Dual deconvolution in multiphoton structured illumination microscopy for deep-tissue super-resolution imaging

Dual deconvolution in multiphoton structured illumination microscopy for deep-tissue super-resolution 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 Dual deconvolution in multiphoton structured illumination microscopy for deep-tissue super-resolution imaging Wonshik Choi, Sumin Lim, Sungsam Kang, Jin Hee Hong, Youngho Jin, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6286509/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 04 Mar, 2026 Read the published version in Nature Communications → Version 1 posted You are reading this latest preprint version Abstract Imaging in thick biological tissues is often degraded by sample-induced aberrations, which reduce resolution and contrast, particularly in super-resolution techniques. While hardware-based adaptive optics (AO) using wavefront shaping can correct these aberrations, their complexity and cost hinder widespread adoption. Here, we present a computational AO framework for multiphoton structured illumination microscopy, enabling deep-tissue super-resolution imaging with minimal hardware modifications. By replacing the photodetector in a conventional laser-scanning multiphoton microscope with a camera, we capture a sequence of scanned images. Using virtual structured illumination, we develop a dual deconvolution algorithm that independently corrects excitation and emission aberrations, recovering an aberration-free object spectrum with an extended spatial frequency bandwidth. We experimentally validate this framework through two-photon super-resolution imaging, achieving a lateral resolution of 130 nm—one-fourth of the emission wavelength—at a depth of 180 µm in thick mouse brain tissue, where conventional deconvolution fails to maintain super-resolution capability. This approach provides a cost-effective and accessible alternative to hardware-based AO, expanding the potential for high-resolution deep-tissue imaging in biological research. Physical sciences/Optics and photonics/Applied optics/Adaptive optics Physical sciences/Optics and photonics/Optical techniques/Microscopy/Multiphoton microscopy Biological sciences/Biological techniques/Imaging/Fluorescence imaging Physical sciences/Optics and photonics/Optical techniques/Microscopy/Super-resolution microscopy Full Text Additional Declarations There is NO Competing Interest. Supplementary Files SIsubmit.docx Supplementary Information Cite Share Download PDF Status: Published Journal Publication published 04 Mar, 2026 Read the published version in Nature Communications → 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-6286509","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":471816148,"identity":"1b4a9414-ab7e-4738-b984-84dc65aa3323","order_by":0,"name":"Wonshik 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