Filtering-based preconditioner for accelerated high-dimensional cone beam CT image reconstruction

Filtering-based preconditioner for accelerated high-dimensional cone beam CT image reconstruction | 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 Filtering-based preconditioner for accelerated high-dimensional cone beam CT image reconstruction Ville-Veikko Wettenhovi, Ari Hietanen, Kati Niinimäki, Marko Vauhkonen, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5741932/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 29 Nov, 2025 Read the published version in Journal of Mathematical Imaging and Vision → Version 1 posted 9 You are reading this latest preprint version Abstract Model-based image reconstruction algorithms are known to produce high-accuracy images but are still rarely used in cone beam computed tomography. One of the reasons for this is the computational requirements of model-based iterative algorithms, as it can take hundreds of iterations to obtain converged images. In this work, we present a measurement space-based preconditioner applied to the primal-dual hybrid gradient (PDHG) algorithm. The method is compared with the regular PDHG, FISTA, and OS-SART algorithms, as well as to a PDHG algorithm where the step-size parameters are adaptively computed. All tested algorithms utilize subsets for acceleration. The presented filtering-based preconditioner can obtain convergence in 10 iterations with 20 subsets, compared to a hundred or more iterations required by the other tested methods. The presented method is also computationally fast and has only a 15% increase in computation time per iteration compared to PDHG without the preconditioner. Cone beam computed tomography image reconstruction model-based iterative primal-dual preconditioner Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 29 Nov, 2025 Read the published version in Journal of Mathematical Imaging and Vision → Version 1 posted Editorial decision: Revision requested 11 Aug, 2025 Reviews received at journal 07 Apr, 2025 Reviews received at journal 04 Apr, 2025 Reviewers agreed at journal 07 Feb, 2025 Reviewers agreed at journal 02 Feb, 2025 Reviewers invited by journal 02 Feb, 2025 Editor assigned by journal 26 Jan, 2025 Submission checks completed at journal 02 Jan, 2025 First submitted to journal 31 Dec, 2024 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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