Deep Learning Reconstruction for 40-keV Virtual Monoenergetic CT of Colon Cancer: Evaluation of Image Quality and Edge Sharpness

Deep Learning Reconstruction for 40-keV Virtual Monoenergetic CT of Colon Cancer: Evaluation of Image Quality and Edge Sharpness | 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 Deep Learning Reconstruction for 40-keV Virtual Monoenergetic CT of Colon Cancer: Evaluation of Image Quality and Edge Sharpness Yuhan Bao, Juan Long, Zhen Wang, Xiaohan Liu, Chen Wu, Haini Zhang, and 6 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8621121/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 06 Apr, 2026 Read the published version in Abdominal Radiology → Version 1 posted 9 You are reading this latest preprint version Abstract Background: Virtual monoenergetic imaging (VMI) at 40 keV improves iodine attenuation in colon cancer CT but is constrained by severe image noise. Deep learning image reconstruction (DLIR) may address this limitation, but its effect on anatomical edge preservation across multiple targets requires investigation. Purpose: To evaluate the impact of DLIR on objective and subjective image quality of 40-keV VMIs in colon adenocarcinoma, with emphasis on the trade-off between noise reduction and edge definition. Materials and Methods: In this retrospective study (May 2024–February 2025), 60 patients (mean age, 62.8 years ± 15.1; 34 men) with confirmed colon adenocarcinoma underwent dual-energy CT using a low-iodine protocol (1.0 mL/kg). Portal venous phase data were reconstructed at 40 keV using adaptive statistical iterative reconstruction-V (ASIR-V) 50%, medium-strength DLIR (DLIR-M), and high-strength DLIR (DLIR-H). Contrast-to-noise ratio (CNR), signal-to-noise ratio (SNR), and edge rise slope (ERS) were measured for tumors, feeding arteries, and regional lymph nodes. Two radiologists scored overall quality and boundary definition (5-point Likert scale). Data were compared using the Friedman test and post-hoc Bonferroni correction. Results: DLIR-H yielded the lowest image noise and highest CNR and SNR across all anatomical targets compared with DLIR-M and ASIR-V 50% (all P < .001). For colon tumors, the CNR of DLIR-H (5.4 ± 2.2) was 82% higher than that of ASIR-V 50% (3.0 ± 1.1, P < .001). Although ASIR-V 50% maintained a higher ERS than DLIR-H (108.0 ± 15.2 vs 101.4 ± 14.1 HU/mm, P < .001), DLIR-H received the highest subjective scores for overall image quality and lesion boundary definition (median, 5.0 [IQR: 4.0–5.0] vs 3.0 [IQR: 2.0–3.0]; P < .001). Conclusion: In 40-keV virtual monoenergetic CT of colon cancer, DLIR-H significantly improves image quality for tumors, vessels, and lymph nodes. While a minor objective edge-smoothing effect exists, DLIR-H provides an optimal balance between robust noise suppression and anatomical clarity, facilitating low-iodine spectral protocols. Colon Neoplasms Computed Tomography Dual-Energy Deep Learning Image Reconstruction Virtual Monoenergetic Imaging Contrast-to-Noise Ratio Image Quality Full Text Additional Declarations No competing interests reported. Supplementary Files SupplementaryTableE1.QualitativeSubjectiveImageQualityScale2.xlsx Cite Share Download PDF Status: Published Journal Publication published 06 Apr, 2026 Read the published version in Abdominal Radiology → Version 1 posted Editorial decision: Revision requested 04 Mar, 2026 Reviews received at journal 03 Mar, 2026 Reviews received at journal 14 Feb, 2026 Reviewers agreed at journal 02 Feb, 2026 Reviewers agreed at journal 19 Jan, 2026 Reviewers invited by journal 19 Jan, 2026 Editor assigned by journal 17 Jan, 2026 Submission checks completed at journal 17 Jan, 2026 First submitted to journal 16 Jan, 2026 You are reading this latest preprint version 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. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8621121","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":577197275,"identity":"3366c714-01be-4d21-9e49-162541b6cf9f","order_by":0,"name":"Yuhan Bao","email":"","orcid":"","institution":"the Affiliated Hospital of Xuzhou Medical University","correspondingAuthor":false,"prefix":"","firstName":"Yuhan","middleName":"","lastName":"Bao","suffix":""},{"id":577197276,"identity":"d80c14cd-6b6b-4143-8abd-31bfde260200","order_by":1,"name":"Juan Long","email":"","orcid":"","institution":"the Affiliated Hospital of Xuzhou Medical 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40-keV Virtual Monoenergetic CT of Colon Cancer: Evaluation of Image Quality and Edge Sharpness","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"abdominal-radiology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"aima","sideBox":"Learn more about [Abdominal Radiology](http://link.springer.com/journal/261)","snPcode":"261","submissionUrl":"https://submission.springernature.com/new-submission/261/3","title":"Abdominal Radiology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Colon Neoplasms, Computed Tomography, Dual-Energy, Deep Learning, Image Reconstruction, Virtual Monoenergetic Imaging, Contrast-to-Noise Ratio, Image Quality","lastPublishedDoi":"10.21203/rs.3.rs-8621121/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8621121/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground:\u003c/h2\u003e \u003cp\u003eVirtual monoenergetic imaging (VMI) at 40 keV improves iodine attenuation in colon cancer CT but is constrained by severe image noise. Deep learning image reconstruction (DLIR) may address this limitation, but its effect on anatomical edge preservation across multiple targets requires investigation.\u003c/p\u003e\u003ch2\u003ePurpose:\u003c/h2\u003e \u003cp\u003eTo evaluate the impact of DLIR on objective and subjective image quality of 40-keV VMIs in colon adenocarcinoma, with emphasis on the trade-off between noise reduction and edge definition.\u003c/p\u003e\u003ch2\u003eMaterials and Methods:\u003c/h2\u003e \u003cp\u003eIn this retrospective study (May 2024\u0026ndash;February 2025), 60 patients (mean age, 62.8 years\u0026thinsp;\u0026plusmn;\u0026thinsp;15.1; 34 men) with confirmed colon adenocarcinoma underwent dual-energy CT using a low-iodine protocol (1.0 mL/kg). Portal venous phase data were reconstructed at 40 keV using adaptive statistical iterative reconstruction-V (ASIR-V) 50%, medium-strength DLIR (DLIR-M), and high-strength DLIR (DLIR-H). Contrast-to-noise ratio (CNR), signal-to-noise ratio (SNR), and edge rise slope (ERS) were measured for tumors, feeding arteries, and regional lymph nodes. Two radiologists scored overall quality and boundary definition (5-point Likert scale). Data were compared using the Friedman test and post-hoc Bonferroni correction.\u003c/p\u003e\u003ch2\u003eResults:\u003c/h2\u003e \u003cp\u003eDLIR-H yielded the lowest image noise and highest CNR and SNR across all anatomical targets compared with DLIR-M and ASIR-V 50% (all \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001). For colon tumors, the CNR of DLIR-H (5.4\u0026thinsp;\u0026plusmn;\u0026thinsp;2.2) was 82% higher than that of ASIR-V 50% (3.0\u0026thinsp;\u0026plusmn;\u0026thinsp;1.1, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001). Although ASIR-V 50% maintained a higher ERS than DLIR-H (108.0\u0026thinsp;\u0026plusmn;\u0026thinsp;15.2 vs 101.4\u0026thinsp;\u0026plusmn;\u0026thinsp;14.1 HU/mm, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001), DLIR-H received the highest subjective scores for overall image quality and lesion boundary definition (median, 5.0 [IQR: 4.0\u0026ndash;5.0] vs 3.0 [IQR: 2.0\u0026ndash;3.0]; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001).\u003c/p\u003e\u003ch2\u003eConclusion:\u003c/h2\u003e \u003cp\u003eIn 40-keV virtual monoenergetic CT of colon cancer, DLIR-H significantly improves image quality for tumors, vessels, and lymph nodes. While a minor objective edge-smoothing effect exists, DLIR-H provides an optimal balance between robust noise suppression and anatomical clarity, facilitating low-iodine spectral protocols.\u003c/p\u003e","manuscriptTitle":"Deep Learning Reconstruction for 40-keV Virtual Monoenergetic CT of Colon Cancer: Evaluation of Image Quality and Edge Sharpness","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-01-21 20:36:14","doi":"10.21203/rs.3.rs-8621121/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-03-04T13:32:11+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-03-03T07:28:55+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-02-14T07:21:00+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"246561061904378396564991954797337165836","date":"2026-02-02T07:14:45+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"107238322178444589501361475184320702501","date":"2026-01-20T04:15:56+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-01-19T12:57:41+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-01-17T07:06:19+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-01-17T07:06:11+00:00","index":"","fulltext":""},{"type":"submitted","content":"Abdominal Radiology","date":"2026-01-16T16:47:29+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"abdominal-radiology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"aima","sideBox":"Learn more about [Abdominal Radiology](http://link.springer.com/journal/261)","snPcode":"261","submissionUrl":"https://submission.springernature.com/new-submission/261/3","title":"Abdominal Radiology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"882e19ce-fc11-4a37-bd22-f7aa10b7a5c0","owner":[],"postedDate":"January 21st, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2026-04-13T16:06:36+00:00","versionOfRecord":{"articleIdentity":"rs-8621121","link":"https://doi.org/10.1007/s00261-026-05494-1","journal":{"identity":"abdominal-radiology","isVorOnly":false,"title":"Abdominal Radiology"},"publishedOn":"2026-04-06 15:59:22","publishedOnDateReadable":"April 6th, 2026"},"versionCreatedAt":"2026-01-21 20:36:14","video":"","vorDoi":"10.1007/s00261-026-05494-1","vorDoiUrl":"https://doi.org/10.1007/s00261-026-05494-1","workflowStages":[]},"version":"v1","identity":"rs-8621121","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8621121","identity":"rs-8621121","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}