We investigate the nonlinearity in digital X-ray images to determine the feasibility of a noise reduction process using a
mathematical model, which realizes an accurate digital X-ray imaging system. To develop this mathematical model, it is
important to confirm whether the system is linear or nonlinear. We have verified the nonlinearity of the imaging system
through an analysis of computed radiography (CR) images by using the method of surrogate, a statistical test of
nonlinearity, and the Wayland test. In the method of surrogate, we use the Fourier transform surrogate method. The
Wayland test can be used for evaluating the complexity of the orbit of a signal aggregate called the attractor
reconstructed in a high-dimensional phase space using a nonlinear statistical parameter called the translation error.
Nonlinearity is determined by statistically comparing the translation error of the original data with that of the surrogate
data. X-ray images are obtained under different conditions to investigate the effects of various tube voltages--50 and 80
kV--and dose settings--2 and 10 mAs. We extract 30 profiles from both directions, the directions vertical (V-direction)
and horizontal (H-direction) to the X-ray tube. In the H-direction, nonlinearity is found at all voltage and dose settings.
On the other hand, nonlinearity is found only at 10 mAs and 80 kV in the V-direction. Hence, it can be concluded that
nonlinearity is indicated by a decrease in the quantum mottle, and the factors of nonlinearity exhibit the comprehensive
variation produced by the digital X-ray imaging system.
In the soft copy diagnosis, each pixel of the detector is displayed to the correspondent pixel of liquid crystal display
(LCD). But when the image is displayed at the first time, the entire image may be reduced. We examined the influence
that the difference of image reduction rate on LCD exerts on detection performance by using observer performance
experiment. Moreover, to find the best interpolation method, we investigated the several interpolation methods. We
made a simulation image which is similar to Burger phantom. This image consists of 288 signals, each of a different size
and contrast. The matrix size is the same as Phase Contrast Mammography (PCM). We gradated the simulation image by
using an MTF of a geometric blur, and the image was added to the noise image which is uniformly exposed with PCM.
Then the image was reduced by using the nearest-neighbor, the bilinear, and the bicubic methods. The reduction rates
were calculated as the ratios of the number of pixels of LCDs to those of PCM. We displayed the reduced images on
LCD and examined the detection performance. Results of physical evaluation examined before showed that sharpness
and granularity have worsened both in proportion to the reduction rate. The detection performance deteriorated as the
reduction rate becomes high. In the comparison of the interpolation methods, the detection performance of the nearestneighbor
method was worse than those of other interpolation methods. The bilinear method is the most suitable for the
reduction of the image.
Multi-detector row computed tomography (MDCT) allows high-edge-response imaging in z-direction by thinner
slice compared with the conventional single-detector row CT (SDCT). The edge response in x- and y-direction is,
however, basically depending on the reconstruction function or the image processing. In this work, a method of
enhancing the edge response in x- and y-direction, without special image processing, is proposed. In this method,
projection data of a patient is acquired by high-spatial-frequency sampling, and an edge-enhanced image is
reconstructed with same matrix size as a conventional image by averaging the projection data. Edge-enhancement
effect in this method employs nonlinearlity of the projection data, and special image processing is not required.
In order to verify this proposed method, a large water phantom that consists of five resinous rods and a small
one that the similitude rate is 1/2, and is topologically the same as the former large phantom, were scanned, and
high-spatial-frequency sampling was simulated. After that, reconstructed images were obtained by averaging
the high-spatial-frequency sampled data and edge gradients for some rods were obtained and estimated. As a
result, although image noise increased slightly, edge gradients are improved 25 to 97 % without special image
The multi-detector row computed tomography (MDCT) has dramatically increased speed of scanning, and allows high-resolution imaging compared with the conventional single-detector row CT (SDCT). However, use of the MDCT was making use of three-dimensional (3D) volume scanning and four-dimensional (4D) dynamic scanning increase, and made radiation dose to patients increase simultaneously. In addition, in recent years, lung-cancer screening CT (LSCT) is introduced, and low-dose scanning is strongly required to increase the benefit/risk ratio. In this study, high-frequency volume data sampling (over-sampling) method of x-, y- and z-direction was
proposed as technique for reduction of image noise in the MDCT and discussed about reduction of radiation dose and improvement of image quality. In this proposed method, volume data are obtained by over-sampling of x-, y- and z-direction and image is obtained by averaging these data. In x- and y-direction, over-sampling is equivalent to obtaining projection data using large matrix size for same scan-field of view (scan-FOV), and in z-direction, equivalent to using thin slice. Normally, when signal with which noise distribution differs are averaged n-times, signal-to-noise ratio (SNR) will increases by factor of [square root of] n. In this method, each pixel value of the image is obtained from n<sup>2</sup><sub>x,y</sub>n<sub>z</sub> pixels by n<sub>x,y</sub>-times sampling for x- and y-direction, and n<sub>z</sub>-times sampling for z-direction. In other words, SNR of the image increases [square root of] n<sup>2</sup><sub>x,y</sub>n<sub>z</sub>-times. In this high-frequency data sampling method, it is possible to obtain high-quality image as compared with conventional image. Moreover, by applying to noisy image obtained with low-dose scanning, reduction of radiation dose to patients is possible.