We investigate lesion detectability and its trends for different noise structures in single-slice and multislice CBCT images with anatomical background noise. Anatomical background noise is modeled using a power law spectrum of breast anatomy. Spherical signal with a 2 mm diameter is used for modeling a lesion. CT projection data are acquired by the forward projection and reconstructed by the Feldkamp-Davis-Kress algorithm. To generate different noise structures, two types of reconstruction filters (Hanning and Ram-Lak weighted ramp filters) are used in the reconstruction, and the transverse and longitudinal planes of reconstructed volume are used for detectability evaluation. To evaluate single-slice images, the central slice, which contains the maximum signal energy, is used. To evaluate multislice images, central nine slices are used. Detectability is evaluated using human and model observer studies. For model observer, channelized Hotelling observer (CHO) with dense difference-of-Gaussian (D-DOG) channels are used. For all noise structures, detectability by a human observer is higher for multislice images than single-slice images, and the degree of detectability increase in multislice images depends on the noise structure. Variation in detectability for different noise structures is reduced in multislice images, but detectability trends are not much different between single-slice and multislice images. The CHO with D-DOG channels predicts detectability by a human observer well for both single-slice and multislice images.
|Title of host publication||Medical Imaging 2018|
|Subtitle of host publication||Image Perception, Observer Performance, and Technology Assessment|
|Editors||Frank W. Samuelson, Robert M. Nishikawa|
|Publication status||Published - 2018|
|Event||Medical Imaging 2018: Image Perception, Observer Performance, and Technology Assessment - Houston, United States|
Duration: 2018 Feb 11 → 2018 Feb 12
|Name||Progress in Biomedical Optics and Imaging - Proceedings of SPIE|
|Conference||Medical Imaging 2018: Image Perception, Observer Performance, and Technology Assessment|
|Period||18/2/11 → 18/2/12|
Bibliographical noteFunding Information:
This research was supported by Ministry of Science, ICT and Future Planning (IITP-2017-2017-0-01015) and National Research Foundation of Korea (2015R1C1A1A01052268, 2017M2A2A4A01070302, 2017M2A2A6A01019663, 2017M2A2A6A02087175).
All Science Journal Classification (ASJC) codes
- Electronic, Optical and Magnetic Materials
- Atomic and Molecular Physics, and Optics
- Radiology Nuclear Medicine and imaging