Automatic measurement of slice thickness in CT images of a Siemens phantom

Abstract

This study aims to develop a program in Python language for automatic measurement of slice thickness in computed tomography (CT) images of a Siemens phantom with different values of slice thickness, field of view (FOV), and pitch. ASiemens phantom was scanned using a Siemens 64-slice CT scanner with various slice thicknesses (i.e. 2, 4, 6, 8, and 10 mm), FOVs (i.e. 220, 260, and 300 mm), and pitch (i.e. 0.7, 0.9, and 1). Automatic measurement of slice thickness was performed by segmenting the ramp insert in the image and detecting angles of the ramp insert using the Hough transform. The resulting angles were subsequently used to rotate the image. Profiles of pixel along the ramp insert were made from the rotated images,and theslice thickness was calculated by determining the full-width at half maximum (FWHM) of the profiles. The product of the FWHM in pixels and the pixel size was corrected by the tangent of the ramp insert (i.e., 23o) to obtain the measured slice thickness. The results of the automatic measurements were compared with manual measurements carried out using a MicroDicom Viewer. The differences between the automatic and manual measurements at all slice thicknesses were less than 0.30 mm. The automatic and manual measurements had high linear correlations. For variations of the FOV and pitch, the differences between the automatic and manual measurement were less than 0.16 mm. The automatic and manual measurements were not significantly different (p-value > 0.05).

A challenge and solution for automatic thin slice thickness measurements on images of the Catphan phantom

“…However, automatic evaluation is preferable since it can be carried out more quickly and is user-independent. Previously, Amatullah et al [28] developed an algorithm for automatic slice thickness on a Siemens phantom. The evaluation was performed on images scanned with slice thicknesses ranging from 2 to 10 mm.…”