Automatic marker-based alignment method for a nano-resolution full-field transmission X-ray microscope.

Driven by the development of X-ray optics, the spatial resolution of the full-field transmission X-ray microscope (TXM) has reached tens of nanometers and plays an important role in promoting the development of biomedicine and materials science. However, due to the thermal drift and the radial/axial motion error of the rotation stage, TXM computed tomography (CT) data are often associated with random image jitter errors along the horizontal and vertical directions during CT measurement. A nano-resolution 3D structure information reconstruction is almost impossible without a prior appropriate alignment process. To solve this problem, a fully automatic gold particle marker-based alignment approach without human intervention was proposed in this study. It can automatically detect, isolate, and register gold particles for projection image alignment with high efficiency and accuracy, facilitating a high-quality tomographic reconstruction. Simulated and experimental results confirmed the reliability and robustness of this method.

Deep-learning-based ring artifact correction for tomographic reconstruction.

X-ray tomography has been widely used in various research fields thanks to its capability of observing 3D structures with high resolution non-destructively. However, due to the nonlinearity and inconsistency of detector pixels, ring artifacts usually appear in tomographic reconstruction, which may compromise image quality and cause nonuniform bias. This study proposes a new ring artifact correction method based on the residual neural network (ResNet) for X-ray tomography. The artifact correction network uses complementary information of each wavelet coefficient and a residual mechanism of the residual block to obtain high-precision artifacts through low operation costs. In addition, a regularization term is used to accurately extract stripe artifacts in sinograms, so that the network can better preserve image details while accurately separating artifacts. When applied to simulation and experimental data, the proposed method shows a good suppression of ring artifacts. To solve the problem of insufficient training data, ResNet is trained through the transfer learning strategy, which brings advantages of robustness, versatility and low computing cost.

Feature detection network-based correction method for accurate nano-tomography reconstruction.

Driven by the development of advanced x-ray optics such as Fresnel zone plates, nano-resolution full-field transmission x-ray microscopy (Nano-CT) has become a powerful technique for the non-destructive volumetric inspection of objects and has long been developed at different synchrotron radiation facilities. However, Nano-CT data are often associated with random sample jitter because of the drift or radial/axial error motion of the rotation stage during measurement. Without a proper sample jitter correction process prior to reconstruction, the use of Nano-CT in providing accurate 3D structure information for samples is almost impossible. In this paper, to realize accurate 3D reconstruction for Nano-CT, a correction method based on a feature detection neural network, which can automatically extract target features from a projective image and precisely correct sample jitter errors, is proposed, thereby resulting in high-quality nanoscale 3D reconstruction. Compared with other feature detection methods, even if the target feature is overlapped by other high-density materials or impurities, the proposed Nano-CT correction method still acquires sub-pixel accuracy in geometrical correction and is more suitable for Nano-CT reconstruction because of its universal and faster correction speed. The simulated and experimental datasets demonstrated the reliability and validity of the proposed Nano-CT correction method.