Development and validation of pericoronary adipose tissue attenuation analysis system

Development and validation of pericoronary adipose tissue attenuation analysis system | 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 Development and validation of pericoronary adipose tissue attenuation analysis system Masayuki Hattori This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9410323/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Coronary artery disease remains a leading cause of death worldwide, and early detection of coronary inflammation is essential for effective risk assessment. Pericoronary adipose tissue (PCAT) attenuation on coronary computed tomography angiography (CCTA) has emerged as a promising imaging biomarker of coronary inflammation. This study developed and evaluated an automated analysis pipeline for PCAT quantification based on coronary artery segmentation using five deep learning models: 3D U-Net, Attention U-Net, Dynamic U-Net, UNETR, and MedSAM2. Using 600 CCTA cases from an open-source database, the system automatically performed coronary artery segmentation, centerline extraction, and PCAT attenuation measurement with stepwise visual verification. Among the models, the Dynamic U-Net achieved the highest segmentation accuracy, with Dice coefficients of 0.91, 0.84, and 0.82 for the Right Coronary Artery (RCA), Left Anterior Descending (LAD), and Left Circumflex (LCX), respectively. Similar trends were observed for the PCAT regions (Dice: 0.89, 0.84, 0.82). The surface Dice was above 0.96 for arteries and 0.97 for PCAT at a 2-mm tolerance, and the Dynamic U-Net showed the most accurate boundaries, with HD95 values below 2 mm for all arteries. For PCAT attenuation, the Dynamic U-Net achieved mean errors within ± 1 HU across arteries (RCA: −0.06 HU; LAD: −0.06 HU; LCX: −0.24 HU), confirming high reproducibility. The RCA consistently showed the best accuracy, while the LCX exhibited larger errors due to its complex anatomy. The entire analysis was completed within approximately one minute per case, demonstrating the feasibility of the proposed automated pipeline for reliable, clinically applicable assessment of coronary inflammation. coronary computed tomography angiography deep learning segmentation pericoronary adipose tissue attenuation Full Text Additional Declarations The authors declare no competing interests. Cite Share Download PDF Status: Posted Version 1 posted 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. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-9410323","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":622684845,"identity":"c64463d1-4911-4a6a-80f7-6fdefef92740","order_by":0,"name":"Masayuki Hattori","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABFElEQVRIiWNgGAWjYPCCBB4GCQaGAx8M/smBuAceEKmF8eGMggPGYC0JRGhhAGphNub5cCCxAcrHCXTbjz/8zNuWJsMg3cAmwWNwJ31+2OGHQFvs5HQbsGsxO5NjLM3blsPDIHOATULC4FnuxttpBkAtycZmB3BoOZDDANRSAfRLApuEgQFz7sbZCSAtBxK34dJy/vnj33AtCQbM6Yaz0z/g13IjwQziMIkEZoMDBocT5KVzCNhy442Z5ZxzaTxsEomNDxsM0gw3SOcUHEgwwOOX8+mPb7wpS7bnl0g+cPjPHxt5+dnpmz98qLCTw6UFBJh4gAQbA2MDmGcAVmmAWzkIMP5A5sk34Fc9CkbBKBgFIw8AAK2BYQgVT+qXAAAAAElFTkSuQmCC","orcid":"","institution":"Yamagata University Hospital","correspondingAuthor":true,"prefix":"","firstName":"Masayuki","middleName":"","lastName":"Hattori","suffix":""}],"badges":[],"createdAt":"2026-04-14 04:17:01","currentVersionCode":1,"declarations":{"humanSubjects":true,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":true,"humanSubjectConsent":true,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-9410323/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9410323/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":106960791,"identity":"cee024c6-35b9-4ccd-b0c1-1f46d3b79b0c","added_by":"auto","created_at":"2026-04-15 09:23:08","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":617241,"visible":true,"origin":"","legend":"","description":"","filename":"ManuscriptforRPT.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9410323/v1_covered_bec00163-d82d-45f1-9086-a3641352e8a7.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003eDevelopment and validation of pericoronary adipose tissue attenuation analysis system\u003c/p\u003e","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":true,"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":"coronary computed tomography angiography, deep learning, segmentation, pericoronary adipose tissue attenuation","lastPublishedDoi":"10.21203/rs.3.rs-9410323/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9410323/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eCoronary artery disease remains a leading cause of death worldwide, and early detection of coronary inflammation is essential for effective risk assessment. Pericoronary adipose tissue (PCAT) attenuation on coronary computed tomography angiography (CCTA) has emerged as a promising imaging biomarker of coronary inflammation. This study developed and evaluated an automated analysis pipeline for PCAT quantification based on coronary artery segmentation using five deep learning models: 3D U-Net, Attention U-Net, Dynamic U-Net, UNETR, and MedSAM2. Using 600 CCTA cases from an open-source database, the system automatically performed coronary artery segmentation, centerline extraction, and PCAT attenuation measurement with stepwise visual verification. Among the models, the Dynamic U-Net achieved the highest segmentation accuracy, with Dice coefficients of 0.91, 0.84, and 0.82 for the Right Coronary Artery (RCA), Left Anterior Descending (LAD), and Left Circumflex (LCX), respectively. Similar trends were observed for the PCAT regions (Dice: 0.89, 0.84, 0.82). The surface Dice was above 0.96 for arteries and 0.97 for PCAT at a 2-mm tolerance, and the Dynamic U-Net showed the most accurate boundaries, with HD95 values below 2 mm for all arteries. For PCAT attenuation, the Dynamic U-Net achieved mean errors within \u0026plusmn;\u0026thinsp;1 HU across arteries (RCA: \u0026minus;0.06 HU; LAD: \u0026minus;0.06 HU; LCX: \u0026minus;0.24 HU), confirming high reproducibility. The RCA consistently showed the best accuracy, while the LCX exhibited larger errors due to its complex anatomy. The entire analysis was completed within approximately one minute per case, demonstrating the feasibility of the proposed automated pipeline for reliable, clinically applicable assessment of coronary inflammation.\u003c/p\u003e","manuscriptTitle":"Development and validation of pericoronary adipose tissue attenuation analysis system","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-04-15 02:36:57","doi":"10.21203/rs.3.rs-9410323/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":"b5ee5fd0-f7c7-41ab-ae0d-a74ffcc679ac","owner":[],"postedDate":"April 15th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-04-15T02:36:57+00:00","versionOfRecord":[],"versionCreatedAt":"2026-04-15 02:36:57","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9410323","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9410323","identity":"rs-9410323","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}