Journal of X-Ray Science and Technology最新文献

BackgroundMeasuring an X-ray source's focal spot size is vital for Micro-CT resolution. Standard methods are often too complex or inaccurate. The popular JIMA resolution test card is simple to use but lacks a clear, quantitative formula to determine the actual focal spot size.ObjectiveThis study aims to create a reliable quantitative link between JIMA resolution and focal spot size using simulations and experiments.MethodsWe used Monte Carlo simulations and practical experiments to establish the relationship between JIMA resolution and focal spot size.ResultsWe found that the focal spot size is twice the line pair width on the JIMA card when the image contrast (MTF) is at 10%. This method is highly accurate, with a maximum measurement error of less than 8.7% compared to a high-precision technique.ConclusionsOur findings provide a simple, fast, and validated method for measuring focal spot size using the JIMA test card. This makes it a practical and reliable alternative to more complex procedures.

Synchrotron radiation micro-computed tomography (SR-µCT) is a vital technique for the quantitative characterization of three-dimensional internal structures across diverse fields, including energy, integrated circuits, materials science, biomedicine, archaeology etc. While SR-µCT provides high spatial resolution and high image contrast, it typically offers only moderate temporal resolution, with acquisition times ranging from minutes to hours. Recently, dynamic SR-µCT has attracted significant interest for its capacity to capture real-time three-dimensional structural evolution. Here, we demonstrate a dynamic SR-µCT system operating at 26.7 Hz, developed at the BL09B test beamline of the Shanghai Synchrotron Radiation Facility using a filtered white beam. The key components of this system include an air-cooling millisecond fast shutter, an air-bearing rotation stage, a high-efficiency detector integrated with a Photron FASTCAM SA-Z camera and a custom-designed optical system, and a synchronization clock to ensure precise temporal alignment of all devices. Experimental results confirm the feasibility of this approach for in vivo four-dimensional studies, making it particularly promising for applications in biomedical research and related disciplines.

BackgroundIt is fundamental for accurate segmentation and quantification of the pulmonary vessel, particularly smaller vessels, from computed tomography (CT) images in chronic obstructive pulmonary disease (COPD) patients.ObjectiveThe aim of this study was to segment the pulmonary vasculature using a semi-supervised method.MethodsIn this study, a self-training framework is proposed by leveraging a teacher-student model for the segmentation of pulmonary vessels. First, the high-quality annotations are acquired in the in-house data by an interactive way. Then, the model is trained in the semi-supervised way. A fully supervised model is trained on a small set of labeled CT images, yielding the teacher model. Following this, the teacher model is used to generate pseudo-labels for the unlabeled CT images, from which reliable ones are selected based on a certain strategy. The training of the student model involves these reliable pseudo-labels. This training process is iteratively repeated until an optimal performance is achieved.ResultsExtensive experiments are performed on non-enhanced CT scans of 125 COPD patients. Quantitative and qualitative analyses demonstrate that the proposed method, Semi2, significantly improves the precision of vessel segmentation by 2.3%, achieving a precision of 90.3%. Further, quantitative analysis is conducted in the pulmonary vessel of COPD, providing insights into the differences in the pulmonary vessel across different severity of the disease.ConclusionThe proposed method can not only improve the performance of pulmonary vascular segmentation, but can also be applied in COPD analysis. The code will be made available at https://github.com/wuyanan513/semi-supervised-learning-for-vessel-segmentation.

Exterior CT imaging is a special X-ray imaging problem that allows for nondestructive testing of relatively large tubular samples by using smaller detectors. However, due to the incomplete nature of the exterior projection data, the exterior CT imaging problem is highly challenging. In this study, we introduce a new CT reconstruction model for polychromatic spectrum exterior problems, called the Polychromatic Exterior Discrete Grayscale PAEDS (PE-DG-PAEDS) model. This model is based on the prior of discrete grayscale values in images and introduces a radial regularization term using polychromatic spectrum information for exterior CT reconstruction. Additionally, an alternating minimization method and the Discrete Algebraic Reconstruction Technique (DART) algorithm are used for alternating iterations to provide a solution algorithm for this model. Experiments conducted with both simulated and real data have validated the proposed model and algorithm. The results indicate that the method effectively suppresses artifacts associated with polychromatic X-ray CT exterior problem.

BackgroundPulsed Electron paramagnetic resonance (EPR) imaging (EPRI) is an advanced oxygen imaging modality for precision radiotherapy, typically acquires high signal-to-noise ratio (SNR) data by averaging the repeatedly collected projections at the corresponding angle to suppress the random noise. This scan mode is the reason for the slow scan speed. The present mitigation is to reduce the repetition times (termed 'shots') for each projection, which leads to noisy projections.ObjectiveAlthough the directional total variation (DTV) algorithm could reconstruct the image from these noisy projections, it may appear staircase artifacts. To solve this problem, we further propose a novel high order DTV (HODTV) algorithm for fast 3D pulsed EPRI.MethodsThe HODTV model has introduced the regularization of high order derivatives, in which the objective term and the high order derivate regularization aim for data fidelity and detail recovery, respectively. Then, we derive its Chambolle-Pock (CP) solving algorithm and verify the correctness. To evaluate the HODTV algorithm, both qualitative and quantitative results are performed with real-world data.ResultsCompared with the filtered back projection (FBP), total variation (TV), and DTV algorithms, the results demonstrate that our method can achieve higher accurate reconstruction. In specific cases, our algorithm only requires 100 shots of scan acquisitions in $\sim$6 seconds, whereas the FBP algorithm needs 2000 shots of scan acquisitions taking $\sim$120 seconds.ConclusionsThe practical development of clinical imaging workflow, including but not limited to fast 3D pulsed EPRI, may make use of our work.

BackgroundNon-destructive testing (NDT) is crucial for the preservation and restoration of ancient wooden structures, with Computed Tomography (CT) increasingly utilized in this field. However, practical CT examinations of these structures-often characterized by complex configurations, large dimensions, and on-site constraints-frequently encounter difficulties in acquiring full-angle projection data. Consequently, images reconstructed under limited-angle conditions suffer from poor quality and severe artifacts, hindering accurate assessment of critical internal features such as mortise-tenon joints and incipient damage.ObjectiveThis study aims to develop a novel algorithm capable of achieving high-quality image reconstruction from incomplete, limited-angle projection data.MethodsWe propose CADRE (Contour-guided Alternating Direction Method of Multipliers-optimized Deep Radon Enhancement), an unsupervised deep learning reconstruction framework. CADRE innovatively integrates the ADMM optimization strategy, the learning paradigm of Deep Radon Prior (DRP) networks, and a geometric contour-guidance mechanism. This approach synergistically enhances reconstruction performance by iteratively optimizing network parameters and input images, without requiring large-scale paired training data, rendering it particularly suitable for cultural heritage applications.ResultsSystematic validation using both a digital dougong simulation model of the Yingxian Wooden Pagoda and a physical wooden dougong model from Foguang Temple demonstrates that, under typical 90° and 120° limited-angle conditions, the CADRE algorithm significantly outperforms traditional FBP, iterative reconstruction algorithms SART and ADMM-TV, and other representative unsupervised deep learning methods (Deep Image Prior, DIP; Residual Back-Projection with DIP, RBP-DIP; DRP). This superiority is evident in quantitative metrics such as PSNR and SSIM, as well as in visual quality, including artifact suppression and preservation of structural details. CADRE exhibits exceptional capability in accurately reproducing internal mortise-tenon configurations and fine features within ancient timber.ConclusionThe CADRE algorithm provides a robust and efficient solution for limited-angle CT image reconstruction of ancient wooden structures. It effectively overcomes the limitations of existing methods in handling incomplete data, significantly enhances the quality of reconstructed images and the characterization of internal fine structures, and offers strong technical support for the scientific understanding, condition assessment, and precise conservation of cultural heritage, thereby holding substantial academic value and promising application prospects.

Accurate material decomposition constitutes the foundation of Spectral Computed Tomography (Spectral CT) applications across diverse domains. Nevertheless, conventional model-based material decomposition methods face significant limitations including sparse-view sampling artifacts, slow convergence rates, noise amplification, and inherent ill-posedness-challenges that are particularly pronounced in geometrically inconsistent imaging. To overcome these constraints, we propose an unsupervised deep learning framework that synergistically optimizes virtual monochromatic images (VMIs) through the probabilistic diffusion model for direct material decomposition in sparse-view spectral CT. The proposed methodology introduces VMIs as critical differentiation enhancers for polychromatic projections, effectively addressing convergence limitations in iterative reconstruction algorithms. By incorporating probabilistic diffusion priors into the optimization process, we achieve superior refinement of material-specific representations. Our framework systematically enforces dual constraint: 1) data fidelity term ensuring measurement consistency, and 2) probabilistic regularization suppressing unwanted structures, thereby guaranteeing anatomically plausible material image reconstruction. Comprehensive validation on preclinical data demonstrates that our method achieves a 10 dB improvement in the peak-signal-to-noise ratio (PSNR) and a 4.31% increase in structural similarity (SSIM) for soft-tissue reconstructions compared to the optimal comparison algorithm with 90 projections. Experimental results confirm the algorithm's robustness under challenging conditions, maintaining reconstruction fidelity even with geometric inconsistency and sparse sampling.

Cancer remains a leading cause of mortality, where early detection significantly improves survival rates. Advances in technology have enabled automated cancer detection using medical imaging and microarray gene expression data. However, these datasets often contain redundant or noisy features that hinder classification performance. Feature selection is key preprocessing step to enhance accuracy and reduce computational costs. In cancer-related medical research, optimizing deep learning architectures is crucial for better classification outcomes. Metaheuristic algorithms have been popular for tackling both feature selection and deep neural networks (DNN) optimization. This survey reviews 91 peer-reviewed articles (2012-2025) on metaheuristics for feature selection and DNN optimization in cancer classification using medical images and microarray data. Literature was sourced from databases such as Google Scholar, IEEE Xplore, Elsevier, ResearchGate, Springer, MDPI, and ScienceDirect. Our findings indicate that k-Nearest Neighbors (kNN), Support Vector Machines (SVM), and Convolutional Neural Networks (CNN) are the most widely adopted classifiers, used in 23%, 21%, and 18% of cases, respectively. Among metaheuristics, Particle Swarm Optimization (PSO), Genetic Algorithm (GA), and Ant Colony Optimization (ACO) dominate the landscape, appearing in 13%, 11%, and 10% of studies. We also review 39 image-based and 44 microarray cancer datasets. This survey identifies critical gaps in current research and proposes several future directions to enhance model robustness and classification accuracy. Through a detailed comparative analysis, this study provides valuable insights for researchers and decision-makers, highlighting the need for continued innovation in computational methods for cancer detection and diagnosis.

Parkinson's disease (PD) is a challenging neurodegenerative condition often prone to diagnostic errors, where early and accurate diagnosis is critical for effective clinical management. However, existing diagnostic methods often fail to fully exploit multimodal data or systematically incorporate expert domain knowledge. To address these limitations, we propose M²KD-Net, a multimodal and knowledge-driven diagnostic framework that integrates imaging and non-imaging clinical data with structured expert insights to enhance diagnostic performance. The framework consists of three key modules: (1) a contrastive learning-based multimodal feature extractor for improved alignment between imaging and non-imaging data. (2) an expert feature modeling module that encodes domain-specific knowledge through structured annotations, and (3) a cross-modal interaction module that enhances the integration of heterogeneous features across modalities. Experimental results on the Parkinson's Progression Markers Initiative (PPMI) dataset show that M²KD-Net achieves a classification accuracy of 89.6% and an AUC of 0.935 in distinguishing PD patients from healthy controls. This evidence suggests that the developed method provides a dependable, interpretable, and clinically useful solution for PD diagnosis.

ObjectiveThis study aimed to develop a robust framework for breast cancer diagnosis by integrating advanced segmentation and classification approaches. Transformer-based and U-Net segmentation models were combined with radiomic feature extraction and machine learning classifiers to improve segmentation precision and classification accuracy in mammographic images.Materials and MethodsA multi-center dataset of 8000 mammograms (4200 normal, 3800 abnormal) was used. Segmentation was performed using Transformer-based and U-Net models, evaluated through Dice Coefficient (DSC), Intersection over Union (IoU), Hausdorff Distance (HD95), and Pixel-Wise Accuracy. Radiomic features were extracted from segmented masks, with Recursive Feature Elimination (RFE) and Analysis of Variance (ANOVA) employed to select significant features. Classifiers including Logistic Regression, XGBoost, CatBoost, and a Stacking Ensemble model were applied to classify tumors into benign or malignant. Classification performance was assessed using accuracy, sensitivity, F1 score, and AUC-ROC. SHAP analysis validated feature importance, and Q-value heatmaps evaluated statistical significance.ResultsThe Transformer-based model achieved superior segmentation results with DSC (0.94 ± 0.01 training, 0.92 ± 0.02 test), IoU (0.91 ± 0.01 training, 0.89 ± 0.02 test), HD95 (3.0 ± 0.3 mm training, 3.3 ± 0.4 mm test), and Pixel-Wise Accuracy (0.96 ± 0.01 training, 0.94 ± 0.02 test), consistently outperforming U-Net across all metrics. For classification, Transformer-segmented features with the Stacking Ensemble achieved the highest test results: 93% accuracy, 92% sensitivity, 93% F1 score, and 95% AUC. U-Net-segmented features achieved lower metrics, with the best test accuracy at 84%. SHAP analysis confirmed the importance of features like Gray-Level Non-Uniformity and Zone Entropy.ConclusionThis study demonstrates the superiority of Transformer-based segmentation integrated with radiomic feature selection and robust classification models. The framework provides a precise and interpretable solution for breast cancer diagnosis, with potential for scalability to 3D imaging and multimodal datasets.