Fluorescence-illuminated Diffraction Tomography using Explicit Neural Fields

Fluorescence-illuminated Diffraction Tomography using Explicit Neural Fields | 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 Fluorescence-illuminated Diffraction Tomography using Explicit Neural Fields Renzhi He, Yucheng Li, Junjie Chen, Yi Xue This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6442385/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted You are reading this latest preprint version Abstract Multimodal imaging of fluorescence and phase provides distinct and complementary insights into biological samples. Current multimodal techniques for simultaneous phase and fluorescent imaging primarily operate in transmission mode and are limited to thin samples, restricting their applications to bulky tissues and in vivo animals. While multiphoton microscopy has enabled deeptissue fluorescence imaging, integrating it with phase imaging remains challenging due to the limited availability of methods capable of reconstructing the 3D refractive index (RI) of bulky, label-free tissues in reflection mode at subcellular resolution. To bridge the technical gap, we develop fluorescence-illuminated diffraction tomography (FDT) that reconstructs the 3D RI of label-free objects from diffracted fluorescence images acquired in reflection mode under two-photon excitation. The RI reconstruction leverages the transport of intensity equation (TIE) and is solved by a self-supervised neural network based on explicit neural fields. Compared to the state-of-the-art implicit neural fields, the explicit neural fields significantly improve computational speed, reconstruction accuracy, and interpretability. Using FDT, we successfully reconstruct the 3D RI of a 300 µmthick label-free bovine myotube sample over a 530 × 530 µm2 field-of-view at subcellular resolution within 20 min. FDT is the first technique to extract 3D RI from diffracted fluorescence images in reflection mode for thick tissues, overcoming key limitations of existing multimodal systems. This work lays the foundation for broadly accessible, reflection-mode multimodal fluorescence-phase imaging in complex biological systems in the future. Physical sciences/Optics and photonics/Optical techniques/Microscopy/Multiphoton microscopy Physical sciences/Optics and photonics/Optical techniques/Imaging and sensing Physical sciences/Optics and photonics/Other photonics/Biophotonics Optical diffraction tomography neural fields 3D reconstruction multi-model imaging Full Text Additional Declarations There is NO Competing Interest. Supplementary Files mdckzsection.mp4 MDCK Sample: Using explicit Method, Slice-by-Slice Visualization mdckrotate.mp4 MDCK Sample: Using explicit Method, 3D Rotational Visualization 3dtuberotate.mp4 3D view of the tube, Rotational Visualization 3dtubezsection.mp4 3D tube, Slice-by-Slice Visualization mdckimplicitzsection1.mp4 MDCK Sample: Using implicit Method, Slice-by-Slice Visualization mdckimplicitrotate.mp4 MDCK Sample: Using implicit Method, Rotational Visualization Cite Share Download PDF Status: Under Review 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. 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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-6442385","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":459481302,"identity":"c55bae8a-eca6-49fd-9447-76c0077ec672","order_by":0,"name":"Renzhi He","email":"","orcid":"https://orcid.org/0000-0003-1837-4062","institution":"University of California, Davis","correspondingAuthor":false,"prefix":"","firstName":"Renzhi","middleName":"","lastName":"He","suffix":""},{"id":459481303,"identity":"8f5a0be4-5b99-4c12-a5b6-649a8293920d","order_by":1,"name":"Yucheng Li","email":"","orcid":"","institution":"University of California, 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