Suppression of artifacts in X-ray phase-contrast images retrieved by Fourier transform

Abstract

Over the last two decades, the grating-based phase-contrast imaging has aroused the interest of a number of researchers. It could provide an access to three complementary signals simultaneously: the conventional absorption contrast, the differential phase contrast related to refraction of incident wave, and the dark-field contrast that relates to ultra small angle scattering in a sample. The grating-based phase-contrast signals have higher contrast sensitivity for some types of soft samples than the absorption signals. Dark-field signals have better diagnostic effects in the detection of lung tumors, pneumothorax and the identification of microcalcifications in breast. There are two main phase retrieval methods in grating-based X-ray phase-contrast imaging, i.e. phase stepping method and Fourier transform method. The phase signals retrieved by phase stepping is high precise and has low noise. But the sample suffers high dose due to at least three exposures. The phase signals retrieved by Fourier transform is low-dose due to the fact that only one image with sample is needed, but it is easily affected by artifacts when the size of the filtering window is too large. However, when the size of the filtering window is too small, the high-frequency information of the phase-contrast image will be lost, and the image will become blurred. A trade-off between definitions of the image and artifacts should be made. Since the phase-contrast signal and the dark-field signal of the sample are modulated by carrier fringes, the frequency spectrum of the detected image consists of many different harmonics. The artifacts in the retrieved signals originate from the spectrum aliasing between primary peak around zero spatial frequency and first-order harmonic peaks. Therefore, the subtraction between two images with phase difference can remove the primary peak, and the artifacts in the phase-contrast signals and dark-field signals will be suppressed. In order to further suppress the artifacts, we increase the frequency of carrier fringes, which results in a larger distance between first-order harmonic peaks in frequency domain. We finally attain artifact-free phase-contrast images and dark-field images while maintaining high definition of the images. The method proposed here is not only applicable to incoherent imaging system, but also to Talbot-Lau interferometer, and it would be useful in fast and low-dose X-ray phase-contrast and dark-field imaging.

Approximation Algorithm for X-ray Imaging Optimization of High-Absorption Ratio Materials

“…Sanchez-Gonzalez et al [16] applied machine learning methods to predict the pulse characteristics of X-ray free electron lasers and diagnosed the intensity, spectral, and time profiles of X-rays, and predicted the pulse characteristics more accurately; Ivashchuk et al [17] first proposed the concept of working with pyroelectric accelerators and X-ray sources in a pulse mode, in the proof-of-principle experiment, the X-ray radiation power in the pulse mode can be increased by more than two orders of magnitude, which has certain research value for fast X-ray imaging, but the technology is demanding for experimental conditions and difficult to achieve engineering applications. Yang et al [18] proposed an X-ray image dynamic range extension method, which can expand the gray dynamic range of the imaging equipment by reconstructing a wide dynamic range image of the target image taken at two different tube voltages, but multiple measurements under different tube voltages will extend the test time and reduce the detection efficiency. Zhou [19] used the BP neural network algorithm to establish an X-ray imaging quality prediction model, and based on this model, the key parameter combination of optimal imaging quality was solved to improve the imaging quality of X-ray equipment, but they only considered film imaging, and the composition of the test sample was relatively simple.…”