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Proceedings Paper

Effect of anatomical backgrounds on detectability in volumetric cone beam CT images
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Paper Abstract

As anatomical noise is often a dominating factor affecting signal detection in medical imaging, we investigate the effects of anatomical backgrounds on signal detection in volumetric cone beam CT images. Signal detection performances are compared between transverse and longitudinal planes with either uniform or anatomical backgrounds. Sphere objects with diameters of 1mm, 5mm, 8mm, and 11mm are used as the signals. Three-dimensional (3D) anatomical backgrounds are generated using an anatomical noise power spectrum, 1/fβ, with β=3, equivalent to mammographic background [1]. The mean voxel value of the 3D anatomical backgrounds is used as an attenuation coefficient of the uniform background. Noisy projection data are acquired by the forward projection of the uniform and anatomical 3D backgrounds with/without sphere lesions and by the addition of quantum noise. Then, images are reconstructed by an FDK algorithm [2]. For each signal size, signal detection performances in transverse and longitudinal planes are measured by calculating the task SNR of a channelized Hotelling observer with Laguerre-Gauss channels. In the uniform background case, transverse planes yield higher task SNR values for all sphere diameters but 1mm. In the anatomical background case, longitudinal planes yield higher task SNR values for all signal diameters. The results indicate that it is beneficial to use longitudinal planes to detect spherical signals in anatomical backgrounds.

Paper Details

Date Published: 24 March 2016
PDF: 7 pages
Proc. SPIE 9787, Medical Imaging 2016: Image Perception, Observer Performance, and Technology Assessment, 978717 (24 March 2016); doi: 10.1117/12.2214689
Show Author Affiliations
Minah Han, Yonsei Univ. (Korea, Republic of)
Subok Park, U.S. Food and Drug Administration (United States)
Jongduk Baek, Yonsei Univ. (Korea, Republic of)


Published in SPIE Proceedings Vol. 9787:
Medical Imaging 2016: Image Perception, Observer Performance, and Technology Assessment
Craig K. Abbey; Matthew A. Kupinski, Editor(s)

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