometrial cancer T2-weighted imaging | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Feasibility of deep learning-based super-resolution reconstruction for endometrial cancer T2-weighted imaging Nan Wang, Zuxuan Cheng, Changjun Ma, Shifeng Tian, Qingling Song, and 5 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6484322/v2 This work is licensed under a CC BY 4.0 License Status: Posted Version 2 posted You are reading this latest preprint version Show more versions Abstract Background No study has assessed the application of deep learning (DL) networks in combination with more advanced super-resolution (SR) reconstruction for endometrial cancer (EC) MRI and the influence of this methods on myometrial invasion. We aimed to evaluate the performance and clinical value of DL-based SR reconstruction for image quality improvement in under-sampling accelerated EC T2WI. Methods Two sets of T2WI were acquired for 23 volunteers with low (T2 LR ) and high (T2 HR ) resolution, respectively. For 41 EC patients, T2 LR and MultiVane T2WI (T2 MV ) were performed. T2 SR images were reconstructed from T2 LR through DL network. Image quality and diagnostic accuracy were evaluated by radiologists. Results For 23 volunteers, the signal-to-noise ratio and peak signal-to-noise ratio of T2 SR were notably higher than those of T2 LR and T2 HR , and the edge rise distance of T2 SR was lower ( P < 0.005). For 41 EC patients, the image sharpness, capsule delineation, overall image quality, and diagnostic confidence of T2 SR and T2 MV were notably higher than those of T2 LR ( P < 0.005). Compared with T2 MV , the scanning time of T2 SR was reduced by 61% (50s vs. 128s). T2 SR and T2 MV images had similar diagnostic accuracy for myometrial invasion assessed by radiologists. Conclusions DL-based super-resolution reconstruction significantly improved the image quality of uterus T2WI compared to the conventional iterative reconstruction by compressed sensing, saving scanning time and ensuring the accurate evaluation of myometrial invasion of endometrial cancer. Full Text Additional Declarations The authors declare no competing interests. Cite Share Download PDF Status: Posted Version 2 posted You are reading this latest preprint version Show more versions Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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imaging","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-6484322/v2","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6484322/v2","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eNo study has assessed the application of deep learning (DL) networks in combination with more advanced super-resolution (SR) reconstruction for endometrial cancer (EC) MRI and the influence of this methods on myometrial invasion. We aimed to evaluate the performance and clinical value of DL-based SR reconstruction for image quality improvement in under-sampling accelerated EC T2WI.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eTwo sets of T2WI were acquired for 23 volunteers with low (T2\u003csub\u003eLR\u003c/sub\u003e) and high (T2\u003csub\u003eHR\u003c/sub\u003e) resolution, respectively. For 41 EC patients, T2\u003csub\u003eLR\u003c/sub\u003e and MultiVane T2WI (T2\u003csub\u003eMV\u003c/sub\u003e) were performed. T2\u003csub\u003eSR\u003c/sub\u003e images were reconstructed from T2\u003csub\u003eLR\u003c/sub\u003e through DL network. Image quality and diagnostic accuracy were evaluated by radiologists.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eFor 23 volunteers, the signal-to-noise ratio and peak signal-to-noise ratio of T2\u003csub\u003eSR\u003c/sub\u003e were notably higher than those of T2\u003csub\u003eLR\u003c/sub\u003e and T2\u003csub\u003eHR\u003c/sub\u003e, and the edge rise distance of T2\u003csub\u003eSR\u003c/sub\u003e was lower (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.005). For 41 EC patients, the image sharpness, capsule delineation, overall image quality, and diagnostic confidence of T2\u003csub\u003eSR\u003c/sub\u003e and T2\u003csub\u003eMV\u003c/sub\u003e were notably higher than those of T2\u003csub\u003eLR\u003c/sub\u003e (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.005). Compared with T2\u003csub\u003eMV\u003c/sub\u003e, the scanning time of T2\u003csub\u003eSR\u003c/sub\u003e was reduced by 61% (50s vs. 128s). T2\u003csub\u003eSR\u003c/sub\u003e and T2\u003csub\u003eMV\u003c/sub\u003e images had similar diagnostic accuracy for myometrial invasion assessed by radiologists.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eDL-based super-resolution reconstruction significantly improved the image quality of uterus T2WI compared to the conventional iterative reconstruction by compressed sensing, saving scanning time and ensuring the accurate evaluation of myometrial invasion of endometrial cancer.\u003c/p\u003e","manuscriptTitle":"Feasibility of deep learning-based super-resolution reconstruction for endometrial cancer T2-weighted imaging","msid":"","msnumber":"","nonDraftVersions":[{"code":2,"date":"2025-12-12 20:44:29","doi":"10.21203/rs.3.rs-6484322/v2","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}},{"code":1,"date":"2025-05-08 20:41:42","doi":"10.21203/rs.3.rs-6484322/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"532dc184-20b6-4dc4-877d-913c6efe34fa","owner":[],"postedDate":"December 12th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-12-15T17:16:36+00:00","versionOfRecord":[],"versionCreatedAt":"2025-12-12 20:44:29","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v2","identity":"rs-6484322","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6484322","identity":"rs-6484322","version":["v2"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}