A new in-line X-ray phase-contrast computed tomography reconstruction algorithm based on adaptive-weighted anisotropic TpV regularization for insufficient data

Structure-aware guided filtering for a ring artifact correction in synchrotron x-ray microtomography

Synchrotron-based x-ray microtomography (S-µCT) is a powerful non-invasive three-dimensional (3D) imaging technique used for visualizing the internal structure of objects with micron-scale spatial resolution. However, in practical applications, ring artifacts often occur in S-µCT, which significantly degrades image quality and hinders interpretation. In this study, we propose a ring artifact correction method based on guided image filtering (GIF). The method first extracts structural prior from the input S-µCT images and then uses it as the guidance image to correct the ring artifacts. Finally, GIF with a self-guidance image is employed to further enhance image quality. Extensive comparisons and analyses on simulations and real data experiments demonstrate that the proposed method is capable of effectively correcting ring artifacts, accompanied by low-dose noise suppression and sparse-view artifact reduction. These findings suggest that the proposed method has great potential to promote the wider applications of S-µCT in the future.

Adaptive-weighted high order TV algorithm for sparse-view CT reconstruction

BACKGROUND

With the development of low-dose computed tomography (CT), incomplete data reconstruction has been widely concerned. The total variation (TV) minimization algorithm can accurately reconstruct images from sparse or noisy data.

PURPOSE

However, the traditional TV algorithm ignores the direction of structures in images, leading to the loss of edge information and block artifacts when the object is not piecewise constant. Since the anisotropic information can facilitate preserving the edge and detail information in images, we aim to improve the TV algorithm in terms of reconstruction accuracy via this approach.

METHODS

In this paper, we propose an adaptive-weighted high order total variation (awHOTV) algorithm. We construct the second order TV-norm using the second order gradient, adapt the anisotropic edge property between neighboring image pixels, adjust the local image-intensity gradient to keep edge information, and design the corresponding Chambolle-Pock (CP) solving algorithm. Implementing the proposed algorithm, comprehensive studies are conducted in the ideal projection data experiment where the Structural similarity (SSIM), Root Mean Square Error (RMSE), Contrast to noise ratio (CNR), and modulation transform function (MTF) curves are utilized to evaluate the quality of reconstructed images in statism, structure, spatial resolution, and contrast, respectively. In the noisy data experiment, we further use the noise power spectrum (NPS) curve to evaluate the reconstructed images and compare it with other three algorithms.

RESULTS

We use the 2D slice in the XCAT phantom, 2D slice in TCIA Challenge data and FORBILD phantom as simulation phantoms and use real bird data for real verification. The results show that, compared with the traditional TV and FBP algorithms, the awHOTV has better performance in terms of RMSE, SSIM, and Pearson correlation coefficient (PCC) under the projected data with different sparsity. In addition, the awHOTV algorithm is robust against the noise of different intensities.

CONCLUSIONS

The proposed awHOTV method can reconstruct the images with high accuracy under sparse or noisy data. The awHOTV solves the strip artifacts caused by sparse data in the FBP method. Compared with the TV method, the awHOTV can effectively suppress block artifacts and has good detail protection ability.

Improving spatial resolution with an edge-enhancement model for low-dose propagation-based X-ray phase-contrast computed tomography

Propagation-based X-ray phase-contrast computed tomography (PB-PCCT) has been increasingly popular for distinguishing low contrast tissues. Phase retrieval is an important step to quantitatively obtain the phase information before the tomographic reconstructions, while typical phase retrieval methods in PB-PCCT, such as homogenous transport of intensity equation (TIE-Hom), are essentially low-pass filters and thus improve the signal to noise ratio at the expense of the reduced spatial resolution of the reconstructed image. To improve the reconstructed spatial resolution, measured phase contrast projections with high edge enhancement and the phase projections retrieved by TIE-Hom were weighted summed and fed into an iterative tomographic algorithm within the framework of the adaptive steepest descent projections onto convex sets (ASD-POCS), which was employed for suppressing the image noise in low dose reconstructions because of the sparse-view scanning strategy or low exposure time for single phase contrast projection. The merging strategy decreases the accuracy of the linear model of PB-PCCT and would finally lead to the reconstruction failure in iterative reconstructions. Therefore, the additive median root prior is also introduced in the algorithm to partly increase the model accuracy. The reconstructed spatial resolution and noise performance can be flexibly balanced by a pair of antagonistic hyper-parameters. Validations were performed by the established phase-contrast Feldkamp-Davis-Kress, phase-retrieved Feldkamp-Davis-Kress, conventional ASD-POCS and the proposed enhanced ASD-POCS with a numerical phantom dataset and experimental biomaterial dataset. Simulation results show that the proposed algorithm outperforms the conventional ASD-POCS in spatial evaluation assessments such as root mean square error (a ratio of 9.78%), contrast to noise ratio (CNR) (a ratio of 7.46%), and also frequency evaluation assessments such as modulation transfer function (a ratio of 66.48% of MTF_50% (50% MTF value)), noise power spectrum (a ratio of 35.25% of f_50% (50% value of the Nyquist frequency)) and noise equivalent quanta (1-2 orders of magnitude at high frequencies). Experimental results again confirm the superiority of proposed strategy relative to the conventional one in terms of edge sharpness and CNR (an average increase of 67.35%).

Comparison of microstructural imaging of the root canal isthmus using propagation-based X-ray phase-contrast and absorption micro-computed tomography

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.

Exact reconstruction condition for angle-limited computed tomography of chemiluminescence

Computed tomography of chemiluminescence (CTC) is an effective technique for three-dimensional (3D) combustion diagnostics. It reconstructs the 3D concentrations of intermediate species or 3D images of flame topology by multiple chemiluminescence projections captured from different perspectives. In the previous studies of CTC systems, it was assumed that projections from arbitrary perspectives are available. However, for some practical applications, the range of view angles and the number of projections might be restricted due to the optical access limitation, greatly affecting the reconstruction quality. In this paper, the exact reconstruction condition for angle-limited computed tomography of chemiluminescence was studied based on Mojette transform theories, and it was demonstrated by numerical simulations and experiments. The studies indicate that the object tested within limited angles can be well reconstructed when the number of grids, the number of projections, and the sampling rate of projections satisfy the exact reconstruction condition. By increasing the sampling rate of projections, high-quality tomographic reconstruction can be achieved by a few projections in a small angle range. Although this technique is discussed under combustion diagnostics, it can also be used and adapted for other tomography methods.