Publications by authors named "Yuki Ohsugi"

Ixodid ticks are significant vectors of pathogens affecting both humans and animals. Biological control with natural enemies represents a sustainable tool for managing ticks. However, there is a substantial lack of knowledge about the natural enemies of ticks.

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Purpose: The measurement of slice sensitivity profile (SSP) in non-helical CT is conventionally performed by repeated scans with moving a micro-coin phantom little by little in the longitudinal direction at a small interval, which is reliable but laborious and time-consuming. The purpose of this study was to propose a simple method for measuring the SSP in non-helical CT based on a previous method that measured the slice thickness using a tilted metal wire.

Methods: In the proposed method, a CT image was obtained by scanning a wire tilted at an angle θ=30° to the scan plane.

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Purpose: The noise power spectrum (NPS) in computed tomography (CT) images potentially varies with the X-ray tube angle in a spiral orbit of the helical scan. The purpose of this study was to propose a method for measuring the NPS for each angle of the X-ray tube.

Methods: Images of the water phantom were acquired using a helical scan.

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This study aimed to evaluate the impact of region of interest (ROI) size on noise-power spectrum (NPS) measurement in computed tomography (CT) images and to propose a novel method for measuring NPS independent of ROI size. The NPS was measured using the conventional method with an ROI of size P × P pixels in a uniform region in the CT image; the NPS is referred to as NPS. NPSs were obtained and compared to assess their dependency on ROI size.

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Purpose: Various approaches in noise power spectrum (NPS) analysis are currently used for measuring a patient's longitudinal (z-direction) NPS from three-dimensional (3D) CT volume data. The purpose of this study was to clarify the relationship between those NPSs and 3D-NPS based on the central slice theorem.

Methods: We defined the 3D-NPS(f, f, f) that was calculated by 3D Fourier transform (FT) from 3D noise data (3D-Noise(x, y, z), x-y scan plane).

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