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Proceedings Paper

Investigation on viewing direction dependent detectability in a reconstructed 3D volume for a cone beam CT system
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Paper Abstract

In medical imaging systems, several factors (e.g., reconstruction algorithm, noise structures, target size, contrast, etc) affect the detection performance and need to be considered for object detection. In a cone beam CT system, FDK reconstruction produces different noise structures in axial and coronal slices, and thus we analyzed directional dependent detectability of objects using detection SNR of Channelized Hotelling observer. To calculate the detection SNR, difference-of-Gaussian channel model with 10 channels was implemented, and 20 sphere objects with different radius (i.e., 0.25 (mm) to 5 (mm) equally spaced by 0.25 (mm)), reconstructed by FDK algorithm, were used as object templates. Covariance matrix in axial and coronal direction was estimated from 3000 reconstructed noise volumes, and then the SNR ratio between axial and coronal direction was calculated. Corresponding 2D noise power spectrum was also calculated. The results show that as the object size increases, the SNR ratio decreases, especially lower than 1 when the object size is larger than 2.5 mm radius. The reason is because the axial (coronal) noise power is higher in high (low) frequency band, and therefore the detectability of a small (large) object is higher in coronal (axial) images. Our results indicate that it is more beneficial to use coronal slices in order to improve the detectability of a small object in a cone beam CT system.

Paper Details

Date Published: 17 March 2015
PDF: 6 pages
Proc. SPIE 9416, Medical Imaging 2015: Image Perception, Observer Performance, and Technology Assessment, 94161H (17 March 2015); doi: 10.1117/12.2081342
Show Author Affiliations
Junhan Park, Yonsei Univ. (Korea, Republic of)
Changwoo Lee, Yonsei Univ. (Korea, Republic of)
Jongduk Baek, Yonsei Univ. (Korea, Republic of)


Published in SPIE Proceedings Vol. 9416:
Medical Imaging 2015: Image Perception, Observer Performance, and Technology Assessment
Claudia R. Mello-Thoms; Matthew A. Kupinski, Editor(s)

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