{"paper":{"title":"Statistical microlocal analysis in two-dimensional X-ray CT","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["math.FA","stat.TH"],"primary_cat":"math.ST","authors_text":"Alexander Katsevich, Anuj Abhishek, James W. Webber","submitted_at":"2025-06-05T14:57:33Z","abstract_excerpt":"In many imaging applications it is important to assess how well the edges of the original object, $f$, are resolved in an image, $f^\\text{rec}$, reconstructed from the measured data, $g$. In this paper we consider the case of image reconstruction in 2D X-ray Computed Tomography (CT). Let $f$ be a function describing the object being scanned, and $g=Rf + \\eta$ be the Radon transform data in $\\mathbb{R}^2$ corrupted by noise, $\\eta$, and sampled with step size $\\sim\\epsilon$. Conventional microlocal analysis provides conditions for edge detectability based on the scanner geometry in the case of "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2506.05113","kind":"arxiv","version":3},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2506.05113/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"}