Transfer of Fractionated Treatment Plans from Ring-Gantry to L-Shaped Linacs Using Fluence Map Convolution | 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 Transfer of Fractionated Treatment Plans from Ring-Gantry to L-Shaped Linacs Using Fluence Map Convolution Sirikarn Songyen, Wilai Masanga, Thunpisit Mundee, Chirapha Tannanonta, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7421803/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 6 You are reading this latest preprint version Abstract Background: Transferring treatment plans from a ring-gantry Ethos system to a standard L-shaped linear accelerator (linac), such as TrueBeam, is often a time-consuming and labor-intensive process, requiring repeated optimization steps. Objective: This study proposes a novel method for converting Ethos plans to TrueBeam plans using Fluence Map Generation (FMG) based on a fluence map convolution approach, intended to minimize treatment delays in the event of Ethos downtime. Material and Method: The FMG method comprises three key steps: determining an optimal convolution kernel, generating fluence maps from Ethos plans, and applying 2D convolution to simulate corresponding TrueBeam fluence maps. Forty original Ethos plans were used to validate the FMG approach, and the number of available fractions for each converted plan was evaluated. Results: The FMG method successfully generated a moderate number of convertible fractions, ranging from one to six. The FMG-generated plans showed comparable dose distributions between the cumulative dose (Ethos + TrueBeam) and the original Ethos plans. Conclusion: This FMG-based conversion strategy offers a practical solution for mitigating treatment interruptions and reducing the clinical workload associated with plan re-optimization. Full Text Supplementary Files Supplementarymaterial1.docx Supplementarymaterial2.docx Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Minor revisions 10 Feb, 2026 Reviewers agreed at journal 09 Dec, 2025 Reviewers invited by journal 07 Dec, 2025 Editor invited by journal 25 Aug, 2025 Editor assigned by journal 21 Aug, 2025 First submitted to journal 20 Aug, 2025 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-7421803","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":556396402,"identity":"9402ffa8-dca9-49b7-bbd1-d06d05e1021f","order_by":0,"name":"Sirikarn Songyen","email":"","orcid":"","institution":"Chulabhorn Royal Academy","correspondingAuthor":false,"prefix":"","firstName":"Sirikarn","middleName":"","lastName":"Songyen","suffix":""},{"id":556396403,"identity":"e95715c7-7d12-4c55-aea0-8201d67c3d49","order_by":1,"name":"Wilai Masanga","email":"","orcid":"","institution":"Chulabhorn Royal 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