2026-10-15T13:50:26Z https://api.figshare.com/v2/oai

oai:figshare.com:article/33720181 2026-09-14T07:44:30Z portal_1197 item_type_3 month_year_09_2026

Data for Modelling the Penumbra in Computed Tomography Audrey Kueh (24853639) J. W. Warnett (24869140) Gregory John Gibbons (24741439) Julia Brettschneider (17117878) Thomas E. Nichols (8224509) M. A. (Mark A.) Williams (24733852) W. S. Kendall (24752377) Tomography -- Data processing Migrated from ePrints Background: In computed tomography (CT), the spot geometry is one of the main sources of error in CT images. Since X-rays do not arise from a point source, artefacts are produced. In particular there is a penumbra effect, leading to poorly defined edges within a reconstructed volume. Penumbra models can be simulated given a fixed spot geometry and the known experimental setup. Objective: This paper proposes to use a penumbra model, derived from Beer’s law, both to confirm spot geometry from penumbra data, and to quantify blurring in the image. Methods: Two models for the spot geometry are considered; one consists of a single Gaussian spot, the other is a mixture model consisting of a Gaussian spot together with a larger uniform spot. Results: The model consisting of a single Gaussian spot has a poor fit at the boundary. The mixture model (which adds a larger uniform spot) exhibits a much improved fit. The parameters corresponding to the uniform spot are similar across all powers, and further experiments suggest that the uniform spot produces only soft X-rays of relatively low-energy. Conclusions: Thus, the precision of radiographs can be estimated from the penumbra effect in the image. The use of a thin copper filter reduces the size of the effective penumbra.<br><br>Penumbra data ========== These data files correspond to the data discussed in Kueh et al (2016). A 3mm ceramic rod was placed centrally on the manipulator in an approximately vertical alignment such that the magnification was x4.45. Images were taken at a constant voltage of 140kV, at each of the following 7 different exposure/power settings: 0.177s/44W, 0.25s/30.6W, 0.354s/21W, 0.5s/14.6W, 0.708s/9.6W, 1s/6.4W, 1.415s/4W. Three images were taken for each parameter set, and these are placed within their respective folder (labelled by magnification) as TIFF files. Code which imports these files into the R environment is provided in Import.R. File structure ======== Description.txt Import.R 0.177s_44W: 44W_177ms_1.tif 44W_177ms_2.tif 44W_177ms_3.tif 0.25s_30.6W: 30-6W_250ms_1.tif 30-6W_250ms_2.tif 30-6W_250ms_3.tif 0.354s_21W: 21W_354ms_1.tif 21W_354ms_2.tif 21W_354ms_3.tif 0.5s_14.6W: 14.6W_500ms_1.tif 14.6W_500ms_2.tif 14.6W_500ms_3.tif 0.708s_9.6W: 9.6W_708ms_1.tif 9.6W_708ms_2.tif 9.6W_708ms_3.tif 1.415s_4W: 4.0W_1415ms_1.tif 4.0W_1415ms_2.tif 4.0W_1415ms_3.tif 1s_6.4W: 6.4W_1000ms_1.tif 6.4W_1000ms_2.tif 6.4W_1000ms_3.tif 2026-09-14T07:44:30Z Dataset Dataset 10.82444/warw.33720181.v1 https://figshare.com/articles/dataset/Data_for_Modelling_the_Penumbra_in_Computed_Tomography/33720181 CC BY 4.0