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

Park, Junhan; Lee, Chang-Woo; Baek, Jongduk

doi:10.1117/12.2081342

Cited by 1 publication

(3 citation statements)

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“…In the case of uniform backgrounds, which of the longitudinal and transverse planes has a higher task SNR depends on the signal size [3]. In contrast, in the case of anatomical backgrounds, longitudinal planes have higher task SNR values for all signal sizes, demonstrating the benefit of using longitudinal planes to detect sphere signals in anatomical backgrounds.…”

Section: Discussionmentioning

confidence: 95%

“…A 3D Gaussian noise was filtered with 3/2 1/ f , because breast anatomy was reported to have a power spectrum of 3 1/ f [1]. Each voxel of the filtered 3D image was classified into either glandular tissue or adipose tissue based on the voxel value.…”

Section: ) Image Generationmentioning

confidence: 99%

“…In Feldkamp-Davis-Kress (FDK) reconstruction [2], the reconstruction filter is applied only to the transverse direction, which produces high pass noise structure in the transverse plane and low pass noise structure in the longitudinal plane. In previous work [3], we evaluated lesion detection performance for different image planes and signal sizes in CBCT with uniform backgrounds. As anatomical noise [4,5] is often a dominating factor affecting lesion detection, in this work we investigated the effects of anatomical backgrounds on lesion detection performance in CBCT images.…”

Section: Introductionmentioning

confidence: 99%

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

2016

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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/f β , 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.

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Section: Discussionmentioning

confidence: 95%

Section: ) Image Generationmentioning

confidence: 99%