Effective focal spot measurement method for X-ray source based on the dynamic translation of a light barrier.

The accuracy of measuring the effective focal spot of the X-ray source directly affects the spatial resolution of computed tomography (CT) reconstructed images. This study proposes what we believe to be a novel approach to measure the effective focal spot based on the dynamic translation of light barrier using an accessible measuring device. This method discretizes the effective focal spot of the X-ray source into multiple subfocal spots with varying intensities and establishes a nonlinear model between the effective focal spot and measurement data. Measurement data are obtained by moving the light barrier to different positions using the electric displacement stage. The shape, size, and intensity distribution of the effective focal spot are determined by calculating the normalized weighting coefficients for each subfocal spot from measurement data. The measurement device is simple and easy to operate. Additionally, the obtained effective focal spot exhibits high accuracy, and a higher spatial resolution can be realized by reconstructing the CT images using the measured focal spot information. Numerical and real experiments validate the proposed method.

A PCB image segmentation model based on rotational X-ray computed laminography imaging.

BACKGROUND: The rapid development of industrialization in printed circuit board (PCB) warrants more complexity and integrity, which entails an essential procedure of PCB inspection. X-ray computed laminography (CL) enables inspection of arbitrary regions for large-sized flat objects with high resolution. PCB inspection based on CL imaging is worthy of exploration. OBJECTIVE: This work aims to extract PCB circuit layer information based on CL imaging through image segmentation technique. METHODS: In this work, an effective and applicable segmentation model for PCB CL images is established for the first time. The model comprises two components, with one integrating edge diffusion and l0 smoothing to filter CL images with aliasing artifacts, and the other being the fuzzy energy-based active contour model driven by local pre-fitting energy to segment the filtered images. RESULT: The proposed model is able to suppress aliasing artifacts in the PCB CL images and has good performance on images of different circuit layers. CONCLUSIONS: Results of the simulation experiment reveal that the method is capable of accurate segmentation under ideal scanning condition. Testing of different PCBs and comparison of different segmentation methods authenticate the applicability and superiority of the model.

Dual spectral limited-angle CT imaging regularized by edge-preserving diffusion and smoothing.

Limited-angle CT scan is an effective way for nondestructive inspection of planar objects, and various methods have been proposed accordingly. When the scanned object contains high-absorption material, such as metal, existing methods may fail due to the beam hardening of X-rays. In order to overcome this problem, we adopt a dual spectral limited-angle CT scan and propose a corresponding image reconstruction algorithm, which takes the polychromatic property of the X-ray into consideration, makes basis material images free of beam hardening artifacts and metal artifacts, and then helps depress the limited-angle artifacts. Experimental results on both simulated PCB data and real data demonstrate the effectiveness of the proposed algorithm.

Therapeutic targets and potential agents for the treatment of COVID-19.

The outbreak of coronavirus disease 2019 (COVID-19), which is caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection, has become a global crisis. As of November 9, COVID-19 has already spread to more than 190 countries with 50,000,000 infections and 1,250,000 deaths. Effective therapeutics and drugs are in high demand. The structure of SARS-CoV-2 is highly conserved with those of SARS-CoV and Middle East respiratory syndrome-CoV. Enzymes, including RdRp, Mpro /3CLpro , and PLpro , which play important roles in viral transcription and replication, have been regarded as key targets for therapies against coronaviruses, including SARS-CoV-2. The identification of readily available drugs for repositioning in COVID-19 therapy is a relatively rapid approach for clinical treatment, and a series of approved or candidate drugs have been proven to be efficient against COVID-19 in preclinical or clinical studies. This review summarizes recent progress in the development of drugs against SARS-CoV-2 and the targets involved.

Projection data smoothing through noise-level weighted total variation regularization for low-dose computed tomography.

Reducing radiation dose while maintaining the quality of the reconstructed images is a major challenge in the computed tomography (CT) community. In light of the non-stationary Gaussian noise distribution, we developed a model that incorporates a noise-level weighted total variation (NWTV) regularization term for denoising the projection data. Contrary to the well-known edge-weighted total variation method, which aims for better edge preserving, the proposed NWTV tries to adapt the regularization with the spatially varying noise levels. Experiments on simulated data as well as the real imaging data suggest that the proposed NWTV regularization could achieve quite competitive results. For sinograms with sharp edges, the NWTV could do a better job at balancing noise reduction and edge preserving, such that noise is removed in a more uniform manner. Another conclusion from our experiments is that the well-recognized stair-casing artifacts of TV regularization play little role in the reconstructed images when the NWTV method is applied to low-dose CT imaging data.

Detector-unit-dependent calibration for polychromatic projections of rock core CT.

Computed tomography (CT) plays an important role in digital rock analysis, which is a new prospective technique for oil and gas industry. But the artifacts in CT images will influence the accuracy of the digital rock model. In this study, we proposed and demonstrated a novel method to restore detector-unit-dependent functions for polychromatic projection calibration by scanning some simple shaped reference samples. As long as the attenuation coefficients of the reference samples are similar to the scanned object, the size or position is not needed to be exactly known. Both simulated and real data were used to verify the proposed method. The results showed that the new method reduced both beam hardening artifacts and ring artifacts effectively. Moreover, the method appeared to be quite robust.

Attenuator design method for dedicated whole-core CT.

In whole-core CT imaging, scanned data corresponding to the central portion of a cylindrical core often suffer from photon starvation, because increasing photon flux will cause overflow on some detector units under the restriction of detector dynamic range. Either photon starvation or data overflow will lead to increased noise or severe artifacts in the reconstructed CT image. In addition, cupping shaped beam hardening artifacts also appear in the whole-core CT image. In this paper, we present a method to design an attenuator for cone beam whole-core CT, which not only reduces the dynamic range requirement for high SNR data scanning, but also corrects beam hardening artifacts. Both simulation and real data are employed to verify our design method.

Iterative dual energy material decomposition from spatial mismatched raw data sets.

Today's clinical dual energy computed tomography (DECT) scanners generally measure different rays for different energy spectra and acquire spatial mismatched raw data sets. The deficits in clinical DECT technologies suggest that mainly image based material decomposition methods are in use nowadays. However, the image based material decomposition is an approximate technique, and beam hardening artifacts remain in decomposition results. A recently developed image based iterative method for material decomposition from inconsistent rays (MDIR) can achieve much better image quality than the conventional image based methods. Inspired by the MDIR method, this paper proposes an iterative method to indirectly perform raw data based DECT even with completely mismatched raw data sets. The iterative process is initialized by density images that were obtained from an image based material decomposition. Then the density images are iteratively corrected by comparing the estimated polychromatic projections and the measured polychromatic projections. Only three iterations of the method are sufficient to greatly improve the qualitative and quantitative information in material density images. Compared with the MDIR method, the proposed method needs not to perform additional water precorrection. The advantages of the method are verified with numerical experiments from inconsistent noise free and noisy raw data.

Folded cavity angled-grating broad-area lasers.

The angled-grating broad-area laser is a promising candidate for high power, high brightness diode laser source. The key point in the design is the angled gratings which can simultaneously support the unique snake-like zigzag lasing mode and eliminate the direct Fabry-Perot (FP) feedback. Unlike a conventional laser waveguide mode, the phase front of the zigzag mode periodically changes along the propagation direction. By use of the mirror symmetry of the zigzag mode, we propose and demonstrate the folded cavity angled-grating broad-area lasers. One benefit of this design is to reduce the required wafer space compared to a regular angled-grating broad-area laser, especially in a long cavity laser for high power operation. Experimental results show that the folded cavity laser exhibits good beam quality in far field with a slightly larger threshold and smaller slope efficiency due to the additional interface loss.

An approach to increasing the resolution of industrial CT images based on an aperture collimator.

The spatial resolution of CT images is dominated by the focal spot size when it is large relative to the detector cells. We propose an approach to increase the spatial resolution by utilizing an aperture collimator. The aperture collimator is specially designed and placed in front of the X-ray source so that the rays penetrating the collimator form a set of narrow fan beams. Then an iterative algorithm is introduced to reconstruct CT images from the data obtained by scanning the narrow fan beams. Numerical experiments show that the proposed approach could significantly increase the resolution of the CT images. Furthermore, this approach is also robust against some challenging cases, such as the examination of low contrast object, reconstruction based on multi-energy data and perturbation of geometric errors in CT systems.

On-chip coherent combining of angled-grating diode lasers toward bar-scale single-mode lasers.

Single mode operation of broad-area diode lasers, which is the key to obtain high power, high brightness sources, is difficult due to highly nonlinear materials and strong coupling between gain and index. Conventional broad-area lasers usually operate with multiple modes and have poor beam quality. Laser bars usually consist of incoherently combined broad-area single emitters placed side by side. In this article, we have demonstrated a novel integrated laser architecture in which Bragg diffraction is used to realize simultaneous modal control and coherent combining of broad-area diode lasers. Our experimental results show that two 100 µm wide, 1.3mm long InP broad-area lasers provide near-diffraction-limited output beam and are coherently combined at the same time without any external optical components. Furthermore, our design can be expanded to a coherently combined broad-area laser array that turns a laser bar into a coherent single mode laser with diffraction-limited beam quality.

Noise reduction with low dose CT data based on a modified ROF model.

In order to reduce the radiation exposure caused by Computed Tomography (CT) scanning, low dose CT has gained much interest in research as well as in industry. One fundamental difficulty for low dose CT lies in its heavy noise pollution in the raw data which leads to quality deterioration for reconstructed images. In this paper, we propose a modified ROF model to denoise low dose CT measurement data in light of Poisson noise model. Experimental results indicate that the reconstructed CT images based on measurement data processed by our model are in better quality, compared to the original ROF model or bilateral filtering.

A multi-thread scheduling method for 3D CT image reconstruction using multi-GPU.

As a whole process, we present a concept that the complete reconstruction of CT image should include the computation part on GPUs and the data storage part on hard disks. From this point of view, we propose a Multi-Thread Scheduling (MTS) method to implement the 3D CT image reconstruction such as using FDK algorithm, to trade off the computing and storage time. In this method we use Multi-Threads to control GPUs and a separate thread to accomplish data storage, so that we make the calculation and data storage simultaneously. In addition, we use the 4-channel texture to maintain symmetrical projection data in CUDA framework, which can reduce the calculation time significantly. Numerical experiment shows that the time for the whole process with our method is almost the same as the data storage time.

Orientation Control for Nickel-Based Single Crystal Superalloys: Grain Selection Method Assisted by Directional Columnar Grains.

The service performance of single crystal blades depends on the crystal orientation. A grain selection method assisted by directional columnar grains is studied to control the crystal orientation of Ni-based single crystal superalloys. The samples were produced by the Bridgman technique at withdrawal rates of 100 µm/s. During directional solidification, the directional columnar grains are partially melted, and a number of stray grains are formed in the transition zone just above the melt-back interface. The grain selected by this method was one that grew epitaxially along the un-melted directional columnar grains. Finally, the mechanism of selection grain and application prospect of this grain selection method assisted by directional columnar grains is discussed.

Heterologous Expression of the Lactobacillus sakei Multiple Copper Oxidase to Degrade Histamine and Tyramine at Different Environmental Conditions.

Biogenic amines (BAs) are produced by microbial decarboxylation in various foods. Histamine and tyramine are recognized as the most toxic of all BAs. Applying degrading amine enzymes such as multicopper oxidase (MCO) is considered an effective method to reduce BAs in food systems. This study analyzed the characterization of heterologously expressed MCO from L. sakei LS. Towards the typical substrate 2,2'-azino-bis (3-ethylbenzothiazoline-6-sulfonic acid) (ABTS), the optimal temperature and pH for recombinant MCO (rMCO) were 25 °C and 3.0, respectively, with the specific enzyme activity of 1.27 U/mg. Then, the effect of different environmental factors on the degrading activity of MCO towards two kinds of BAs was investigated. The degradation activity of rMCO is independent of exogenous copper and mediators. Additionally, the oxidation ability of rMCO was improved for histamine and tyramine with an increased NaCl concentration. Several food matrices could influence the amine-oxidizing activity of rMCO. Although the histamine-degrading activities of rMCO were affected, this enzyme reached a degradation rate of 28.1% in the presence of surimi. Grape juice improved the tyramine degradation activity of rMCO by up to 31.18%. These characteristics of rMCO indicate that this enzyme would be a good candidate for degrading toxic biogenic amines in food systems.

Construction of an Assisted Model Based on Natural Language Processing for Automatic Early Diagnosis of Autoimmune Encephalitis.

INTRODUCTION: Early diagnosis and etiological treatment can effectively improve the prognosis of patients with autoimmune encephalitis (AE). However, anti-neuronal antibody tests which provide the definitive diagnosis require time and are not always abnormal. By using natural language processing (NLP) technology, our study proposes an assisted diagnostic method for early clinical diagnosis of AE and compares its sensitivity with that of previously established criteria. METHODS: Our model is based on the text classification model trained by the history of present illness (HPI) in electronic medical records (EMRs) that present a definite pathological diagnosis of AE or infectious encephalitis (IE). The definitive diagnosis of IE was based on the results of traditional etiological examinations. The definitive diagnosis of AE was based on the results of neuronal antibodies, and the diagnostic criteria of definite autoimmune limbic encephalitis proposed by Graus et al. used as the reference standard for antibody-negative AE. First, we automatically recognized and extracted symptoms for all HPI texts in EMRs by training a dataset of 552 cases. Second, four text classification models trained by a dataset of 199 cases were established for differential diagnosis of AE and IE based on a post-structuring text dataset of every HPI, which was completed using symptoms in English language after the process of normalization of synonyms. The optimal model was identified by evaluating and comparing the performance of the four models. Finally, combined with three typical symptoms and the results of standard paraclinical tests such as cerebrospinal fluid (CSF), magnetic resonance imaging (MRI), or electroencephalogram (EEG) proposed from Graus criteria, an assisted early diagnostic model for AE was established on the basis of the text classification model with the best performance. RESULTS: The comparison results for the four models applied to the independent testing dataset showed the naïve Bayesian classifier with bag of words achieved the best performance, with an area under the receiver operating characteristic curve of 0.85, accuracy of 84.5% (95% confidence interval [CI] 74.0-92.0%), sensitivity of 86.7% (95% CI 69.3-96.2%), and specificity of 82.9% (95% CI 67.9-92.8%), respectively. Compared with the diagnostic criteria proposed previously, the early diagnostic sensitivity for possible AE using the assisted diagnostic model based on the independent testing dataset was improved from 73.3% (95% CI 54.1-87.7%) to 86.7% (95% CI 69.3-96.2%). CONCLUSIONS: The assisted diagnostic model could effectively increase the early diagnostic sensitivity for AE compared to previous diagnostic criteria, assist physicians in establishing the diagnosis of AE automatically after inputting the HPI and the results of standard paraclinical tests according to their narrative habits for describing symptoms, avoiding misdiagnosis and allowing for prompt initiation of specific treatment.

Nervous system manifestations related to COVID-19 and their possible mechanisms.

In December 2019, the novel coronavirus disease (COVID-19) due to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection broke. With the gradual deepening understanding of SARS-CoV-2 and COVID-19, researchers and clinicians noticed that this disease is closely related to the nervous system and has complex effects on the central nervous system (CNS) and peripheral nervous system (PNS). In this review, we summarize the effects and mechanisms of SARS-CoV-2 on the nervous system, including the pathways of invasion, direct and indirect effects, and associated neuropsychiatric diseases, to deepen our knowledge and understanding of the relationship between COVID-19 and the nervous system.

Deep Learning-Based Classification of Cancer Cell in Leptomeningeal Metastasis on Cytomorphologic Features of Cerebrospinal Fluid.

Background: It is a critical challenge to diagnose leptomeningeal metastasis (LM), given its technical difficulty and the lack of typical symptoms. The existing gold standard of diagnosing LM is to use positive cerebrospinal fluid (CSF) cytology, which consumes significantly more time to classify cells under a microscope. Objective: This study aims to establish a deep learning model to classify cancer cells in CSF, thus facilitating doctors to achieve an accurate and fast diagnosis of LM in an early stage. Method: The cerebrospinal fluid laboratory of Xijing Hospital provides 53,255 cells from 90 LM patients in the research. We used two deep convolutional neural networks (CNN) models to classify cells in the CSF. A five-way cell classification model (CNN1) consists of lymphocytes, monocytes, neutrophils, erythrocytes, and cancer cells. A four-way cancer cell classification model (CNN2) consists of lung cancer cells, gastric cancer cells, breast cancer cells, and pancreatic cancer cells. Here, the CNN models were constructed by Resnet-inception-V2. We evaluated the performance of the proposed models on two external datasets and compared them with the results from 42 doctors of various levels of experience in the human-machine tests. Furthermore, we develop a computer-aided diagnosis (CAD) software to generate cytology diagnosis reports in the research rapidly. Results: With respect to the validation set, the mean average precision (mAP) of CNN1 is over 95% and that of CNN2 is close to 80%. Hence, the proposed deep learning model effectively classifies cells in CSF to facilitate the screening of cancer cells. In the human-machine tests, the accuracy of CNN1 is similar to the results from experts, with higher accuracy than doctors in other levels. Moreover, the overall accuracy of CNN2 is 10% higher than that of experts, with a time consumption of only one-third of that consumed by an expert. Using the CAD software saves 90% working time of cytologists. Conclusion: A deep learning method has been developed to assist the LM diagnosis with high accuracy and low time consumption effectively. Thanks to labeled data and step-by-step training, our proposed method can successfully classify cancer cells in the CSF to assist LM diagnosis early. In addition, this unique research can predict cancer's primary source of LM, which relies on cytomorphologic features without immunohistochemistry. Our results show that deep learning can be widely used in medical images to classify cerebrospinal fluid cells. For complex cancer classification tasks, the accuracy of the proposed method is significantly higher than that of specialist doctors, and its performance is better than that of junior doctors and interns. The application of CNNs and CAD software may ultimately aid in expediting the diagnosis and overcoming the shortage of experienced cytologists, thereby facilitating earlier treatment and improving the prognosis of LM.

An iterative reconstruction method based on monochromatic images for dual energy CT.

PURPOSE: Dual-energy computed tomography (DECT) scans objects using two different X-ray spectra to acquire more information, which is also called dual spectral CT (DSCT) in some articles. Compared to traditional CT, DECT exhibits superior material distinguishability. Therefore, DECT can be widely used in the medical and industrial domains. However, owing to the nonlinearity and ill condition of DECT, studies are underway on DECT reconstruction to obtain high quality images and achieve fast convergence speed. Therefore, in this study, we propose an iterative reconstruction method based on monochromatic images (IRM-MI) to rapidly obtain high-quality images in DECT reconstruction. METHODS: An IRM-MI is proposed for DECT. The proposed method converts DECT reconstruction problem from the basis material images decomposition to monochromatic images decomposition to significantly improve the convergence speed of DECT reconstruction by changing the coefficient matrix of the original equations to increase the angle of the high- and low-energy projection curves or reduce the condition number of the coefficient matrix. The monochromatic images were then decomposed into basis material images. Furthermore, we conducted numerical experiments to evaluate the performance of the proposed method. RESULTS: The decomposition results of the simulated data and real data experiments confirmed the effectiveness of the proposed method. Compared to the extended algebraic reconstruction technique (E-ART) method, the proposed method exhibited a significant increase in the convergence speed by increasing the angle of polychromatic projection curves or decreasing the condition number of the coefficient matrix, when choosing the appropriate monochromatic images. Therefore, the proposed method is also advantageous in acquiring high quality and rapidly converged images. CONCLUSIONS: We developed an iterative reconstruction method based on monochromatic images for the material decomposition for DECT. The numerical experiments using the proposed method validated its capability of decomposing the basis material images. Furthermore, the proposed method achieved faster convergence speed compared to the E-ART method.

4D-AirNet: a temporally-resolved CBCT slice reconstruction method synergizing analytical and iterative method with deep learning.

Four-dimensional (4D) cone-beam CT (CBCT) reconstructs temporally-resolved phases of 3D volumes often with the same amount of projection data that are meant for reconstructing a single 3D volume. 4D CBCT is a sparse-data problem that is very challenging for high-quality 4D CBCT image reconstruction. Here we develop a new method, namely 4D-AirNet, that synergizes analytical and iterative method with deep learning for high-quality temporally-resolved CBCT slice reconstruction. 4D-AirNet is an unrolling method using the optimization framework of fused analytical and iterative reconstruction (AIR), which is based on proximal forward-backward splitting (PFBS). Three different strategies are developed for 4D-AirNet: random-phase (RP), prior-guided (PG), and all-phase (AP). RP-AirNet and PG-AirNet utilize phase-by-phase training and reconstruction, while PG-AirNet also uses a prior image reconstructed with all-phase projection data. Dense connectivity is built into 4D-AirNet networks for improved reconstruction quality. In contrast, AP-AirNet trains and reconstructs all phases simultaneously. In addition, the joint regularization method of DL and conventional spatiotemporal total variation (TV) is investigated. 4D-AirNet methods were evaluated in comparison with conventional iterative (TV) and deep learning (LEARN) methods, using simulated 2D-t CBCT scans from a lung dataset with various sparse-data levels. The reconstruction results suggest 4D-AirNet methods outperform TV and LEARN, and AP-AirNet provides the best reconstruction quality overall.