Impact of Gaussian Feathering on Diagnostic Metrics in Tile-Based Micro-CT Sinogram Infilling

Impact of Gaussian Feathering on Diagnostic Metrics in Tile-Based Micro-CT Sinogram Infilling | 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 Impact of Gaussian Feathering on Diagnostic Metrics in Tile-Based Micro-CT Sinogram Infilling Falk L. Wiegmann, Nancy L. Ford This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8941600/v1 This work is licensed under a CC BY 4.0 License Status: Under Revision Version 1 posted 10 You are reading this latest preprint version Abstract Tile-based generative AI methods for micro-CT sinogram infilling require merging overlapping tiles to reconstruct full images. Gaussian feathering, the standard blending approach, produces visually seamless results but its effect on diagnostic image quality metrics has not been characterized. This study quantifies how Gaussian feathering affects noise power spectrum (NPS), modulation transfer function (MTF), noise equivalent quanta (NEQ), structural similarity index (SSIM), and peak signal-to-noise ratio (PSNR) compared to nearest priority blending, also known as Voronoi partition blending. We mathematically derived the variance reduction mechanism in Gaussian blending and experimentally compared both methods using DeepFill v2 infilled sinograms from a micro-CT quality assurance phantom, reconstructed from 50% undersampled data. Gaussian feathering reduced variance by up to 19.6% at overlap centers, causing NPS reduction of up to 13.8% at low spatial frequencies and NEQ inflation of up to 38.5%. MTF showed mixed effects with improvements at low frequencies but reductions at high frequencies. SSIM and PSNR showed statistically significant differences: sinogram PSNR differed by 0.05 dB (p = 0.003) and reconstruction SSIM by 0.009 (p = 0.036), both with small effect sizes. Gaussian feathering distorts diagnostic metrics through a variance reduction mechanism, while standard fidelity metrics detect only subtle changes. Nearest priority blending should be preferred when diagnostic metrics are used to validate infilling methods. Physical sciences/Engineering Physical sciences/Mathematics and computing Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Revision Version 1 posted Editorial decision: Revision requested 13 Mar, 2026 Reviews received at journal 11 Mar, 2026 Reviews received at journal 06 Mar, 2026 Reviewers agreed at journal 25 Feb, 2026 Reviewers agreed at journal 24 Feb, 2026 Reviewers invited by journal 24 Feb, 2026 Editor invited by journal 24 Feb, 2026 Editor assigned by journal 23 Feb, 2026 Submission checks completed at journal 23 Feb, 2026 First submitted to journal 22 Feb, 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-8941600","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":597738176,"identity":"f49b6086-58b3-4a6b-919b-9ce25bc52c3d","order_by":0,"name":"Falk L. 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Gaussian feathering, the standard blending approach, produces visually seamless results but its effect on diagnostic image quality metrics has not been characterized. This study quantifies how Gaussian feathering affects noise power spectrum (NPS), modulation transfer function (MTF), noise equivalent quanta (NEQ), structural similarity index (SSIM), and peak signal-to-noise ratio (PSNR) compared to nearest priority blending, also known as Voronoi partition blending. We mathematically derived the variance reduction mechanism in Gaussian blending and experimentally compared both methods using DeepFill v2 infilled sinograms from a micro-CT quality assurance phantom, reconstructed from 50% undersampled data. Gaussian feathering reduced variance by up to 19.6% at overlap centers, causing NPS reduction of up to 13.8% at low spatial frequencies and NEQ inflation of up to 38.5%. MTF showed mixed effects with improvements at low frequencies but reductions at high frequencies. SSIM and PSNR showed statistically significant differences: sinogram PSNR differed by 0.05 dB (p = 0.003) and reconstruction SSIM by 0.009 (p = 0.036), both with small effect sizes. Gaussian feathering distorts diagnostic metrics through a variance reduction mechanism, while standard fidelity metrics detect only subtle changes. 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