Ultrafast Fly-scan Nano-Tomography on the Seconds Timescale using Deep Learning

Ultrafast Fly-scan Nano-Tomography on the Seconds Timescale using Deep Learning | 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 Ultrafast Fly-scan Nano-Tomography on the Seconds Timescale using Deep Learning Jiayong Zhang, Wah-Keat Lee, Mingyuan Ge This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-1531489/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 16 Nov, 2022 Read the published version in Communications Materials → Version 1 posted You are reading this latest preprint version Abstract X-ray computed tomography (CT) is an important technique for characterizing the 3D structure of materials, and, has found broad applications in medical imaging, biological research, and material science. Recently, due to the advances in x-ray source technology which provides very high beam intensities, there is great interest in applying CT to track 3D dynamics of samples, sometimes referred as 4D imaging. However, conventional CT reconstruction algorithms require that the sample does not change over the course of the CT data acquisition time, which limits the achievable temporal resolution of CT. The timescale of the sample dynamics being investigated must be much longer than the CT data acquisition time, in order to fulfill the stable sample requirement during data acquisition. At the same time, there is increasing concern about x-ray induced sample damage and undesired x-ray induced interactions. Thus, there is great interest and research to significantly reduce the CT data acquisition time and at the same time, reduce the sample dose. Here, we describe a new machine-learning (ML) based algorithm that significantly reduces CT data acquisition time as well as overall x-ray dose. With this approach, we achieved an ultrafast nano-tomography with sub-10 s data acquisition time and sub-50 nm spatial resolution with a Transmission X-ray Microscope. We applied our new algorithm to study the dynamic morphology changes in a Li-ion battery cathode material under a heating rate of 50 o C/s and found a self-healing of cracks during the sintering process. The proposed reconstruction protocol can be applied to other tomography modalities. Full Text Additional Declarations There is NO Competing Interest. Supplementary Files Supportinformation.docx Supporting information Cite Share Download PDF Status: Published Journal Publication published 16 Nov, 2022 Read the published version in Communications Materials → 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. 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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-1531489","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":101226011,"identity":"8a3186c7-a0a5-4806-8745-421c9978a095","order_by":0,"name":"Jiayong Zhang","email":"","orcid":"","institution":"Brookhaven National Laboratroy","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Jiayong","middleName":"","lastName":"Zhang","suffix":""},{"id":101226012,"identity":"dd87ddb7-f530-4160-b9b9-97d0dd9e3973","order_by":1,"name":"Wah-Keat Lee","email":"","orcid":"","institution":"Brookhaven National 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