Journal of X-Ray Science and Technology最新文献

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.

Background and objectiveCOVID-19 is considered as the biggest global health disaster in the 21st century, and it has a huge impact on the world.MethodsThis paper publishes a publicly available dataset of CT images of multiple types of pneumonia (COVID-19CT+). Specifically, the dataset contains 409,619 CT images of 1333 patients, with subset-A containing 312 community-acquired pneumonia cases and subset-B containing 1021 COVID-19 cases. In order to demonstrate that there are differences in the methods used to classify COVID-19CT+ images across time, we selected 13 classical machine learning classifiers and 5 deep learning classifiers to test the image classification task.ResultsIn this study, two sets of experiments are conducted using traditional machine learning and deep learning methods, the first set of experiments is the classification of COVID-19 in Subset-B versus COVID-19 white lung disease, and the second set of experiments is the classification of community-acquired pneumonia in Subset-A versus COVID-19 in Subset-B, demonstrating that the different periods of the methods differed on COVID-19CT+. On the first set of experiments, the accuracy of traditional machine learning reaches a maximum of 97.3% and a minimum of only 62.6%. Deep learning algorithms reaches a maximum of 97.9% and a minimum of 85.7%. On the second set of experiments, traditional machine learning reaches a high of 94.6% accuracy and a low of 56.8%. The deep learning algorithm reaches a high of 91.9% and a low of 86.3%.ConclusionsThe COVID-19CT+ in this study covers a large number of CT images of patients with COVID-19 and community-acquired pneumonia and is one of the largest datasets available. We expect that this dataset will attract more researchers to participate in exploring new automated diagnostic algorithms to contribute to the improvement of the diagnostic accuracy and efficiency of COVID-19.

Accurate segmentation of lung regions from CT scan images is critical for diagnosing and monitoring respiratory diseases. This study introduces a novel hybrid architecture Adaptive Attention U-NetAA, which combines the strengths of U-Net3 + and Transformer based attention mechanisms models for high-precision lung segmentation. The U-Net3 + module effectively segments the lung region by leveraging its deep convolutional network with nested skip connections, ensuring rich multi-scale feature extraction. A key innovation is introducing an adaptive attention mechanism within the Transformer module, which dynamically adjusts the focus on critical regions in the image based on local and global contextual relationships. This model's adaptive attention mechanism addresses variations in lung morphology, image artifacts, and low-contrast regions, leading to improved segmentation accuracy. The combined convolutional and attention-based architecture enhances robustness and precision. Experimental results on benchmark CT datasets demonstrate that the proposed model achieves an IoU of 0.984, a Dice coefficient of 0.989, a MIoU of 0.972, and an HD95 of 1.22 mm, surpassing state-of-the-art methods. These results establish U-NetAA as a superior tool for clinical lung segmentation, with enhanced accuracy, sensitivity, and generalization capability.

BackgroundLimited-angle Computed Tomography imaging suffers from severe artifacts in the reconstructed image due to incomplete projection data. Deep learning methods have been developed currently to address the challenges of robustness and low contrast of the limited-angle CT reconstruction with a relatively effective way.ObjectiveTo improve the low contrast of the current limited-angle CT reconstruction image, enhance the robustness of the reconstruction method and the contrast of the limited-angle image.MethodIn this paper, we proposed a limited-angle CT reconstruction method that combining the Fourier domain reweighting and wavelet domain enhancement, which fused information from different domains, thereby getting high-resolution reconstruction images.ResultsWe verified the feasibility and effectiveness of the proposed solution through experiments, and the reconstruction results are improved compared with the state-of-the-art methods.ConclusionsThe proposed method enhances some features of the original image domain data from different domains, which is beneficial to the reasonable diffusion and restoration of diffuse detail texture features.

X-ray ionizing radiation from Computed Tomography (CT) scanning increases cancer risk for patients, thus making sparse view CT, which diminishes X-ray exposure by lowering the number of projections, highly significant in diagnostic imaging. However, reducing the number of projections inherently degrades image quality, negatively impacting clinical diagnosis. Consequently, attaining reconstructed images that meet diagnostic imaging criteria for sparse view CT is challenging. This paper presents a novel network (CSUF), specifically designed for ultra-sparse view lung CT image reconstruction. The CSUF network consists of three cohesive components including (1) a compressed sensing-based CT image reconstruction module (VdCS module), (2) a U-shaped end-to-end network, CT-RDNet, enhanced with a self-attention mechanism, acting as the generator in a Generative Adversarial Network (GAN) for CT image restoration and denoising, and (3) a feedback loop. The VdCS module enriches CT-RDNet with enhanced features, while CT-RDNet supplies the VdCS module with prior images infused with rich details and minimized artifacts, facilitated by the feedback loop. Engineering simulation experimental results demonstrate the robustness of the CSUF network and its potential to deliver lung CT images with diagnostic imaging quality even under ultra-sparse view conditions.