Clear and complete microstructural imaging of the root canal isthmus is an important part of pathological investigations in research and clinical practice. X-ray micro-computed tomography (μCT) is a widely used non-destructive imaging technique, which allows for distortion-free three-dimensional (3D) visualisation. While absorption μCT typically has poor contrast resolution for observing the root canal isthmus, especially for weak-absorbing tissues, propagation-based X-ray phase-contrast imaging (PBI) is a powerful imaging method, which in its combination with μCT (PB-PCμCT) enables high-resolution and high-contrast microstructural imaging of the weak-absorbing tissues in samples. To investigate the feasibility and ability of PB-PCμCT in microstructural imaging of the root canal isthmus, conventional absorption μCT and PB-PCμCT experiments were performed. The two-dimensional (2D) and 3D comparison results demonstrated that, compared to absorption μCT, PB-PCμCT has the ability to image the root canal isthmus more clearly and completely, and thus, it has great potential to serve as a valuable tool for biomedical and preclinical studies on the root canal isthmus.

High-resolution synchrotron radiation X-ray phase contrast microtomography (PC-µCT) images often suffer from severe ring artifacts, which are mainly caused by undesirable responses of detector elements. In the medical imaging field, the existence of ring artifacts can lead to degraded visual quality and can directly affect diagnosis accuracy. Thus, removing or at least effectively reducing ring artifacts is indispensable. Method: The existing ring artifacts removal algorithms mainly focus on twodimensional (matrix-based) priors, and these algorithms fail to consider correlations hidden in sequential computed tomography (CT) images. This paper proposed a novel three-dimensional (tensor-based) ring artifacts removal algorithm for synchrotron radiation X-ray PC-µCT images. In the sinogram domain, ring artifacts manifest as vertical stripe artifacts. From an image decomposition perspective, a degraded sinogram can be decomposed into a stripe artifacts component and an underlying clean sinogram component. The proposed algorithm is designed to detect and remove stripe artifacts from a degraded sinogram by fully identifying the characteristics of the two components. Specifically, for the stripe artifacts component, tensor Tucker decomposition is used to describe its low-rank character. For the underlying clean sinogram component, spatialsequential total variation regularization is adopted to enhance the piecewise smoothness. Moreover, the Frobenius norm term is further used to model Gaussian noise. An efficient augmented Lagrange multiplier method is designed to solve the proposed optimization model. Results:The proposed method is evaluated utilizing both simulations and real data containing different ring artifacts patterns. In the simulations, the human chest CT images are used for evaluating the proposed method. We compare the peak signal-to-noise ratio (PSNR), structural similarity (SSIM), and mean absolute error (MAE) results of our algorithm with the Naghia's method, the RRRTV method, the wavelet-FFT method, and the SDRSD-GIF method. The proposed method was also evaluated on real data from rat liver samples and rat tooth samples. Our proposed method outperforms the competing methods in terms of both qualitative and quantitative evaluation results. Additionally, the 3D visualization results were presented to make the ring artifacts removal effect more intuitive.

In-line X-ray phase-contrast computed tomography typically contains two independent procedures: phase retrieval and computed tomography reconstruction, in which multi-material and streak artifacts are two important problems. To address these problems simultaneously, an accelerated 3D iterative image reconstruction algorithm is proposed. It merges the above-mentioned two procedures into one step, and establishes the data fidelity term in raw projection domain while introducing 3D total variation regularization term in image domain. Specifically, a transport-of-intensity equation (TIE)-based phase retrieval method is updated alternately for different areas of the multi-material sample. Simulation and experimental results validate the effectiveness and efficiency of the proposed algorithm.

To solve the tracking drift caused by rotation, uneven illumination, and beyond the field of view in the moving process of the target with low contrast extension, a method of edge feature points matching is proposed in this paper. Specifically, feature points are extracted from image edges to improve the stability of feature points matching. In addition, combined Non-Local algorithm and improved Contrast Limited Adaptive Histogram Equalization algorithms are used to enhance the image. Experimental results show that this method has satisfactory performance, has good anti-rotation characteristics, can be stably tracked when the target reappears in the field of view and basically meets the real-time performance.