Truncated total variation in fractional B-spline wavelet transform for micro-CT image denoising.

BACKGROUND: In medical applications, computed tomography (CT) is widely used to evaluate various sample characteristics. However, image quality of CT reconstruction can be degraded due to artifacts. OBJECTIVE: To propose and test a truncated total variation (truncation TV) model to solve the problem of large penalties for the total variation (TV) model. METHODS: In this study, a truncated TV image denoising model in the fractional B-spline wavelet domain is developed to obtain the best solution. The method is validated by the analysis of CT reconstructed images of actual biological Pigeons samples. For this purpose, several indices including the peak signal-to-noise ratio (PSNR), structural similarity index (SSIM) and mean square error (MSE) are used to evaluate the quality of images. RESULTS: Comparing to the conventional truncated TV model that yields 22.55, 0.688 and 361.17 in PSNR, SSIM and MSE, respectively, using the proposed fractional B-spline-truncated TV model, the computed values of these evaluation indices change to 24.24, 0.898 and 244.98, respectively, indicating substantial reduction of image noise with higher PSNR and SSIM, and lower MSE. CONCLUSIONS: Study results demonstrate that compared with many classic image denoising methods, the new denoising algorithm proposed in this study can more effectively suppresses the reconstructed CT image artifacts while maintaining the detailed image structure.

Synchrotron microtomography image restoration via regularization representation and deep CNN prior.

BACKGROUND AND OBJECTIVE: Synchrotron-based X-ray microtomography (S-µCT) is a promising imaging technique that plays an important role in modern medical science. S-µCT systems often cause various artifacts and noises in the reconstructed CT images, such as ring artifacts, quantum noise, and electronic noise. In most situations, such noise and artifacts occur simultaneously, which results in a deterioration in the image quality and affects subsequent research. Due to the complexity of the distribution of these mixed artifacts and noise, it is difficult to restore the corrupted images. To address this issue, we propose a novel algorithm to remove mixed artifacts and noise from S-µCT images simultaneously. METHODS: There are two important aspects of our method. Regarding ring artifacts, because of their specific structural characteristics, regularization-based methods are more suitable; thus, low-rank tensor decomposition and total variation are utilized to represent their directional and locally piecewise smoothness properties. Moreover, to determine the implicit prior of the random noise, a convolutional neural network (CNN) based method is used. The advantages of traditional regularization and the deep CNN are then combined and embedded in a plug-and-play framework. Hence, an efficient image restoration algorithm is proposed to address the problem of mixed artifacts and noise in S-µCT images. RESULTS: Our proposed method was assessed by utilizing simulations and real data experiments. The qualitative results showed that the proposed method could effectively remove ring artifacts as well as random noise. The quantitative results demonstrated that the proposed method achieved almost the best results in terms of PSNR, SSIM and MAE compared to other methods. CONCLUSIONS: The proposed method can serve as an effective tool for restoring corrupted S-µCT images, and it has the potential to promote the application of S-µCT.

3D ring artifacts removal algorithm combined low-rank tensor decomposition with spatial-sequential total variation regularization and its application in phase-contrast microtomography.

PURPOSE: 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 two-dimensional (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, spatial-sequential 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. CONCLUSION: The experimental results on simulations and real data clearly demonstrated that the proposed algorithm can significantly improve the quality of PC-µCT images compared with the existing popular algorithms, and it has great potential to promote the application of high-resolution imaging for visualizing biological tissues.

Dual-path deep learning reconstruction framework for propagation-based X-ray phase-contrast computed tomography with sparse-view projections.

Propagation-based X-ray phase-contrast computed tomography (PB-PCCT) can serve as an effective tool for studying organ function and pathologies. However, it usually suffers from a high radiation dose due to the long scan time. To alleviate this problem, we propose a deep learning reconstruction framework for PB-PCCT with sparse-view projections. The framework consists of dual-path deep neural networks, where the edge detection, edge guidance, and artifact removal models are incorporated into two subnetworks. It is worth noting that the framework has the ability to achieve excellent performance by exploiting the data-based knowledge of the sample material characteristics and the model-based knowledge of PB-PCCT. To evaluate the effectiveness and capability of the proposed framework, simulations and real experiments were performed. The results demonstrated that the proposed framework could significantly suppress streaking artifacts and produce high-contrast and high-resolution computed tomography images.

Iterative reconstruction algorithm based on discriminant adaptive-weighted TV regularization for fibrous biological tissues using in-line X-ray phase-contrast imaging.

In-line X-ray phase-contrast computed tomography (IL-PCCT) can produce high-contrast and high-resolution images of biological samples, and it has a great advantage with regard to imaging the microstructures and morphologies of fibrous biological tissues (FBTs). Filtered back projection (FBP) is widely used in ILPCCT. However, it requires long scanning times and high radiation doses to produce high-quality CT images, and this restricts its applicability in biomedical and preclinical studies on FBTs. To solve this problem, a novel IL-PCCT reconstruction algorithm is proposed to decrease the radiation dose by reducing the number of projections and reconstruct high-quality CT images of FBTs. The proposed algorithm incorporates the FBP method into the iterative reconstruction framework. Considering the area types and anisotropic edge properties of FBTs, a discriminant adaptive-weighted total variation model is introduced to optimize the intermediate reconstructed images. A fibrous phantom simulation and real experiment were performed to assess the performance of the proposed algorithm. Simulation and experimental results demonstrated that the proposed algorithm is an effective IL-PCCT reconstruction method for FBTs with incomplete projection data, and it has a great ability to suppress artifacts and preserve the edges of fibrous structures.

Sparse-domain regularized stripe decomposition combined with guided-image filtering for ring artifact removal in propagation-based x-ray phase-contrast CT.

Propagation-based x-ray phase-contrast computed tomography (PB-PCCT) images often suffer from severe ring artifacts. Ring artifacts are mainly caused by the nonuniform response of detector elements, and they can degrade image quality and affect the subsequent image processing and quantitative analyses. To remove ring artifacts in PB-PCCT images, a novel method combined sparse-domain regularized stripe decomposition (SDRSD) method with guided image filtering (GIF) was proposed. In this method, polar coordinate transformation was utilized to convert the ring artifacts to stripe artifacts. And then considering the directional and sparse properties of the stripe artifacts and the continuity characteristics of the sample, the SDRSD method was designed to remove stripe artifacts. However, for the SDRSD method, the presence of noise may destroy the edges of the stripe artifacts and lead to incomplete decomposition. Hence, a simple and efficient smoothing technique, namely GIF, was employed to overcome this issue. The simulations and real experiments demonstrated that the proposed method could effectively remove ring artifacts as well as preserve the structures and edges of the samples. In conclusion, the proposed method can serve as an effective tool to remove ring artifacts in PB-PCCT images, and it has high potential for promoting the biomedical and preclinical applications of PB-PCCT.

Three-dimensional visualization of microvasculature from few-projection data using a novel CT reconstruction algorithm for propagation-based X-ray phase-contrast imaging.

Propagation-based X-ray phase-contrast imaging (PBI) is a powerful nondestructive imaging technique that can reveal the internal detailed structures in weakly absorbing samples. Extending PBI to CT (PBCT) enables high-resolution and high-contrast 3D visualization of microvasculature, which can be used for the understanding, diagnosis and therapy of diseases involving vasculopathy, such as cardiovascular disease, stroke and tumor. However, the long scan time for PBCT impedes its wider use in biomedical and preclinical microvascular studies. To address this issue, a novel CT reconstruction algorithm for PBCT is presented that aims at shortening the scan time for microvascular samples by reducing the number of projections while maintaining the high quality of reconstructed images. The proposed algorithm combines the filtered backprojection method into the iterative reconstruction framework, and a weighted guided image filtering approach (WGIF) is utilized to optimize the intermediate reconstructed images. Notably, the homogeneity assumption on the microvasculature sample is adopted as prior knowledge, and therefore, a prior image of microvasculature structures can be acquired by a k-means clustering approach. Then, the prior image is used as the guided image in the WGIF procedure to effectively suppress streaking artifacts and preserve microvasculature structures. To evaluate the effectiveness and capability of the proposed algorithm, simulation experiments on 3D microvasculature numerical phantom and real experiments with CT reconstruction on the microvasculature sample are performed. The results demonstrate that the proposed algorithm can, under noise-free and noisy conditions, significantly reduce the artifacts and effectively preserve the microvasculature structures on the reconstructed images and thus enables it to be used for clear and accurate 3D visualization of microvasculature from few-projection data. Therefore, for 3D visualization of microvasculature, the proposed algorithm can be considered an effective approach for reducing the scan time required by PBCT.

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

In-line X-ray phase-contrast computed tomography (IL-PCCT) is a valuable tool for revealing the internal detailed structures in weakly absorbing objects (e.g. biological soft tissues), and has a great potential to become clinically applicable. However, the long scanning time for IL-PCCT will result in a high radiation dose to biological samples, and thus impede the wider use of IL-PCCT in clinical and biomedical imaging. To alleviate this problem, a new iterative CT reconstruction algorithm is presented that aims to decrease the radiation dose by reducing the projection views, while maintaining the high quality of reconstructed images. The proposed algorithm combines the adaptive-weighted anisotropic total p-variation (AwaTpV, 0 < p < 1) regularization technique with projection onto convex sets (POCS) strategy. Noteworthy, the AwaTpV regularization term not only contains the horizontal and vertical image gradients but also adds the diagonal image gradients in order to enforce the directional continuity in the gradient domain. To evaluate the effectiveness and ability of the proposed algorithm, experiments with a numerical phantom and synchrotron IL-PCCT were performed, respectively. The results demonstrated that the proposed algorithm had the ability to significantly reduce the artefacts caused by insufficient data and effectively preserved the edge details under noise-free and noisy conditions, and thus could be used as an effective approach to decrease the radiation dose for IL-PCCT.

An iterative image reconstruction algorithm combined with forward and backward diffusion filtering for in-line X-ray phase-contrast computed tomography.

In-line X-ray phase-contrast computed tomography (IL-PCCT) can reveal fine inner structures for low-Z materials (e.g. biological soft tissues), and shows high potential to become clinically applicable. Typically, IL-PCCT utilizes filtered back-projection (FBP) as the standard reconstruction algorithm. However, the FBP algorithm requires a large amount of projection data, and subsequently a large radiation dose is needed to reconstruct a high-quality image, which hampers its clinical application in IL-PCCT. In this study, an iterative reconstruction algorithm for IL-PCCT was proposed by combining the simultaneous algebraic reconstruction technique (SART) with eight-neighbour forward and backward (FAB8) diffusion filtering, and the reconstruction was performed using the Shepp-Logan phantom simulation and a real synchrotron IL-PCCT experiment. The results showed that the proposed algorithm was able to produce high-quality computed tomography images from few-view projections while improving the convergence rate of the computed tomography reconstruction, indicating that the proposed algorithm is an effective method of dose reduction for IL-PCCT.

[Correction of the projection center of rotation based on the sinogram using translation matching method].

The accurate position of the center of rotation (COR) is a key factor to ensure the quality of computed tomography (CT) reconstructed images. The classic cross-correlation matching algorithm can not satisfy the requirements of high-quality CT imaging when the projection angle is 0 and 180°, and thus needs to be improved and innovated. In this study, considering the symmetric characteristic of the 0° and flipped 180° projection data in sinogram, a novel COR correction algorithm based on the translation and match of the 0° and 180° projection data was proposed. The OTSU method was applied to reduce noise on the background, and the minimum offset of COR was quantified using the L1-norm, and then a precise COR was obtained for the image correction and reconstruction. The Sheep-Logan simulation model with random gradients and Gaussian noise and the real male SD rats samples which contained the heterogenous tooth image and the homogenous liver image, were adopted to verify the performance of the new algorithm and the cross-correlation matching algorithm. The results show that the proposed algorithm has better robustness and higher accuracy of the correction (when the sampled data is from 10% to 50% of the full projection data, the COR value can still be measured accurately using the proposed algorithm) with less computational burden compared with the cross-correlation matching algorithm, and it is able to significantly improve the quality of the reconstructed images.

Combine TV-L1 model with guided image filtering for wide and faint ring artifacts correction of in-line x-ray phase contrast computed tomography.

In practice, mis-calibrated detector pixels give rise to wide and faint ring artifacts in the reconstruction image of the In-line phase-contrast computed tomography (IL-PC-CT). Ring artifacts correction is essential in IL-PC-CT. In this study, a novel method of wide and faint ring artifacts correction was presented based on combining TV-L1 model with guided image filtering (GIF) in the reconstruction image domain. The new correction method includes two main steps namely, the GIF step and the TV-L1 step. To validate the performance of this method, simulation data and real experimental synchrotron data are provided. The results demonstrate that TV-L1 model with GIF step can effectively correct the wide and faint ring artifacts for IL-PC-CT.

Simultaneous algebraic reconstruction technique based on guided image filtering.

The challenge of computed tomography is to reconstruct high-quality images from few-view projections. Using a prior guidance image, guided image filtering smoothes images while preserving edge features. The prior guidance image can be incorporated into the image reconstruction process to improve image quality. We propose a new simultaneous algebraic reconstruction technique based on guided image filtering. Specifically, the prior guidance image is updated in the image reconstruction process, merging information iteratively. To validate the algorithm practicality and efficiency, experiments were performed with numerical phantom projection data and real projection data. The results demonstrate that the proposed method is effective and efficient for nondestructive testing and rock mechanics.

Image reconstruction algorithm for in-line phase contrast imaging computed tomography with an improved anisotropic diffusion model.

Phase contrast imaging (PCI) is a new physical and biochemical technique. Practical biomedical applications combine PCI with computer tomography (CT), Phase contrast CT (PC-CT) can reconstruct 3D images of samples. How to reconstruct high quality image at a low radiation dose level is a hot topic for PC-CT. In order to reduce radiation dose, a strategy is to collect incomplete projection data by few-view projection data. This work presents a reconstruction method for incomplete data PC-CT. It is based on an algebraic iteration reconstruction algorithm and combined with an anisotropic diffusion model to reduce aliasing distortions.To validate the availability of this method, the research carried out a computer-simulated and real experimental synchrotron data. The computer-simulated and real data results demonstrate that the presented method can improve the convergence speed of image reconstruction and reduce the aliasing distortions by incomplete projection data for PC-CT. However, there is no proof that this is true for all kinds of structures.