In the dual-phase grating system of X-ray phase-contrast imaging, the reduction in structural requirements for the absorption grating relaxes the constraints on its aspect ratio and period, thereby broadening the applicability of X-ray phase-contrast imaging techniques in a wider market. Traditionally, the extraction of phase-contrast information primarily relies on phase-stepping methods or Fourier transform algorithms, which often introduce artifacts and blurring into the images. To achieve higher quality image restoration, this study introduces generative adversarial networks (GANs) for high-quality image reconstruction. Our approach uses ideal images as labels and images containing object stripe information as inputs, utilizing GANs for feature learning to facilitate the transformation from object stripe images to high-quality phase-contrast images. The network also employs transfer learning to process previously unseen object stripe images and generate corresponding phase-contrast images. This technique not only significantly enhances image resolution but also substantially reduces artifacts and blurring in the image processing, paving the way for high-precision demands in medical diagnostics and industrial inspection.
The cascade X-ray phase-contrast imaging system is composed of a set of Talbot-Lau interferometers and inverse Talbot-Lau interferometers, which can avoid the difficulty of making small-period high aspect ratio absorption gratings, and is expected to realize the application of X-ray phase-contrast imaging for large-field of view. This equipment can simultaneously obtain the absorption images, phase contrast images and scattering images of the sample using Fourier transform algorithm of a single sample exposure. The selection of the frequency domain window function of the sample fringe image and its influencing factors are the key to optimize image quality. Aiming at the X-ray cascade grating phase-contrast imaging system, this paper use the X-ray chest transmission image as the simulation sample, simulates the selection scheme of Fourier window function in frequency domain and its influencing factors by numerical calculation, and obtains the selection range of window function for the optimal image. The simulation results show that the optimal window function is selected by taking the high frequency edge of the sample fringe image as one side edge of the window function and extending linearly to the low frequency side. The selection range of window function is inversely proportional to the sample fringe period. The smaller the fringe period is, the larger the selection range of window function is, and the more favorable it is to obtain the optimal phase-contrast image.
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