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.
The abnormal contraction of ciliary muscles due to the performance of a near visual task for several hours causes various
vision problems such as asthenopia and visual loss. However, these problems can be resolved by activating the muscles
by alternately repeating negative and positive accommodation. In this study, we have verified the effect of
accommodation training that uses the strategy of presenting a stereoscopic movie to myopic youth and measuring the
uncorrected distant visual acuity, spherical diopter (SPH), and subjective index of asthenopia obtained using a visual
analog scale (VAS). Stereoscopic movies are prepared by using the POWER 3D method (Olympus Visual
Communications Co., Ltd.), which reduces the inconsistency between the experienced and the actual senses. Thirty two
myopic students aged 20 ± 1 years (16 males and 16 females) were chosen as the subjects. One group performed the
accommodation training for 6 min, and the other group underwent a near visual task during the same period as the
control group. We concluded the following from each item of verification: (a) The accommodation training using a
stereoscopic movie had temporarily improved visual acuity. (b) This training led to a decrease in asthenopia. (c) The
training improved the near-point accommodation function.
It has been reported that even users of virtual environments and entertainment systems experience motion sickness. This
visually induced motion sickness (VIMS) is known to be caused by sensory conflict, for instance, the disagreement
between vergence and visual accommodation while viewing stereoscopic images. The simulator sickness questionnaire is
a well-known tool that is used herein for verifying the occurrence of VIMS. We used the SSQ and also quantitatively
measured head acceleration and sway of the center of gravity of the human body before and during the exposure to
stereoscopic images on a head-mounted display. During the measurement, the subjects were instructed to maintain the
Romberg posture for the first 60 s and a wide stance (with the midlines of heels 20 cm apart) for the next 60 s. We
proposed a method to obtain stochastic differential equations (SDEs) as a mathematical model of the body sway on the
basis of the stabilogram. While there are several minimal points of time-averaged potential function in the SDEs, the
exposure decreases the gradient of the potential function. We have succeeded in estimating the decrease in the gradient
of the potential function by using an index called sparse density.