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. 2023 Sep 8;13:14806. doi: 10.1038/s41598-023-41854-3

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© The Author(s) 2023

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.

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Overview of the quantitative image analysis pipeline using an in-house developed MATLAB script. All stents were scanned on (a) PCD-CT and (b) EID-CT. (c) CT DICOM dataset of every stent was reconstructed, (d) 2D intensity profiles were calculated for each stent, (e) and plotted as 3D intensity plot across the entire length of the stent, (f) mean intensity profile of the 3D intensity profile, (g) automatic estimation of the ERS of every stent and kernel, (h) mean intensity-profile curve of the stent with the edge rise slope a HU of 10% and 90% of the maximum CT attenuation are shown. PCD-CT, Photon-counting detector CT; EID-CT, Energy integrating detector CT; DICOM, Digital imaging and communications in medicine; ERS, Edge rise slope; HU, Hounsfield Units