Inter-module gap filling method for photon counting detectors based on dual acquisition.

The use of photon counting detectors in X-ray imaging missions can effectively improve the signal-to-noise ratio and image resolution. However, the stitching of photon counting detector modules leads to large-size localized information loss in the acquired projected image, which seriously affects the regional observation. In this paper, we propose a method to fill the inter-module gap based on dual acquisition, referred to as the GFDA algorithm, which is divided into three main steps: (i) acquire the main projection by short-exposure scanning, and then scan again by vertically moving the carrier table to acquire the reference projection; (ii) use the alignment method to locate the projected region of interest; (iii) use image stitching and image fusion to recover the missing information. We analyzed the gray value of the region of interest of the Siemens star projection and the reconstructed conch slice data, and proved that the proposed method can recover the information more smoothly and perfectly. The GFDA algorithm is able to achieve a better image restoration effect without additional scanning time and better retain image details. In addition, the GFDA algorithm is scalable, which is demonstrated in the task of filling the stitching of multiple types of photonic technology detectors.

Exploration of the Binding Mechanism of Cyclic Dinucleotide Analogs to Stimulating Factor Proteins and the Implications for Subsequent Analog Drug Design.

Cyclic dinucleotides (CDNs) are cyclic molecules consisting of two nucleoside monophosphates linked by two phosphodiester bonds, which act as a second messenger and bind to the interferon gene stimulating factor (STING) to activate the downstream signaling pathway and ultimately induce interferon secretion, initiating an anti-infective immune response. Cyclic dinucleotides and their analogs are lead compounds in the immunotherapy of infectious diseases and tumors, as well as immune adjuvants with promising applications. Many agonists of pathogen recognition receptors have been developed as effective adjuvants to optimize vaccine immunogenicity and efficacy. In this work, the binding mechanism of human-derived interferon gene-stimulating protein and its isoforms with cyclic dinucleotides and their analogs was theoretically investigated using computer simulations and combined with experimental results in the hope of providing guidance for the subsequent synthesis of cyclic dinucleotide analogs.

Engineering of formate dehydrogenase for improving conversion potential of carbon dioxide to formate.

Formate dehydrogenase (FDH) is a D-2-hydroxy acid dehydrogenase, which can reversibly reduce CO2 to formate and thus act as non-photosynthetic CO2 reductase. In order to increase catalytic efficiency of formate dehydrogenase for CO2 reduction, two mutants V328I/F285W and V354G/F285W were obtained of which reduction activity was about two times more than the parent CbFDHM2, and the formate production from CO2 catalyzed by mutants were 2.9 and 2.7-fold higher than that of the parent CbFDHM2. The mutants had greater potential in CO2 reduction. The optimal temperature for V328I/F285W and V354G/F285W was 55 °C, and they showed increasement of relative activity under 45 °C to 55 °C compared with parent. The optimal pH for the mutants was 9.0, and they showed excellent stability in pH 4.0-11.5. The kcat/Km values of mutants were 1.75 times higher than that of the parent. Then the molecular basis for its improvement of biochemical characteristics were preliminarily elucidated by computer-aided methods. All of these results further established a solid foundation for molecular modification of formate dehydrogenase and CO2 reduction.

Tracking the Progression of the Simulated Bronze Disease-A Laboratory X-ray Microtomography Study.

The internal three-dimensional characteristics of X-ray microtomography (micro-CT) has great application potential in the field of bronze corrosion. This work presents a method of simulating bronze disease based on an in situ micro-CT image to study the characteristics of the oxidative hydrolysis reactions of copper(I) chloride and copper(II) chloride dihydrate. A series of high-resolution reconstruction images were obtained by carrying out micro-CT at three key points throughout the experiment. We found that the reactions of copper(I) chloride and copper(II) chloride dihydrate showed different characteristics at different stages of the simulation in the micro-CT view. The method proposed in this work specifically simulated one single type of bronze corrosion and characterized the evolution characteristics of simulated bronze disease. It provides a new perspective to investigate bronze disease and can help improve the subsequent use of micro-CT to distinguish real bronze corrosions.

Drift correction in laboratory nanocomputed tomography using joint feature correlation.

Laboratory nanocomputed tomography (nano-CT), which can provide a spatial resolution of up to 100 nm, has been widely used due to its volume advantage. However, the drift of the x-ray source focal spot and the thermal expansion of the mechanical system can cause projection drift during long-time scanning. The three-dimensional result reconstructed from the drifted projections contains severe drift artifacts, which reduce the spatial resolution of nano-CT. Registering the drifted projections using rapidly acquired sparse projections is one of the mainstream correction methods, but the high noise and contrast differences of projections in nano-CT affect the correction effectiveness of existing methods. Herein, we propose a rough-to-refined projection registration method, which fully combines the information of the features in the gray and frequency domains of the projections. Simulation data show that the drift estimation accuracy of the proposed method is improved by 5× and 16× compared with the mainstream random sample consensus and locality preserving matching based on features. The proposed method can effectively improve the imaging quality of nano-CT.

Correction to "Highly Sensitive Surface-Enhanced Raman Spectroscopy for the Surface Corrosion Analysis of Bronze Relics Using the Polyacrylonitrile/Polyvinylpyrrolidone Silver Nanoparticle Flexible Substrate".

[This corrects the article DOI: 10.1021/acsomega.2c06376.].

Geometric parameters sensitivity evaluation based on projection trajectories for X-ray cone-beam computed laminography.

BACKGROUND: X-ray cone-beam computed laminography (CL) is widely used for large flat objects that computed tomography (CT) cannot investigate. The rotation angle of axis tilt makes geometric correction of CL system more complicated and has more uncertain factors. Therefore, it is necessary to evaluate sensitivity of the geometric parameters of CL system in advance. OBJECTIVE: This study aims to objectively and comprehensively evaluate sensitivity of CL geometric parameters based on the projection trajectory. METHODS: This study proposes the Minimum Deviation Unit (MDU) to evaluate sensitivity of CL geometric parameters. First, the projection trajectory formulas are derived according to the spatial relationship of CL system geometric parameters. Next, the MDU of the geometric parameters is obtained based on the projection trajectories and used as the evaluation index to measure the sensitivity of parameters. Then, the influence of the rotation angle of the axis tilt and magnification on the MDU of the parameters is analyzed. RESULTS: At low magnification, three susceptible parameters (η, u0, v0) with MDU less than 1 (° or mm) must be calibrated accurately to avoid geometric artifacts. The sensitivity of CL parameters increases as the magnification increases, and all parameters become highly sensitive when the magnification power is greater than 10. CONCLUSION: The results of this study have important guiding significance for the subsequent further parameter calibration.

Highly Sensitive Surface-Enhanced Raman spectroscopy for the Surface Corrosion Analysis of Bronze Relics Using the Polyacrylonitrile/Polyvinylpyrrolidone Silver Nanoparticle Flexible Substrate.

Surface-enhanced Raman spectroscopy (SERS) is widely used in biological and chemical analyses and in other fields because of its advantages such as high sensitivity and nondestructive nature. Ancient bronze cultural relics of China are exquisitely shaped and highly ornamental. Harmful rust components on the surface of bronze cultural relics have been extensively analyzed. SERS is beneficial to the surface composition analysis of ancient Chinese bronze relics and can be used for accurate characterization with almost zero damage to the surface. In this study, we designed a solution with polyacrylonitrile (PAN) and polyvinylpyrrolidone (PVP) macromolecules as precursors, which were electrospun and used as the nanofiber substrate. After tannic acid modification, the substrate was loaded with silver nanoparticles by using Tollens' reagent as the silver source and glutaraldehyde as the reducing agent in a water bath. The morphology and size of silver nanoparticles were adjusted by changing the reaction times. The effects of tannic acid and PVP as stabilizers were investigated. R6G and basic copper chloride were used as probe molecules for substrate SERS, and the Raman enhancement factor was calculated. The SERS performance of the substrate with high sensitivity was verified through characterization.

STEDNet: Swin transformer-based encoder-decoder network for noise reduction in low-dose CT.

BACKGROUND: Low-dose computed tomography (LDCT) can reduce the dose of X-ray radiation, making it increasingly significant for routine clinical diagnosis and treatment planning. However, the noise introduced by low-dose X-ray exposure degrades the quality of CT images, affecting the accuracy of clinical diagnosis. Purpose The noises, artifacts, and high-frequency components are similarly distributed in LDCT images. Transformer can capture global context information in an attentional manner to create distant dependencies on targets and extract more powerful features. In this paper, we reduce the impact of image errors on the ability to retain detailed information and improve the noise suppression performance by fully mining the distribution characteristics of image information. METHODS: This paper proposed an LDCT noise and artifact suppressing network based on Swin Transformer. The network includes a noise extraction sub-network and a noise removal sub-network. The noise extraction and removal capability are improved using a coarse extraction network of high-frequency features based on full convolution. The noise removal sub-network improves the network's ability to extract relevant image features by using a Swin Transformer with a shift window as an encoder-decoder and skip connections for global feature fusion. Also, the perceptual field is extended by extracting multi-scale features of the images to recover the spatial resolution of the feature maps. The network uses a loss constraint with a combination of L1 and MS-SSIM to improve and ensure the stability and denoising effect of the network. RESULTS: The denoising ability and clinical applicability of the methods were tested using clinical datasets. Compared with DnCNN, RED-CNN, CBDNet and TSCN, the STEDNet method shows a better denoising effect on RMSE and PSNR. The STEDNet method effectively removes image noise and preserves the image structure to the maximum extent, making the reconstructed image closest to the NDCT image. The subjective and objective analysis of several sets of experiments shows that the method in this paper can effectively maintain the structure, edges, and textures of the denoised images while having good noise suppression performance. In the real data evaluation, the RMSE of this method is reduced by 18.82%, 15.15%, 2.25%, and 1.10% on average compared with DnCNN, RED-CNN, CBDNet, and TSCNN, respectively. The average improvement of PSNR is 9.53%, 7.33%, 2.65%, and 3.69%, respectively. CONCLUSIONS: This paper proposed a LDCT image denoising algorithm based on end-to-end training. The method in this paper can effectively improve the diagnostic performance of CT images by constraining the details of the images and restoring the LDCT image structure. The problem of increased noise and artifacts in CT images can be solved while maintaining the integrity of CT image tissue structure and pathological information. Compared with other algorithms, this method has better denoising effects both quantitatively and qualitatively.

Thermal drift correction method for laboratory nanocomputed tomography based on global mixed evaluation.

Nanocomputed tomography (nanoCT) is an effective tool for the nondestructive observation of 3D structures of nanomaterials; however, it requires additional correction phantom to reduce artifacts induced by the focal drift of the X-ray source and mechanical thermal expansion. Drift correction without a correction phantom typically uses rapidly acquired sparse projections to align the original projections. The noise and brightness difference in the projections limit the accuracy of existing feature-based methods such as locality preserving matching (LPM) and random sample consensus (RANSAC). Herein, a rough-to-refined correction framework based on global mixed evaluation (GME) is proposed for precise drift estimation. First, a new evaluation criterion for projection alignment, named GME, which comprises the structural similarity (SSIM) index and average phase difference (APD), is designed. Subsequently, an accurate projection alignment is achieved to estimate the drift by optimizing the GME within the proposed correction framework based on the rough-to-refined outlier elimination strategy. The simulated 2D projection alignment experiments show that the accuracy of the GME is improved by 14× and 12× than that of the mainstream feature-based methods LPM and RANSAC, respectively. The proposed method is validated through actual 3D imaging experiments.

Multiscale Dense U-Net: A Fast Correction Method for Thermal Drift Artifacts in Laboratory NanoCT Scans of Semi-Conductor Chips.

The resolution of 3D structure reconstructed by laboratory nanoCT is often affected by changes in ambient temperature. Although correction methods based on projection alignment have been widely used, they are time-consuming and complex. Especially in piecewise samples (e.g., chips), the existing methods are semi-automatic because the projections lose attenuation information at some rotation angles. Herein, we propose a fast correction method that directly processes the reconstructed slices. Thus, the limitations of the existing methods are addressed. The method is named multiscale dense U-Net (MD-Unet), which is based on MIMO-Unet and achieves state-of-the-art artifacts correction performance in nanoCT. Experiments show that MD-Unet can significantly boost the correction performance (e.g., with three orders of magnitude improvement in correction speed compared with traditional methods), and MD-Unet+ improves 0.92 dB compared with MIMO-Unet in the chip dataset.

Completion of Metal-Damaged Traces Based on Deep Learning in Sinogram Domain for Metal Artifacts Reduction in CT Images.

In computed tomography (CT) images, the presence of metal artifacts leads to contaminated object structures. Theoretically, eliminating metal artifacts in the sinogram domain can correct projection deviation and provide reconstructed images that are more real. Contemporary methods that use deep networks for completing metal-damaged sinogram data are limited to discontinuity at the boundaries of traces, which, however, lead to secondary artifacts. This study modifies the traditional U-net and adds two sinogram feature losses of projection images-namely, continuity and consistency of projection data at each angle, improving the accuracy of the complemented sinogram data. Masking the metal traces also ensures the stability and reliability of the unaffected data during metal artifacts reduction. The projection and reconstruction results and various evaluation metrics reveal that the proposed method can accurately repair missing data and reduce metal artifacts in reconstructed CT images.

Thermal Drift Correction for Laboratory Nano Computed Tomography via Outlier Elimination and Feature Point Adjustment.

Thermal drift of nano-computed tomography (CT) adversely affects the accurate reconstruction of objects. However, feature-based reference scan correction methods are sometimes unstable for images with similar texture and low contrast. In this study, based on the geometric position of features and the structural similarity (SSIM) of projections, a rough-to-refined rigid alignment method is proposed to align the projection. Using the proposed method, the thermal drift artifacts in reconstructed slices are reduced. Firstly, the initial features are obtained by speeded up robust features (SURF). Then, the outliers are roughly eliminated by the geometric position of global features. The features are refined by the SSIM between the main and reference projections. Subsequently, the SSIM between the neighborhood images of features are used to relocate the features. Finally, the new features are used to align the projections. The two-dimensional (2D) transmission imaging experiments reveal that the proposed method provides more accurate and robust results than the random sample consensus (RANSAC) and locality preserving matching (LPM) methods. For three-dimensional (3D) imaging correction, the proposed method is compared with the commonly used enhanced correlation coefficient (ECC) method and single-step discrete Fourier transform (DFT) algorithm. The results reveal that proposed method can retain the details more faithfully.

X-ray cone-beam computed tomography geometric artefact reduction based on a data-driven strategy.

Cone-beam computed tomography (CBCT) enables three-dimensional imaging of the internal structure of objects in a non-invasive way with high accuracy. Practical misalignment of the CBCT system causes geometric artefacts in reconstructed images, which seriously degrades image quality in ways such as detail loss and decreased spatial resolution. This leads to inaccurate distinction of defects in detection, especially in precise industrial fields like aerospace and instrument manufacturing. This paper presents a method to reduce the geometric artefacts based on a data-driven strategy, which is an end-to-end modified fully convolutional neural network (M-FCNN). The designed M-FCCN contains five convolution layers and five deconvolution layers for feature extraction and output image rebuilding, respectively. In addition, the pooling layer is not used in the designed M-FCNN, considering the preservation of details in the reconstructed image. In this M-FCCN, artefact images with different features have been trained separately. After training, the M-FCNN can be applied to directly reduce geometric artefacts in the reconstructed image. The designed M-FCNN has been demonstrated with different types of synthetic data and has achieved accurate results. It is also validated with practical data, including carbon composite and medical oral phantoms with comparable quality to phantom-based methods, proving that it is an effective way to reduce geometric artefacts in the image domain by means of a data-driven strategy.

A method to determine the detector locations of the cone-beam projection of the balls' centers.

In geometric calibration of cone-beam computed tomography (CBCT), sphere-like objects such as balls are widely imaged, the positioning information of which is obtained to determine the unknown geometric parameters. In this process, the accuracy of the detector location of CB projection of the center of the ball, which we call the center projection, is very important, since geometric calibration is sensitive to errors in the positioning information. Currently in almost all the geometric calibration using balls, the center projection is invariably estimated by the center of the support of the projection or the centroid of the intensity values inside the support approximately. Clackdoyle's work indicates that the center projection is not always at the center of the support or the centroid of the intensity values inside, and has given a quantitative analysis of the maximum errors in evaluating the center projection by the centroid. In this paper, an exact method is proposed to calculate the center projection, utilizing both the detector location of the ellipse center and the two axis lengths of the ellipse. Numerical simulation results have demonstrated the precision and the robustness of the proposed method. Finally there are some comments on this work with non-uniform density balls, as well as the effect by the error occurred in the evaluation for the location of the orthogonal projection of the cone vertex onto the detector.

3D alternating direction TV-based cone-beam CT reconstruction with efficient GPU implementation.

Iterative image reconstruction (IIR) with sparsity-exploiting methods, such as total variation (TV) minimization, claims potentially large reductions in sampling requirements. However, the computation complexity becomes a heavy burden, especially in 3D reconstruction situations. In order to improve the performance for iterative reconstruction, an efficient IIR algorithm for cone-beam computed tomography (CBCT) with GPU implementation has been proposed in this paper. In the first place, an algorithm based on alternating direction total variation using local linearization and proximity technique is proposed for CBCT reconstruction. The applied proximal technique avoids the horrible pseudoinverse computation of big matrix which makes the proposed algorithm applicable and efficient for CBCT imaging. The iteration for this algorithm is simple but convergent. The simulation and real CT data reconstruction results indicate that the proposed algorithm is both fast and accurate. The GPU implementation shows an excellent acceleration ratio of more than 100 compared with CPU computation without losing numerical accuracy. The runtime for the new 3D algorithm is about 6.8 seconds per loop with the image size of 256 × 256 × 256 and 36 projections of the size of 512 × 512.

Edge guided image reconstruction in linear scan CT by weighted alternating direction TV minimization.

Linear scan computed tomography (CT) is a promising imaging configuration with high scanning efficiency while the data set is under-sampled and angularly limited for which high quality image reconstruction is challenging. In this work, an edge guided total variation minimization reconstruction (EGTVM) algorithm is developed in dealing with this problem. The proposed method is modeled on the combination of total variation (TV) regularization and iterative edge detection strategy. In the proposed method, the edge weights of intermediate reconstructions are incorporated into the TV objective function. The optimization is efficiently solved by applying alternating direction method of multipliers. A prudential and conservative edge detection strategy proposed in this paper can obtain the true edges while restricting the errors within an acceptable degree. Based on the comparison on both simulation studies and real CT data set reconstructions, EGTVM provides comparable or even better quality compared to the non-edge guided reconstruction and adaptive steepest descent-projection onto convex sets method. With the utilization of weighted alternating direction TV minimization and edge detection, EGTVM achieves fast and robust convergence and reconstructs high quality image when applied in linear scan CT with under-sampled data set.

Practical geometric calibration for helical cone-beam industrial computed tomography.

In helical cone-beam industrial computed tomography (ICT), the reconstructed images may be interfered by geometry artifacts due to the presence of mechanical misalignments. To obtain artifact-free reconstruction images, a practical geometric calibration method for helical scan is investigated based on Noo's analytic geometric calibration method for circular scan. The presented method is implemented by first dividing the whole ascending path of helical scan into several pieces, then acquiring the projections of a dedicated calibration phantom in circular scan at each section point, of which geometry parameters are calculated using Noo's analytic method. At last, the geometry parameters of each projection in a piece can be calculated by those of the two end points of the piece. We performed numerical simulations and real data experiments to study the performance of the presented method. The experimental results indicated that the method can obtain high-precision geometry parameters of helical scan and give satisfactory reconstruction images.

Data processing and analysis of the imaging Thomson scattering diagnostic system on HT-7 tokamak.

A high spatial resolution imaging Thomson scattering diagnostic system was developed in ASIPP (Institute of Plasma Physics, Chinese Academy of Sciences). After about one month trial running on the superconducting HT-7 (Hefei Tokamak-7) tokamak, the system was proved to be capable of measuring plasma electron temperature. The system setup and data calibration are described in this paper and then the instrument function is studied in detail, as well as the measurement capability, an electron temperature of 50 eV to 2 keV and density beyond 1 × 10(19) m(-3). Finally, the data processing method and experimental results are presented.

Upgraded multipulse laser and multipoint Thomson scattering diagnostics on EAST.

Recently a new Thomson scattering diagnostic system was upgraded in EAST tokamak experiment using a multipulse Nd:YAG (neodymium-yttrium aluminium garnet) laser and a multipoint observation volumes. This diagnostic uses a new optical laser alignment technique that was made to determine accurately the laser position, and a new lens collection system that enables the measurement of wider plasma's object. A composite control system made we can get the results in several seconds. Furthermore, a new data processing method was adopted for much exact results.