Journal of X-Ray Science and Technology最新文献

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.

Dual-spectral computed tomography (DSCT) has found extensive application in medical and industrial imaging due to its superior capability to distinguish different materials. However, a significant challenge in DSCT lies in the presence of X-ray scatter, which degrades the quality of reconstructed images. Traditional DSCT reconstruction methods often neglect the impact of scatter, leading to inaccurate basis material decomposition, especially under severe scatter conditions. To address this limitation, this paper proposes an innovative iterative DSCT reconstruction algorithm based on the filtered back-projection (FBP) method. Specifically, we first refine the commonly used polychromatic attenuation model to more accurately account for the effects of scatter. Building on this improved model, we propose an iterative reconstruction approach combined with the FBP method, achieving high-quality DSCT reconstructions that effectively mitigate scatter artifacts and improve the accuracy of basis material decomposition. Experiments on both simulated phantoms and real data demonstrate the superior performance of the proposed method in DSCT reconstruction. Notably, our approach achieves outstanding basis material decomposition results without requiring additional pre or post-processing steps, making it particularly suitable for practical DSCT applications.

Background: Sparse-View X-ray 3D Foot Reconstruction aims to reconstruct the three-dimensional structure of the foot from sparse-view X-ray images, a challenging task due to data sparsity and limited viewpoints.

Objective: This paper presents a novel method using a multi-scale geometric Transformer to enhance reconstruction accuracy and detail representation.

Methods: Geometric position encoding technology and a window mechanism are introduced to divide X-ray images into local areas, finely capturing local features. A multi-scale Transformer module based on Neural Radiance Fields (NeRF) enhances the model's ability to express and capture details in complex structures. An adaptive weight learning strategy further optimizes the Transformer's feature extraction and long-range dependency modelling.

Results: Experimental results demonstrate that the proposed method significantly improves the reconstruction accuracy and detail preservation of the foot structure under sparse-view X-ray conditions. The multi-scale geometric Transformer effectively captures local and global features, leading to more accurate and detailed 3D reconstructions.

Conclusions: The proposed method advances medical image reconstruction, significantly improving the accuracy and detail preservation of 3D foot reconstructions from sparse-view X-ray images.

BackgroundPathological images play a crucial role in the diagnosis of critically ill cancer patients. Since cancer patients often seek medical assistance when their condition is severe, doctors face the urgent challenge of completing accurate diagnoses and developing surgical plans within a limited timeframe. The complexity and diversity of pathological images require a significant investment of time from specialized physicians for processing and analysis, which can lead to missing the optimal treatment window.PurposeCurrent medical decision support systems are challenged by the high computational complexity of deep learning models, which demand extensive data training, making it difficult to meet the real-time needs of emergency diagnostics.MethodThis study addresses the issue of emergency diagnosis for malignant bone tumors such as osteosarcoma by proposing a Lightened Boundary-enhanced Digital Pathological Image Recognition Strategy (LB-DPRS). This strategy optimizes the self-attention mechanism of the Transformer model and innovatively implements a boundary segmentation enhancement strategy, thereby improving the recognition accuracy of tissue backgrounds and nuclear boundaries. Additionally, this research introduces row-column attention methods to sparsify the attention matrix, reducing the computational burden of the model and enhancing recognition speed. Furthermore, the proposed complementary attention mechanism further assists convolutional layers in fully extracting detailed features from pathological images.ResultsThe DSC value of LB-DPRS strategy reached 0.862, the IOU value reached 0.749, and the params was only 10.97 M.ConclusionExperimental results demonstrate that the LB-DPRS strategy significantly improves computational efficiency while maintaining prediction accuracy and enhancing model interpretability, providing powerful and efficient support for the emergency diagnosis of malignant bone tumors such as osteosarcoma.

Investigating the state of two-phase flow in heat transfer pipes is crucial for ensuring reactor safety and enhancing operational efficiency. Current measurement methods fail to address the requirements for identifying flow patterns and void fractions in high-velocity two-phase flow within small-diameter alloy steel pipes. The laboratory proposes a method for measuring high-velocity two-phase flow utilizing stationary computed tomography (CT) and verifies its feasibility. Constrained by the overall physical arrangement of the system, the CT system can only gather under complete sparse projection data. We propose an unsupervised deep learning algorithm called Deep Radon Prior (DRP). This algorithm directly reconstructs images from projection data by optimizing errors in radon domain. It leverages the neural network's capacity to learn regular information inherent in the image, in conjunction with an iterative algorithmic approach. Experimental results demonstrate the algorithm's effectiveness in suppressing image artifacts and noise, yielding significantly improved reconstruction quality compared to the Filtered Back Projection (FBP) and Alternating Direction Method of Multiplier - Total Variation (ADMM-TV) algorithms. This enhancement enables the visualization of small bubbles with a diameter of 0.3 mm. The DRP algorithm has wider applicability in fluids with different patterns in pipe and is more suitable for measurements of actual bubble flows.

Background: Photon counting CT has demonstrated exceptional performance in spatial resolution, density resolution, and image quality, earning recognition as a groundbreaking technology in medical imaging. However, its technical implementation continues to face substantial challenges, including charge sharing effects.

Objective: To develop a spatio-energetic charge-sharing modulation model for accurate photon counting CT simulation (SmuSim). Specifically, SmuSim is built upon the previously developed photon counting toolkit (PcTK) and thoroughly incorporates the charge sharing effects that occur in photon counting CT.

Methods: The proposed SmuSim firstly enrolls three primary modules, i.e., photon transport, charge transport, and charge induction to characterize the charge sharing effects in the photon counting CT imaging chain. Then, Monte Carlo simulation is also conducted to validate the feasibility of the proposed SmuSim with well-built charge sharing effects model.

Results: Under diverse detector configurations, SmuSim's energy spectrum response curves exhibit a remarkable alignment with Monte Carlo simulations, in stark contrast to the Pctk results. In both digital and clinical phantom studies, SmuSim effectively simulates distorted photon counting CT images. In digital physical phantom simulations, the deviations in attenuation coefficient due to charge sharing effects are -49.70%, -19.66%, and -3.33% for the three energy bins, respectively. In digital clinical phantom simulations, the differences in attenuation coefficient are -19.92%, -4.98%, and -0.6%, respectively. In the two simulation studies, the deviations between the results obtained from SmuSim and those from Monte Carlo simulation are less than 3% and 2%, respectively, demonstrating the effectiveness of the proposed SmuSim.

Conclusion: We analyze charge sharing effects in photon counting CT, a comprehensive analytical model, and finally simulate CT images with charge sharing effects for evaluation.

BackgroundThe precise pneumoconiosis staging suffers from progressive pair label noise (PPLN) in chest X-ray datasets, because adjacent stages are confused due to unidentifialble and diffuse opacities in the lung fields. As deep neural networks are employed to aid the disease staging, the performance is degraded under such label noise.ObjectiveThis study improves the effectiveness of pneumoconiosis staging by mitigating the impact of PPLN through network architecture refinement and sample selection mechanism adjustment.MethodsWe propose a novel multi-branch architecture that incorporates the dual-threshold sample selection. Several auxiliary branches are integrated in a two-phase module to learn and predict the progressive feature tendency. A novel difference-based metric is introduced to iteratively obtained the instance-specific thresholds as a complementary criterion of dynamic sample selection. All the samples are finally partitioned into clean and hard sets according to dual-threshold criteria and treated differently by loss functions with penalty terms.ResultsCompared with the state-of-the-art, the proposed method obtains the best metrics (accuracy: 90.92%, precision: 84.25%, sensitivity: 81.11%, F1-score: 82.06%, and AUC: 94.64%) under real-world PPLN, and is less sensitive to the rise of synthetic PPLN rate. An ablation study validates the respective contributions of critical modules and demonstrates how variations of essential hyperparameters affect model performance.ConclusionsThe proposed method achieves substantial effectiveness and robustness against PPLN in pneumoconiosis dataset, and can further assist physicians in diagnosing the disease with a higher accuracy and confidence.

Cone beam computed tomography (CBCT) is increasingly used in clinical settings, with the radiation dose incurred during X-ray acquisition emerging as a critical concern. Traditional algorithms for reconstructing high-quality CBCT images typically necessitate hundreds of X-ray projections, prompting a shift towards sparse-view CBCT reconstruction as a means to minimize radiation exposure. A novel approach, leveraging the Neural Attenuation Field (NAF) based on neural radiation field algorithms, has recently gained traction. This method offers rapid and promising CBCT reconstruction outcomes using a mere 50 views. Nonetheless, NAF tends to overlook the inherent structural properties of projected images, which can lead to shortcomings in accurately capturing the structural essence of the object being imaged. To address these limitations, we introduce an enhanced method: Regularization Neural Attenuation Fields (RNAF). Our approach includes two key innovations. First, we implement a hash coding regularization technique designed to retain low-frequency details within the reconstructed images, thereby preserving essential structural information. Second, we incorporate a Local Patch Global (LPG) sampling strategy. This method focuses on extracting local geometric details from the projection image, ensuring that the intensity variations in randomly sampled X-rays closely mimic those in the actual projection image. Comparative analyses across various body parts (Chest, Jaw, Foot, Abdomen, Knee) reveal that RNAF substantially outperforms existing algorithms. Specifically, its reconstruction quality exceeds that of previous NeRF-based, optimization-based, and analysis algorithms by margins of at least 2.09 dB, 3.09 dB, and 13.84 dB respectively. This significant enhancement in performance underscores the potential of RNAF as a groundbreaking solution in the realm of CBCT imaging, offering a path towards achieving high-quality reconstructions with reduced radiation exposure. Our implementation is publically available at https://github.com/springXIACJ/FRNAF.