The installation and debugging of optical circuit structure in the application of carbon monoxide distributed laser gas analysis and measurement, there are difficult key technical problems. Based on the three-component expansion theory, multi-multiple expander structure with expansion ratio of 4, 5, 6 and 7 is adopted in the absorption chamber to enhance the adaptability of the installation environment of the gas analysis and measurement device. According to the basic theory of aberration, the optimal design of multi-multiple beam expander structure is carried out. By using image quality evaluation method, the difference of image quality under different magnifications is analyzed. The results show that the optical quality of the optical system with the expanded beam structure is the best when the expansion ratio is 5-7.
The patient motion can damage the quality of computed tomography images, which are typically acquired in cone-beam geometry. The rigid patient motion is characterized by six geometric parameters and are more challenging to correct than in fan-beam geometry. We extend our previous rigid patient motion correction method based on the principle of locally linear embedding (LLE) from fan-beam to cone-beam geometry and accelerate the computational procedure with the graphics processing unit (GPU)-based all scale tomographic reconstruction Antwerp toolbox. The major merit of our method is that we need neither fiducial markers nor motion-tracking devices. The numerical and experimental studies show that the LLE-based patient motion correction is capable of calibrating the six parameters of the patient motion simultaneously, reducing patient motion artifacts significantly.
This paper proposes an image segmentation algorithm with fully convolutional networks (FCN) in binocular imaging system under various circumstance. Image segmentation is perfectly solved by semantic segmentation. FCN classifies the pixels, so as to achieve the level of image semantic segmentation. Different from the classical convolutional neural networks (CNN), FCN uses convolution layers instead of the fully connected layers. So it can accept image of arbitrary size. In this paper, we combine the convolutional neural network and scale invariant feature matching to solve the problem of visual positioning under different scenarios. All high-resolution images are captured with our calibrated binocular imaging system and several groups of test data are collected to verify this method. The experimental results show that the binocular images are effectively segmented without over-segmentation. With these segmented images, feature matching via SURF method is implemented to obtain regional information for further image processing. The final positioning procedure shows that the results are acceptable in the range of 1.4∼1.6 m, the distance error is less than 10mm.
X-ray tensor tomography is a promising imaging modality for probing the micro structure of a sample by reconstructing
small-angle scattering densities in different scattering directions. However, the current x-ray grating technique still faces
an obstacle when a divergent x-ray beam from a point x-ray source propagates through a large object and reaches large
planar gratings. In this situation, tensor interior tomography is essential to perform the image reconstruction over a
region of interest (ROI) in the object. Therefore, we propose interior tensor tomography with 2D gratings to extract dark
field images isotropically. Our numerical results demonstrate that the proposed methods are promising for reconstruction
of local images from truncated dark field projection data.
Bowtie filters are used to modulate an incoming x-ray beam as a function of the angle of the x-ray to balance the photon flux on a detector array. Because of their key roles in radiation dose reduction and multi-energy imaging, bowtie filters have attracted a major attention in modern X-ray computed tomography (CT). However, few researches are concerned on the effects of the structure and materials for the bowtie filter in the Cone Beam CT (CBCT). In this study, the influence of bowtie filters’ structure and materials on X-ray photons distribution are analyzed using Monte Carlo (MC) simulations by MCNP5 code. In the current model, the phantom was radiated by virtual X-ray source (its’ energy spectrum calculated by SpekCalc program) filtered using bowtie, then all photons were collected through array photoncounting detectors. In the process above, two bowtie filters’ parameters which include center thickness (B), edge thickness (controlled by A), changed respectively. Two kinds of situation are simulated: 1) A=0.036, B=1, 2, 3, 4, 5, 6mm and the material is aluminum; 2) A=0.016, 0.036, 0.056, 0.076, 0.096, B=2mm and the material is aluminum. All the X-ray photons' distribution are measured through MCNP. The results show that reduction in center thickness and edge thickness can reduce the number of background photons in CBCT. Our preliminary research shows that structure parameters of bowtie filter can influence X-ray photons, furthermore, radiation dose distribution, which provide some evidences in design of bowtie filter for reducing radiation dose in CBCT.
In this article, the Contourlet-based image fusion method of digital neutron radiation image and X-ray radiograph is
proposed. As one of the multi-scale geometric analysis, Contourlet transform is full of application potentials in the field
of image process due to its good capability of representing high dimensional singularity of image. Meanwhile, in order to
overcome the shortcoming of pixel-based fusion, this method proposed realizes the local adaptive fusion through
Neighborhood Homogeneity Measurement (NHM). Experiments show that this fusion method retains more image detail
and therefore provides more accurate information than traditional image fusion methods. It is proved to be a novel idea
for the complementary application of neutron radiation imaging and X-ray radiograph
Sensor, influencing on system's performances, is one of some key techniques in x-ray industrial computed tomography (CT) systems. Instead of common sensors such as temperature, humidity, pressure and optic fiber, the sensor used for detecting x- or (gamma) -ray in industrial CT system, generally is called nuclear radiation detector, so that the radiation energy involved in tested information can be transformed into electric signals easy to be measured. Considering the existing problems for sensor (or detector) in CT systems, a new sensor is experimented by using optoelectronic integrated technique with optic fiber face- plate for optically coupling and transmission, and it is beneficial to the performances with high integrated, high efficiency, high resolution, miniaturized and low cost in industrial CT systems.