研究目的
To evaluate the lesion detectability using human and model observer studies in single-slice and multislice cone beam computed tomography (CBCT) images with a breast anatomical background.
研究成果
Detectability by a human observer in CBCT images with breast anatomical background is affected by the image viewing mode, and the effect of the viewing mode depends on the signal size and noise structure. D-DOG and Gabor CHO with internal noise predict the detectability by a human observer well for both the single-slice and multislice image viewing modes.
研究不足
The study did not consider physical factors such as scatter, beam hardening, and nonideal detector responses in simulations. The shape of a real lesion is usually more complex than the spherical signals used.
1：Experimental Design and Method Selection:
The study involved signal-known-exactly and background-known-statistically detection tasks on transverse and longitudinal images reconstructed using the Feldkamp–Davis–Kress algorithm with Hanning and Ram-Lak weighted ramp filters.
2：Sample Selection and Data Sources:
Breast anatomical background was modeled using a power law spectrum of mammograms and the lesion was modeled with a spherical signal.
3：List of Experimental Equipment and Materials:
CBCT system simulation parameters were used for data acquisition.
4：Experimental Procedures and Operational Workflow:
The human observer study was conducted on three different viewing modes: single-slice, and sequential and simultaneous multislice.
5：Data Analysis Methods:
Detectability by CHO with internal noise was compared with that of the human observer for all viewing modes.
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