Journal of X-Ray Science and Technology最新文献

In recent years Covid-19 impact is causing unprecedented difficulties worldwide, affecting lifestyle choices. The post-pandemic era has made this even more critical.COVID-19 triggers widespread inflammation throughout the body, potentially causing damage to the heart and other vital organs. Mortality data from COVID-19 clearly show that the highest death rates occur in individuals with chronic conditions, such as diabetes, pneumonia, cardiovascular disease (CVD), and acute renal failure.CVD is a particular concern in the medical field. The early detection of CVD remains a significant challenge, as early identification can prompt lifestyle changes and ensure appropriate medical interventions when needed. Individuals with CVD are at an increased risk for heart attack and other serious complications. There is a limited amount of data available to study the effects of COVID-19 on CVD in COVID-19 patients. However, it is essential to monitor these patients to ensure full recovery without complications. The proposed system is specifically designed for individuals experiencing prolonged symptoms following a COVID-19 infection, commonly referred to as long COVID patients. This research introduces a novel Decision-Making System for CVD Prediction, utilizing an improved dual-attention residual bi-directional gated recurrent neural network unit (DA-ResBiGRU) algorithm with AI-Biruni Earth Radius Optimization (ABER). The proposed system employs state-of-the-art predictive algorithms and real-time monitoring to assess individual patient risk profiles accurately. This research addresses the critical need for personalized risk assessment in patients with long-term COVID, aiming to assist healthcare providers in timely and targeted interventions. By analyzing intricate patterns in patient data, the decision-making system enhances the precision of CVD prediction. Additionally, the system's adaptive nature allows it to continuously learn from new patient data, ensuring that its predictions remain up-to-date and reflective of the evolving understanding of long COVID-related cardiovascular risks. The simulation findings of this research highlight the potential of the proposed algorithm to be integrated into clinical decision-making, helping healthcare professionals identify high-risk patients more effectively. The proposed method outperformed existing algorithms, such as Deep Neural Network (DNN), Long short-term memory (LSTM), Inception-v3, Xception, and MobileNetV2, achieving the highest accuracy (97.88%), sensitivity (95.50%), specificity (94.29%), precision (96.68%), and F-measure (95.85%).

Objective: Computed tomography (CT) is a widely used medical imaging modality, but its radiation exposure poses potential risks to human health. Sparse-view scanning has emerged as an effective approach to reduce radiation dose; however, images reconstructed using the filtered back-projection (FBP) algorithm from sparse-view projections often suffer from severe streak artifacts. Achieving high-quality CT image reconstructed from sparse-view projections remains a challenging task.

Methods: Building on compressed sensing (CS), the total variation (TV) algorithm is applied for high-quality sparse-view reconstruction. We further propose a relative total variation (RTV) algorithm to enhance the accuracy of sparse-view reconstruction. Experimental results indicate that while the RTV algorithm improves accuracy, it has limitations in edge preservation. To address this, inspired by the success of directional TV (DTV) in limited-angle reconstruction, we develop a directional relative TV (DRTV) model. This model applies the RTV technique in both x and y directions independently, and we derive its adaptive steepest descent projection onto convex set (ASD-POCS) solution algorithm.

Results: Experiments conducted on simulated phantoms and real CT images demonstrate the correctness, convergence, and superior performance of the DRTV algorithm in sparse-view reconstruction. Compared with the TV, DTV, and RTV algorithm, the DRTV algorithm exhibits superior preservation of structural features and texture details.

Significance: The DRTV algorithm represents an advanced method for high-precision sparse-view CT reconstruction, providing stable and accurate results. Moreover, the approach is applicable to other medical imaging modalities.

Background: Low-dose computed tomography (LDCT) effectively reduces the risk of malignant disease; however, reducing the radiation dose introduces additional noise and stripe artifacts in the CT imaging process. While Convolutional Neural Networks (CNN) have demonstrated performance advantages in LDCT imaging tasks, their end-to-end network architecture limits adaptability to CT reconstruction tasks, leaving room for further performance improvement.

Objective: To propose a low-dose CT reconstruction network based on the iterative algorithms, incorporating an interpretable network architecture to achieve superior reconstruction performance.

Methods: To better adapt to CT reconstruction tasks, we proposed an interpretable deep unfolding network leveraging time-frequency and image domain priors to fully exploit the features extracted in the transform domain. The iterative optimization process of the proposed algorithm is mapped into a deep unfolding network, and a Stage Information Memory Network (SIMN) is designed to address information loss between adjacent stages and within each stage.

Results: Experimental results on Mayo and Piglet datasets show that the proposed model outperforms state-of-the-art techniques in both quantitative metrics and visual quality.

Conclusions: The proposed network effectively removes artifacts and noise from low-dose CT images, achieving excellent reconstruction performance.

ObjectiveThe purpose of this study is to perform multiple ($\ge 3$) material decomposition with deep learning method for spectral cone-beam CT (CBCT) imaging based on ultra-slow kV switching.ApproachIn this work, a novel deep neural network called SkV-Net is developed to reconstruct multiple material density images from the ultra-sparse spectral CBCT projections acquired using the ultra-slow kV switching technique. In particular, the SkV-Net has a backbone structure of U-Net, and a multi-head axial attention module is adopted to enlarge the perceptual field. It takes the CT images reconstructed from each kV as input, and output the basis material images automatically based on their energy-dependent attenuation characteristics. Numerical simulations and experimental studies are carried out to evaluate the performance of this new approach.Main ResultsIt is demonstrated that the SkV-Net is able to generate four different material density images, i.e., fat, muscle, bone and iodine, from five spans of kV switched spectral projections. Physical experiments show that the decomposition errors of iodine and CaCl${}_2$ are less than 6$\%$, indicating high precision of this novel approach in distinguishing materials.SignificanceSkV-Net provides a promising multi-material decomposition approach for spectral CBCT imaging systems implemented with the ultra-slow kV switching scheme.

Background: Segmentation of coronary arteries in Coronary Computed Tomography Angiography (CCTA) images is crucial for diagnosing coronary artery disease (CAD), but remains challenging due to small artery size, uneven contrast distribution, and issues like over-segmentation or omission.

Objective: The aim of this study is to improve coronary artery segmentation in CCTA images using both conventional and deep learning techniques.

Methods: We propose MHASegNet, a lightweight network for coronary artery segmentation, combined with a tailored refinement method. MHASegNet employs multi-scale hybrid attention to capture global and local features, and integrates a 3D context anchor attention module to focus on key coronary artery structures while suppressing background noise. An iterative, region-growth-based refinement addresses crown breaks and reduces false alarms. We evaluated the method on an in-house dataset of 90 subjects and two public datasets with 1060 subjects.

Results: MHASegNet, coupled with tailored refinement, outperforms state-of-the-art algorithms, achieving a Dice Similarity Coefficient (DSC) of 0.867 on the in-house dataset, 0.875 on the ASOCA dataset, and 0.827 on the ImageCAS dataset.

Conclusion: The tailored refinement significantly reduces false positives and resolves most discontinuities, even for other networks. MHASegNet and the tailored refinement may aid in diagnosing and quantifying CAD following further validation.

In image reconstruction and processing, incorporating prior information, particularly the nonnegativity of pixel values, is essential. Existing computed tomography (CT) iterative reconstruction algorithms, including the algebraic reconstruction technique (ART), simultaneous ART (SART), and the simultaneous iterative reconstruction technique (SIRT), typically address negative components during the iteration process by either setting them to zero, introducing regularization terms to prevent negativity, or leaving them unchanged. This paper establishes a general framework in which enforcing the nonnegativity prior accelerates the convergence of the reconstructed image toward the true solution. Within this framework, we propose two efficient and simple acceleration techniques: setting negative pixel values to their absolute values and updating them to the estimated values from the previous update. Experiments were conducted using ART, SIRT, and SART algorithms, integrated with the corresponding acceleration techniques, on full-angle, limited-angle, and noisy simulated data, as well as real data. The results validate the effectiveness of the proposed acceleration methods by evaluating image quality using the PSNR and SSIM metrics. Notably, the proposed technique that sets negative pixel values to their absolute values is strongly recommended, as it significantly outperforms the existing technique that sets them to zero, both in terms of image quality and iteration time.

BackgroundOut-of-plane artifacts in digital breast tomosynthesis (DBT) can affect image quality, even subtly, and are influenced by the size and z-position of features with contrast of clinical images.ObjectiveTo propose a phantom and metric to further characterize out-of-plane artifacts in DBT.MethodsPhantoms with a signal inserted were manufactured, and the reconstructed planes were obtained using the DBT system. Normalized maximum contrast within the plane area was used to quantitatively evaluate out-of-plane artifacts. The spread of out-of-plane artifacts within the reconstructed plane was qualitatively evaluated by observing the profile within the plane area.ResultsThe larger the signal diameter, the stronger the effect of out-of-plane artifacts on the z-position far from the in-focus plane. When the z-position of the signal was on the upper side of the z-position of the center of X-ray tube rotation, out-of-plane artifacts were stronger on the upper side and weaker on the lower side of the signal. The spread of out-of-plane artifacts in the off-focus plane changed from monomodal to bimodal, with movement away from the signal's location in the z-direction.ConclusionsThis work proposes new phantoms and analysis methods to investigate the characteristics of out-of-plane artifacts, supplementing conventional methods.

Purposes:  Suppressing noise can effectively promote image quality and save radiation dose in clinical imaging with x-ray computed tomography (CT). To date, numerous statistical noise reduction approaches have ever been proposed in image domain, projection domain or both domains. Especially, a multiscale decomposition strategy can be exploited to enhance the performance of noise suppression while preserving image sharpness. Recognizing the inherent advantage of noise suppression in the projection domain, we have previously proposed a projection domain multiscale penalized weighted least squares (PWLS) method for fan-beam CT imaging, wherein the sampling intervals are explicitly taken into account for the possible variation of sampling rates. In this work, we extend our previous method into cone-beam (CB) CT imaging, which is more relevant to practical imaging applications.

Methods:  The projection domain multiscale PWLS method is derived for CBCT imaging by converting an isotropic diffusion partial differential equation (PDE) in the three-dimensional (3D) image domain into its counterpart in the CB projection domain. With adoption of the Markov random field (MRF) objective function, the CB projection domain multiscale PWLS method suppresses noise at each scale. The performance of the proposed method for statistical noise reduction in CBCT imaging is experimentally evaluated and verified using the projection data acquired by an actual micro-CT scanner.

Results:  The preliminary result shows that the proposed CB projection domain multiscale PWLS method outperforms the CB projection domain single-scale PWLS, the 3D image domain discriminative feature representation (DFR), and the 3D image domain multiscale nonlinear diffusion methods in noise reduction. Moreover, the proposed method can preserve image sharpness effectively while avoiding generation of novel artifacts.

Conclusions:  Since the sampling intervals are explicitly taken into account in the projection domain multiscale decomposition, the proposed method would be beneficial to advanced applications where the CBCT imaging is employed and the sampling rates vary.

Accurate X-ray Computed tomography (CT) image segmentation of the abdominal organs is fundamental for diagnosing abdominal diseases, planning cancer treatment, and formulating radiotherapy strategies. However, the existing deep learning based models for three-dimensional (3D) CT image abdominal multi-organ segmentation face challenges, including complex organ distribution, scarcity of labeled data, and diversity of organ structures, leading to difficulties in model training and convergence and low segmentation accuracy. To address these issues, a novel multi-stage training and a deep supervision model based segmentation approach is proposed. It primary integrates multi-stage training, pseudo- labeling technique, and a developed deep supervision model with attention mechanism (DLAU-Net), specifically designed for 3D abdominal multi-organ segmentation. The DLAU-Net enhances segmentation performance and model adaptability through an improved network architecture. The multi-stage training strategy accelerates model convergence and enhances generalizability, effectively addressing the diversity of abdominal organ structures. The introduction of pseudo-labeling training alleviates the bottleneck of labeled data scarcity and further improves the model's generalization performance and training efficiency. Experiments were conducted on a large dataset provided by the FLARE 2023 Challenge. Comprehensive ablation studies and comparative experiments were conducted to validate the effectiveness of the proposed method. Our method achieves an average organ accuracy (AVG) of 90.5% and a Dice Similarity Coefficient (DSC) of 89.05% and exhibits exceptional performance in terms of training speed and handling data diversity, particularly in the segmentation tasks of critical abdominal organs such as the liver, spleen, and kidneys, significantly outperforming existing comparative methods.