Iodine concentration, HU accuracy, and metal artifacts evaluation on second-generation dual-layer spectral detector CT images with metal implants: a phantom study.

BACKGROUND: Metal implants may affect the image quality, iodine concentration (IC), and CT Hounsfield unit (HU) quantification accuracy. PURPOSE: To investigate the quantitative accuracy of IC and HU from dual-layer spectral detector (DLCT) in the presence of metal artifacts. MATERIAL AND METHODS: An experimental cylindrical phantom containing eight iodine inserts and two metal inserts was designed. The phantom underwent scanning at three radiation dose levels and two tube voltage settings. A set of conventional images (CIs), virtual monoenergetic images (VMIs), and iodine concentration maps (ICMs) were generated and measured for all the eight iodine inserts. Quantitative indicators of mean absolute percentage error (MAPE), artifact index (AI), contrast-to-noise ratio (CNR), signal-to-noise ratio (SNR), and standard deviation (SD) on CIs and VMIs were calculated for IC and HU. Subjective score evaluation was also conducted. RESULTS: The MAPEiodine values of all regions of interest across different scanning configurations were all <5%. Almost all APEiodine values were <5%, indicating that metal artifacts had little impact on IC measurements. When the tube voltage was fixed, the SD value of attenuation decreased with the increase of the tube current; this is also true when the tube current was fixed. The middle energy reconstructions seemed to give a good balance between reducing artifacts and improving contrast. CONCLUSION: VMIs from DLCT can reduce metal artifacts, the accuracy of IC quantification is not sensitive to imaging parameters. In summary, metal implants exhibit minimal impact on image quality and IC quantification accuracy in reconstructed images from DLCT.

[LSTM-XGBoost Based RR Intervals Time Series Prediction Method in Hypertensive Patients].

Objective: The prediction of RR intervals in hypertensive patients can help clinicians to analyze and warn patients' heart condition. Methods: Using 8 patients' data as samples, the RR intervals of patients were predicted by long short-term memory network (LSTM) and gradient lift tree (XGBoost), and the prediction results of the two models were combined by the inverse variance method to overcome the disadvantage of single model prediction. Results: Compared with the single model, the proposed combined model had a different degree of improvement in the prediction of RR intervals in 8 patients. Conclusion: LSTM-XGBoost model provides a method for predicting RR intervals in hypertensive patients, which has potential clinical feasibility.

[Free trajectory cone beam computed tomography reconstruction method for synchronous scanning of geometric calibration phantom and imaging object].

In order to suppress the geometrical artifacts caused by random jitter in ray source scanning, and to achieve flexible ray source scanning trajectory and meet the requirements of task-driven scanning imaging, a method of free trajectory cone-beam computed tomography (CBCT) reconstruction is proposed in this paper. This method proposed a geometric calibration method of two-dimensional plane. Based on this method, the geometric calibration phantom and the imaging object could be simultaneously imaged. Then, the geometric parameters could be obtained by online calibration method, and then combined with the geometric parameters, the alternating direction multiplier method (ADMM) was used for image iterative reconstruction. Experimental results showed that this method obtained high quality reconstruction image with high contrast and clear feature edge. The root mean square errors (RMSE) of the simulation results were rather small, and the structural similarity (SSIM) values were all above 0.99. The experimental results showed that it had lower image information entropy (IE) and higher contrast noise ratio (CNR). This method provides some practical value for CBCT to realize trajectory freedom and obtain high quality reconstructed image.

[A fast iterative reconstruction algorithm of sparse angular CT based on the selective total variation].

Aiming at the problem of high-quality image reconstruction from projection data at sparse angular views, we proposed an improved fast iterative reconstruction algorithm based on the minimization of selective image total variation (TV). The new reconstruction scheme consists of two components. Firstly, the algebraic reconstruction technique (ART) algorithm was adopted to reconstruct image that met the identity and non-negativity of projection data, and then, secondly, the selective TV minimization was used to modify the above image. Two phases were alternated until it met the convergence criteria. In order to further speed up the convergence of the algorithm, we applied a fast convergence technology in the iterative process. Experiments on simulated Shepp-Logan phantom were carried out. The results demonstrated that the new method not only improved image reconstruction quality and protected the edge of the image characteristics, but also improved the convergence speed of the iterative reconstruction significantly.

Characteristics and mechanism of local scour reduction around spur dike using the collar in clear water.

To reduce the local scour around the spur dike, the U-shaped collar is proposed in this study. The influence of the collar's length, width, and porosity on the local scour reduction in clear water is studied by model tests and numerical simulations. Experimental studies show that the collar has a significant effect on reducing the local scour. The location of the maximum scour depth of the spur dike moves downstream. The width of the collar has the greatest impact on the reduction effect among the three selected factors, followed by the porosity and the length. Local scour reduction efficiency of the collar can reach 56.9%. Based on the regression analysis of the range and variety, a formula for predicting the reduction effect around the spur dike is put forward, and the deviation between the values by formula and that in experiments are within ± 4%. The characteristics of the flow field around the spur dike under constant conditions with a collar are studied via numerical simulation. The numerical simulation results show that compared to the case without collar, the flow velocities around the spur dike in cases with permeable collar and solid collar reduced by 45% and 25%, respectively, and the shear stresses reduced by 20% and 28.6%, respectively. The results of this study can provide a reference for local scour reduction using the solid collar or collar made of permeable materials such as gabions.

Radiomics predict the WHO/ISUP nuclear grade and survival in clear cell renal cell carcinoma.

OBJECTIVES: This study aimed to assess the predictive value of radiomics derived from intratumoral and peritumoral regions and to develop a radiomics nomogram to predict preoperative nuclear grade and overall survival (OS) in patients with clear cell renal cell carcinoma (ccRCC). METHODS: The study included 395 patients with ccRCC from our institution. The patients in Center A (anonymous) institution were randomly divided into a training cohort (n = 284) and an internal validation cohort (n = 71). An external validation cohort comprising 40 patients from Center B also was included. Computed tomography (CT) radiomics features were extracted from the internal area of the tumor (IAT) and IAT combined peritumoral areas of the tumor at 3 mm (PAT 3 mm) and 5 mm (PAT 5 mm). Independent predictors from both clinical and radiomics scores (Radscore) were used to construct a radiomics nomogram. Kaplan-Meier analysis with a log-rank test was performed to evaluate the correlation between factors and OS. RESULTS: The PAT 5-mm radiomics model (RM) exhibited exceptional predictive capability for grading, achieving an area under the curves of 0.80, 0.80, and 0.90 in the training, internal validation, and external validation cohorts. The nomogram and RM gained from the PAT 5-mm region were more clinically useful than the clinical model. The association between OS and predicted nuclear grade derived from the PAT 5-mm Radscore and the nomogram-predicted score was statistically significant (p < 0.05). CONCLUSION: The CT-based radiomics and nomograms showed valuable predictive capabilities for the World Health Organization/International Society of Urological Pathology grade and OS in patients with ccRCC. CRITICAL RELEVANCE STATEMENT: The intratumoral and peritumoral radiomics are feasible and promising to predict nuclear grade and overall survival in patients with clear cell renal cell carcinoma, which can contribute to the development of personalized preoperative treatment strategies. KEY POINTS: The multi-regional radiomics features are associated with clear cell renal cell carcinoma (ccRCC) grading and prognosis. The combination of intratumoral and peritumoral 5 mm regional features demonstrated superior predictive performance for grading. The nomogram and radiomics models have a broad range of clinical applications.

Technical note: A GPU-based shared Monte Carlo method for fast photon transport in multi-energy x-ray exposures.

BACKGROUND: The Monte Carlo (MC) method is an accurate technique for particle transport calculation due to the precise modeling of physical interactions. Nevertheless, the MC method still suffers from the problem of expensive computational cost, even with graphics processing unit (GPU) acceleration. Our previous works have investigated the acceleration strategies of photon transport simulation for single-energy CT. But for multi-energy CT, conventional individual simulation leads to unnecessary redundant calculation, consuming more time. PURPOSE: This work proposes a novel GPU-based shared MC scheme (gSMC) to reduce unnecessary repeated simulations of similar photons between different spectra, thereby enhancing the efficiency of scatter estimation in multi-energy x-ray exposures. METHODS: The shared MC method selects shared photons between different spectra using two strategies. Specifically, we introduce spectral region classification strategy to select photons with the same initial energy from different spectra, thus generating energy-shared photon groups. Subsequently, the multi-directional sampling strategy is utilized to select energy-and-direction-shared photons, which have the same initial direction, from energy-shared photon groups. Energy-and-direction-shared photons perform shared simulations, while others are simulated individually. Finally, all results are integrated to obtain scatter distribution estimations for different spectral cases. RESULTS: The efficiency and accuracy of the proposed gSMC are evaluated on the digital phantom and clinical case. The experimental results demonstrate that gSMC can speed up the simulation in the digital case by ∼37.8% and the one in the clinical case by ∼20.6%, while keeping the differences in total scatter results within 0.09%, compared to the conventional MC package, which performs an individual simulation. CONCLUSIONS: The proposed GPU-based shared MC simulation method can achieve fast photon transport calculation for multi-energy x-ray exposures.

[ART reconstruction from few views using bilateral-filtering iterative method].

An algebraic image reconstruction from few views using bilateral-filtering iterative method was proposed due to the problem of computed tomography insufficient data in the present study. In each iteration reconstruction, we first used algebraic reconstruction technique (ART) algorithm to reconstruct an image, ensuring the non-negativity of the reconstructed image at the same time, and then performed bilateral-filtering to the above-mentioned image. In order to improve reconstructed image quality and accelerate the convergence speed, we developed a modified bilateral-filtering method. Shepp-Logan simulation experiments and real CT projection data reconstructions showed the feasibility of the algorithm. The results showed that, compared with the traditional methods of filtered back projection (FBP), ART and GF-ART,the proposed method has a higher signal-to-noise ratio, and maintains more effectively the image edge information.

Analysis of the response subsidence and grouting treatment in the goaf of multi-layer inclined coal seam.

Based on existing researches, field drillings and numerical simulations are carried out in this paper to analyze the problems of subsidence control in the goaf of multi-layer inclined coal seam. Midas/GTS NX is used to build a three-dimensional calculation model of the goaf. A new method of using borehole data to check simulation parameters is proposed. The whole process of goaf excavation, construction of roadbed (pile foundation) and grouting treatment is analyzed. Analysis theory of different subgrade construction schemes and grouting treatment process on goaf is established. Response characteristics of displacement and equivalent stress and strain of goaf in multilayer inclined coal seam are obtained. A new method for analyzing the characteristics of the stress and deformation of the rock strata before and after grouting in the goaf under the conditions of different foundation schemes on the surface is provided in this research.

Scatter correction based on adaptive photon path-based Monte Carlo simulation method in Multi-GPU platform.

Monte Carlo (MC)-based simulation is the most precise method in scatter correction for Cone-beam CT (CBCT). Nonetheless, the existing MC methods cannot be fully applied in clinical due to its low efficiency. The traditional MC simulations perform calculations via a particle-by-particle scheme, which leads to high computation costs because abundant photons do not reach the X-ray detector in transport. The conventional approaches cannot control where the particle ends. Hence, it unavoidably waste lots of time in transporting numerous photons that have no contribution to the signal at the detector, yielding a low computational efficiency. To solve the problem, an innovative GPU-based Metropolis MC (gMMC) method was proposed. Compared with the traditional ones, the Metropolis based algorithm utilizes a path-by-path sampling method. The method can automatically control each particle path and eventually accelerate the convergence. In this paper, we firstly take planning CT image as prior information because of its precise CT value, and utilize gMMC to estimate scatter signal. Then the scatter signals are removed from the raw CBCT projections. Afterwards, FDK reconstruction is performed to obtain the corrected image,some accelerating strategies including reducing photon history number, pixels sampling, projection angles sampling and reconstructed image down-sampling achieve adaptive fast CBCT image reconstruction. For having high computational efficiency, we implemented the whole workflow on a 4-GPU workstation. In order to verify the feasibility of the the method, the experiment of several cases are conducted including simulation, phantom, and real patient cases. Results indicate that the image contrast becomes better, the scatter artifacts are eliminated. The maximum error (emax), the minimum error (emin), the 95th percentile error (e95%), average error (¯e) are reduced from 264, 56, 14 and 21 HU to 28, 10, 3 and 7 HU in full-fan case, and from 387, 5, 19 and 95 HU to 39, 2, 2 and 6 HU in the half-fan case. In terms of computation time, the MC simulation time of all cases is within 2.5 seconds, and the total time is within 15 seconds.

Patch Based Grid Artifact Suppressing in Digital Mammography.

The mammography is the first choice of breast cancer screening, which has proven to be the most effective screening method. An antiscatter grid is usually employed to enhance the contrast of image by absorbing unexpected scattered signals. However, the grid pattern casts shadows and grid artifacts, which severely degrade the image quality. To solve the problem, we propose the patch based frequency signal filtering for fast grid artifacts suppressing. As opposed to whole image processing synchronously, the proposed method divides image into a number of blocks for tuning filter simultaneously, which reduces the frequency interference among image blocks and saves computation time by multithread processing. Moreover, for mitigating grid artifacts more precisely, characteristic peak detection is employed in each block automatically, which can accurately identify the location of the antiscatter grid and its motion pattern. Qualitative and quantitative studies were performed on simulation and real machine data to validate the proposed method. The results show great potential for fast suppressing grid artifacts and generating high quality of digital mammography.

John's Equation-based Consistency Condition and Corrupted Projection Restoration in Circular Trajectory Cone Beam CT.

In transmitted X-ray tomography imaging, the acquired projections may be corrupted for various reasons, such as defective detector cells and beam-stop array scatter correction problems. In this study, we derive a consistency condition for cone-beam projections and propose a method to restore lost data in corrupted projections. In particular, the relationship of the geometry parameters in circular trajectory cone-beam computed tomography (CBCT) is utilized to convert an ultra-hyperbolic partial differential equation (PDE) into a second-order PDE. The second-order PDE is then transformed into a first-order ordinary differential equation in the frequency domain. The left side of the equation for the newly derived consistency condition is the projection derivative of the current and adjacent views, whereas the right side is the projection derivative of the geometry parameters. A projection restoration method is established based on the newly derived equation to restore corrupted data in projections in circular trajectory CBCT. The proposed method is tested in beam-stop array scatter correction, metal artifact reduction, and abnormal pixel correction cases to evaluate the performance of the consistency condition and corrupted projection restoration method. Qualitative and quantitative results demonstrate that the present method has considerable potential in restoring lost data in corrupted projections.

Simultaneous calibration phantom commission and geometry calibration in cone beam CT.

Geometry calibration is a vital step for describing the geometry of a cone beam computed tomography (CBCT) system and is a prerequisite for CBCT reconstruction. In current methods, calibration phantom commission and geometry calibration are divided into two independent tasks. Small errors in ball-bearing (BB) positioning in the phantom-making step will severely degrade the quality of phantom calibration. To solve this problem, we propose an integrated method to simultaneously realize geometry phantom commission and geometry calibration. Instead of assuming the accuracy of the geometry phantom, the integrated method considers BB centers in the phantom as an optimized parameter in the workflow. Specifically, an evaluation phantom and the corresponding evaluation contrast index are used to evaluate geometry artifacts for optimizing the BB coordinates in the geometry phantom. After utilizing particle swarm optimization, the CBCT geometry and BB coordinates in the geometry phantom are calibrated accurately and are then directly used for the next geometry calibration task in other CBCT systems. To evaluate the proposed method, both qualitative and quantitative studies were performed on simulated and realistic CBCT data. The spatial resolution of reconstructed images using dental CBCT can reach up to 15 line pair cm-1. The proposed method is also superior to the Wiesent method in experiments. This paper shows that the proposed method is attractive for simultaneous and accurate geometry phantom commission and geometry calibration.

Iterative image reconstruction using modified non-local means filtering for limited-angle computed tomography.

PURPOSE: Limited-angle CT imaging is an effective technique to reduce radiation. However, existing image reconstruction methods can effectively reduce streak artifacts but fail to suppress those artifacts around edges due to incomplete projection data. Thus, a modified NLM (mNLM) based reconstruction method is proposed. METHODS: Since the artifacts around edges mainly exist in local position, it is possible to restore the true pixels in artifacts using pixels located in artifacts-free regions. In each iteration, mNLM is performed on image reconstructed by ART followed by positivity constraint. To solve the problem caused by ART-mNLM that there is undesirable information that may appear in the image, ART-TV is then utilized in the following iterative process after ART-mNLM iterates for a number of iterations. The proposed algorithm is named as ART-mNLM/TV. RESULTS: Simulation experiments are performed to validate the feasibility of algorithm. When the scanning range is [0, 150°], our algorithm outperforms the ART-NLM and ART-TV with more than 40% and 29% improvement in terms of SNR and with more than 58% and 49% reduction in terms of MAE. Consistently, reconstructed images from real projection data also demonstrate the effectiveness of presented algorithm. CONCLUSION: This paper uses mNLM which benefits from redundancy of information across the whole image, to recover the true value of pixels in artifacts region by utilizing pixels from artifact-free regions, and artifacts around the edges can be mitigated effectively. Experiments show that the proposed ART-mNLM/TV is able to achieve better performances compared to traditional methods.

Few-view CT reconstruction via a novel non-local means algorithm.

PURPOSE: Non-local means (NLM) based reconstruction method is a promising algorithm for few-view computed tomography (CT) reconstruction, but often suffers from over-smoothed image edges. To address this problem, an adaptive NLM reconstruction method based on rotational invariance (ART-RIANLM) is proposed. METHODS: The method consists of four steps: 1) Initializing parameters; 2) ART reconstruction using raw data; 3) Positivity constraint of the reconstructed image; 4) Image updating by RIANLM filtering. In RIANLM, two kinds of rotational invariance measures which are average gradient (AG) and region homogeneity (RH) are proposed to calculate the distance between two patches and a novel NLM filter is developed to avoid over-smoothed image. Moreover, the parameter h in RIANLM which controls the decay of the weights is adaptive to avoid over-smoothness, while it is constant in NLM during the whole reconstruction process. The proposed method is validated on two digital phantoms and real projection data. RESULTS: In our experiments, the searching neighborhood size is set as 15×15 and the similarity window is set as 3×3. For the simulated case of Shepp-Logan phantom, ART-RIANLM produces higher SNR (36.23dB>24.00dB) and lower MAE (0.0006<0.0024) reconstructed images than ART-NLM. The visual inspection demonstrated that the proposed method could suppress artifacts or noises more effectively and recover image edges better. The result of real data case is also consistent with the simulation result. CONCLUSIONS: A RIANLM based reconstruction method for few-view CT is presented. Compared to the traditional ART-NLM method, SNR and MAE from ART-RIANLM increases 51% and decreases 75%, respectively.

Iterative Image Reconstruction for Limited-Angle CT Using Optimized Initial Image.

Limited-angle computed tomography (CT) has great impact in some clinical applications. Existing iterative reconstruction algorithms could not reconstruct high-quality images, leading to severe artifacts nearby edges. Optimal selection of initial image would influence the iterative reconstruction performance but has not been studied deeply yet. In this work, we proposed to generate optimized initial image followed by total variation (TV) based iterative reconstruction considering the feature of image symmetry. The simulated data and real data reconstruction results indicate that the proposed method effectively removes the artifacts nearby edges.

Stromal fibroblasts derived from mammary gland of bovine with mastitis display inflammation-specific changes.

Fibroblasts are predominant components of mammary stromal cells and play crucial roles in the development and involution of bovine mammary gland; however, whether these cells contribute to mastitis has not been demonstrated. Thus, we have undertaken biological and molecular characterization of inflammation-associated fibroblasts (INFs) extracted from bovine mammary glands with clinical mastitis and normal fibroblasts (NFs) from slaughtered dairy cows because of fractured legs during lactation. The functional contributions of INFs to normal epithelial cells were also investigated by using an in vitro co-culture model. We present evidence that the INFs were activated fibroblasts and showed inflammation-related features. Moreover, INFs significantly inhibited the proliferation and ß-casein secretion of epithelial cells, as well as upregulated the expression of tumor necrosis factor-α and interleukin-8 in epithelial cells. These findings indicate that functional alterations can occur in stromal fibroblasts within the bovine mammary gland during mastitis, demonstrating the importance of stromal fibroblasts in bovine mastitis and its treatment.

CT Image Reconstruction from Sparse Projections Using Adaptive TpV Regularization.

Radiation dose reduction without losing CT image quality has been an increasing concern. Reducing the number of X-ray projections to reconstruct CT images, which is also called sparse-projection reconstruction, can potentially avoid excessive dose delivered to patients in CT examination. To overcome the disadvantages of total variation (TV) minimization method, in this work we introduce a novel adaptive TpV regularization into sparse-projection image reconstruction and use FISTA technique to accelerate iterative convergence. The numerical experiments demonstrate that the proposed method suppresses noise and artifacts more efficiently, and preserves structure information better than other existing reconstruction methods.

Sparse-view computed tomography image reconstruction via a combination of L(1) and SL(0) regularization.

Low-dose computed tomography reconstruction is an important issue in the medical imaging domain. Sparse-view has been widely studied as a potential strategy. Compressed sensing (CS) method has shown great potential to reconstruct high-quality CT images from sparse-view projection data. Nonetheless, low-contrast structures tend to be blurred by the total variation (TV, L1-norm of the gradient image) regularization. Moreover, TV will produce blocky effects on smooth and edge regions. To overcome this limitation, this study has proposed an iterative image reconstruction algorithm by combining L1 regularization and smoothed L0 (SL0) regularization. SL0 is a smooth approximation of L0 norm and can solve the problem of L0 norm being sensitive to noise. To evaluate the proposed method, both qualitative and quantitative studies were conducted on a digital Shepp-Logan phantom and a real head phantom. Experimental comparative results have indicated that the proposed L1/SL0-POCS algorithm can effectively suppress noise and artifacts, as well as preserve more structural information compared to other existing methods.

Regional Risk Evaluation of Flood Disasters for the Trunk-Highway in Shaanxi, China.

Due to the complicated environment there are various types of highway disasters in Shaanxi Province (China). The damages caused are severe, losses are heavy, and have rapidly increased over the years, especially those caused by flood disasters along the rivers in mountainous areas. Therefore, research on risk evaluations, which play important roles in the prevention and mitigation of highway disasters are very important. An evaluation model was established based on the superposition theory of regional influencing factors to highway flood disasters. Based on the formation mechanism and influencing factors of highway flood disasters, the main influencing factors were selected. These factors include rainstorms, terrain slopes, soil types, vegetation coverage and regional river density, which are based on evaluation indexes from climate conditions and underlying surface of the basin. A regional risk evaluation of highway flood disasters in Shaanxi was established using GIS. The risk index was divided into five levels using statistical methods, in accordance with the regional characteristics of highway flood disasters. Considering the difference in upfront investments, road grade, etc, between expressways and trunk-highways in China, a regional risk evaluation of trunk-highway flood disasters was completed. The evaluation results indicate that the risk evaluation is consistent with the actual situation.