A Physics-Informed Neural Network for In Vivo Dosimetry Using Quantitative Radiacoustic Imaging

A Physics-Informed Neural Network for In Vivo Dosimetry Using Quantitative Radiacoustic 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 Article A Physics-Informed Neural Network for In Vivo Dosimetry Using Quantitative Radiacoustic Imaging LESHAN SUN, Kristina Bjegovic, Lucia Rodriguez-Gonzalez, Yifei Xu, and 10 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8503498/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 Accurate dosimetry is critical for safe and effective radiotherapy, yet no clinical method currently measures dose directly within the patient in vivo . Radiacoustic imaging (RAI), which detects acoustic waves generated by thermoelastic expansion during radiation delivery, offers a promising solution but has been limited to qualitative output. We present a quantitative RAI (qRAI) framework powered by a physics-informed neural network (PINN) that reconstructs quantitative dose maps in vivo . The PINN incorporates the physics of acoustic wave generation and propagation, along with a digital twin of the radiation delivery and radiacoustic detection systems, enabling accurate reconstruction from limited-view data. Reconstructed pressure maps are calibrated against experimental and simulated dose references. We validate the method across diverse clinical scenarios, including water tank dosimetry, human torso phantoms, and FLASH electron therapy. Compared to purely data-driven models, our PINN approach offers superior robustness and generalizability, especially in clinical settings lacking experimental ground truth. These results establish PINN-based qRAI as a powerful tool for real-time, adaptive, and quantitative in vivo dosimetry. Biological sciences/Cancer Physical sciences/Engineering Health sciences/Medical research Health sciences/Oncology Physical sciences/Physics Radiotherapy In Vivo Dosimetry Quantitative Radiacoustic Imaging Physics-informed Neural Network Full Text Additional Declarations No competing interests reported. Supplementary Files Supplementary.pdf Cite Share Download PDF Status: Under Review Version 1 posted Reviews received at journal 18 Feb, 2026 Reviewers agreed at journal 21 Jan, 2026 Reviewers invited by journal 16 Jan, 2026 Editor assigned by journal 11 Jan, 2026 Submission checks completed at journal 09 Jan, 2026 First submitted to journal 02 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. 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-8503498","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":576843986,"identity":"5c9c835e-4201-4dc7-b11f-aa39cfe46c7f","order_by":0,"name":"LESHAN SUN","email":"","orcid":"","institution":"University of California, Irvine","correspondingAuthor":false,"prefix":"","firstName":"LESHAN","middleName":"","lastName":"SUN","suffix":""},{"id":576843987,"identity":"d1f94667-09e6-4605-a616-55aba62a80da","order_by":1,"name":"Kristina Bjegovic","email":"","orcid":"","institution":"University of California, 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