Journal of X-Ray Science and Technology最新文献

BackgroundThe primary challenges in PET/MR imaging include prolonged scan durations for both PET and MR components and radiation exposure associated with the PET modality. Artificial intelligence (AI)-based techniques offer a promising approach to overcome these limitations.ObjectiveThis study evaluates the AI-based image enhancement methods integrated into the United Imaging PET/MR system, focusing on improvements in image quality, reduced injection dose, and shortened acquisition duration.MethodSixty-three patients underwent ¹⁸F-FDG PET/MR scans using uPMR790 (0.09 ± 0.01 mCi/kg, 5 min/bed, n = 29) and uPMR890 (0.05 ± 0.01 mCi/kg, 2.5 min/bed for PET and accelerated MR protocols, n = 34) with advanced AI-enhanced method. Shortened MR protocols included T1 W and T2 W sequences. Image quality was evaluated subjectively by two physicians and objectively using SNR and artifact ratios.ResultsThe AI-enhanced system achieved high-quality PET and MR images despite reduced PET doses and scan durations for both PET and MR components. AI-based reconstruction images showed higher SNR, fewer artifacts, and reduced noise compared to the conventional system.ConclusionsAI-enhanced PET/MR significantly improves imaging efficiency by reducing PET/MR acquisition durations, lowering radiation dose, and enhancing overall image quality, making it a valuable tool for clinical hybrid imaging.

Background: Myocardial blood flow (MBF) provides important diagnostic information for myocardial ischemia. However, dynamic computed tomography perfusion (CTP) needed for MBF involves multiple exposures, leading to high radiation doses.

Objectives: This study investigated synthesizing MBF from simulated static myocardial CTP to explore dose reduction potential, bypassing the traditional dynamic input function.

Methods: The study included 253 subjects with intermediate-to-high pretest probabilities of obstructive coronary artery disease (CAD). MBF was reconstructed from dynamic myocardial CTP. A deep neural network (DNN) converted simulated static CTP into synthetic MBF. Beyond the usual image quality evaluation, the synthetic MBF was segmented and a clinical functional assessment was conducted, with quantitative analysis for consistency and correlation.

Results: Synthetic MBF closely matched the referenced MBF, with an average structure similarity (SSIM) of 0.87. ROC analysis of ischemic segments showed an area under curve (AUC) of 0.915 for synthetic MBF. This method can theoretically reduce the radiation dose for MBF significantly, provided satisfactory static CTP is obtained, reducing reliance on high time resolution of dynamic CTP.

Conclusions: The proposed method is feasible, with satisfactory clinical functionality of synthetic MBF. Further investigation and validation are needed to confirm actual dose reduction in clinical settings.

Knee osteoarthritis presents a significant health challenge for many adults globally. At present, there are no pharmacological treatments that can cure this medical condition. The primary method for managing the progress of knee osteoarthritis is through early identification. Currently, X-ray imaging serves as a key modality for predicting the onset of osteoarthritis. Nevertheless, the traditional manual interpretation of X-rays is susceptible to inaccuracies, largely due to the varying levels of expertise among radiologists. In this paper, we propose a multimodal model based on pre-trained vision and language models for the identification of the knee osteoarthritis severity Kellgren-Lawrence (KL) grading. Using Vision transformer and Pre-training of deep bidirectional transformers for language understanding (BERT) for images and texts embeddings extraction helps Transformer encoders extracts more distinctive hidden-states that facilitates the learning process of the neural network classifier. The multimodal model was trained and tested on the OAI dataset, and the results showed remarkable performance compared to the related works. Experimentally, the evaluation of the model on the test set comprising X-ray images demonstrated an overall accuracy of 82.85%, alongside a precision of 84.54% and a recall of 82.89%.

BackgroundLung disease is the crucial disease that affects the breathing conditions and even causes death. There are various approaches for the lung disease classification; still the inefficiency in accurate detection, computational complexity and over-fitting issues limits the performance of the model. To overcome the challenges, a deep learning model is proposed in this research. Initially, the input is acquired and is pre-processed using three various techniques like data augmentation, filtering and image re-sizing. Then, the threshold based segmentation is employed for obtaining the required region.ObjectiveFrom the segmented image, various categories of lung diseases like COVID, lung Opacity, Pneumonia and normal are identified using the proposed Optimal Cross Stage Partial Bidirectional Long short term memory (OCBiNet).MethodsThe proposed OCBiNet is designed using Bidirectional Long short-term memory (BiNet) with Cross Stage Partial connection in its hidden state. Besides, the adjustable parameters are modified using the proposed Improved Mother Optimization (ImMO) algorithm.ResultsThe ImMO algorithm is designed by integrating the Logistic Chaotic Mapping within the conventional Mother Optimization algorithm for enhancing the convergence rate in obtaining the global best solution.ConclusionsThe proposed OCBiNet is evaluated based on Accuracy, Recall, Precision, and F-Score and acquired the values of 99.11%, 98.98%, 99.18%, and 99.08% respectively.

ObjectiveThe goal of this study is to assess the effectiveness of a hybrid deep learning model that combines 3D Auto-encoders with attention mechanisms to detect lung cancer early from CT scan images. The study aims to improve diagnostic accuracy, sensitivity, and specificity by focusing on key features in the scans.Materials and methodsA hybrid model was developed that combines feature extraction using 3D Auto-encoder networks with attention mechanisms. First, the 3D Auto-encoder model was tested without attention, using feature selection techniques such as RFE, LASSO, and ANOVA. This was followed by evaluation using several classifiers: SVM, RF, GBM, MLP, LightGBM, XGBoost, Stacking, and Voting. The model's performance was evaluated based on accuracy, sensitivity, F1-Score, and AUC-ROC. After that, attention mechanisms were added to help the model focus on important areas in the CT scans, and the performance was re-assessed.ResultsThe 3D Auto-encoder model without attention achieved an accuracy of 93% and sensitivity of 89%. When attention mechanisms were added, the performance improved across all metrics. For example, the accuracy of SVM increased to 94%, sensitivity to 91%, and AUC-ROC to 0.96. Random Forest (RF) also showed improvements, with accuracy rising to 94% and AUC-ROC to 0.93. The final model with attention improved the overall accuracy to 93.4%, sensitivity to 90.2%, and AUC-ROC to 94.1%. These results highlight the important role of attention in identifying the most relevant features for accurate classification.ConclusionsThe proposed hybrid deep learning model, especially with the addition of attention mechanisms, significantly improves the early detection of lung cancer. By focusing on key features in the CT scans, the attention mechanism helps reduce false negatives and boosts overall diagnostic accuracy. This approach has great potential for use in clinical applications, particularly in the early-stage detection of lung cancer.

Computed tomography (CT) is capable of generating detailed cross-sectional images of the scanned objects non-destructively. So far, CT has become an increasingly vital tool for 3D modelling of cultural relics. Compressed sensing (CS)-based CT reconstruction algorithms, such as the algebraic reconstruction technique (ART) regularized by total variation (TV), enable accurate reconstructions from sparse-view data, which consequently reduces both scanning time and costs. However, the implementation of the ART-TV is considerably slow, particularly in cone-beam reconstruction. In this paper, we propose an efficient and high-quality scheme for cone-beam CT reconstruction based on the traditional ART-TV algorithm. Our scheme employs Joseph's projection method for the computation of the system matrix. By exploiting the geometric symmetry of the cone-beam rays, we are able to compute the weight coefficients of the system matrix for two symmetric rays simultaneously. We then employ multi-threading technology to speed up the reconstruction of ART, and utilize graphics processing units (GPUs) to accelerate the TV minimization. Experimental results demonstrate that, for a typical reconstruction of a 512 × 512 × 512 volume from 60 views of 512 × 512 projection images, our scheme achieves a speedup of 14 × compared to a single-threaded CPU implementation. Furthermore, high-quality reconstructions of ART-TV are obtained by using Joseph's projection compared with that using traditional Siddon's projection.

BackgroundThe lobster eye micro pore optics (MPO) plays a pivotal role in X-ray focusing, composed of thousands of hollow square microfibers. The channel error in MPO can profoundly impact its focusing performance. Due to the complex manufacturing process of MPO, there are numerous factors that can contribute to channel errors.ObjectiveThis paper investigates the impact of two key quality indicators of fiber, i.e., diameter precision and ovality, on the focusing performance of flat MPO.MethodsDuring the actual production process of MPO, fibers with varying diameter precision and ovality are utilized, and point-to-point vacuum X-ray focusing equipment is used to assess MPO's focusing performance. Channel error models related to fiber diameter accuracy and ovality are established in the simulation.ResultsExperiments show that both the diameter precision and ovality of fiber influence MPO focusing abilities, with diameter precision primarily affecting the intensity and uniformity of the central point focus and the parallelism of the line foci, while ovality mainly affects the intensity and continuity of the line foci. Numerical simulation results reveal that tilt channel errors significantly affect the X-ray focusing effects.ConclusionsThese findings hold important guiding significance for the preparation process of square fibers and high quality X-ray focusing device.

BackgroundCommercial micro X-ray fluorescence (μXRF) systems often employ a tilted convergent beam, which can cause a misalignment between X-ray cartography and the corresponding visible images. This misalignment is typically considered a disadvantage, as it hinders the accurate spatial correlation of elemental information. However, this apparent drawback can be exploited to facilitate X-ray stereoscopy.ObjectiveTo demonstrate the use of unmodified commercial μXRF equipment to estimate the 3D configurations of metals and voids within a low-atomic-weight matrix, specifically polymethyl methacrylate, and to explore the implications for enhancing μXRF mapping techniques. This approach could have applications in materials science, archaeology, and other fields requiring non-destructive 3D chemical mapping.MethodsUsing unmodified commercial μXRF equipment, we leveraged both XRF and Compton scattering effects to quantitatively reconstruct the size, position, and depth of embedded tungsten, copper, and silver objects. The study specifically examines how beam divergence affects the acutance of objects located deeper within the sample.ResultsOur findings indicate a depth estimation bias ranging from 4% to 15% for depths between 24 mm, and a size estimation bias below 3.2%. These results validate the methodology and highlight the robustness of our approach under typical operational settings, suggesting that the technique could be applied to a wide range of samples with minimal modifications to existing μXRF systems.ConclusionsThe study confirms that the inclination-induced misalignment in μXRF systems can be harnessed to enhance three-dimensional imaging capabilities. Our work establishes a foundation for advancing current μXRF mapping techniques and interpretation strategies, potentially broadening the applications of μXRF in various scientific and industrial fields.

BackgroundPreconditioned Kaczmarz methods play a pivotal role in image reconstruction. A fundamental theoretical question lies in establishing the convergence conditions for these methods. Practically, devising an efficient block strategy to accelerate the reconstruction process is also critical.ObjectiveThis paper aims to introduce the convergence conditions for the preconditioned Kaczmarz methods and design the block strategy with corresponding preconditioners for these methods in computed tomography (CT).MethodsWe establish a kind of useful convergence conditions for the preconditioned block Kaczmarz methods and prove the dependence of the convergence limit on the initial guess. Tailored for the CT problem, we also propose a new method with a novel block strategy and specific preconditioners, which ensure accelerated convergence.ResultsNumerical experiments with the Shepp-Logan phantom and a real chest CT image demonstrate that our proposed block strategy and preconditioners effectively accelerate the reconstruction process by the preconditioned block Kaczmarz methods while maintaining satisfactory image quality.ConclusionsOur proposed method, which incorporates the designed block strategy and specific preconditioners, has superior performance compared to the traditional Landweber iteration and the block Kaczmarz iteration without preconditioners.

Industrial Computed Tomography (CT) technology is increasingly applied in fields such as additive manufacturing and non-destructive testing, providing rich three-dimensional information for various fields, which is crucial for internal structure detection, defect detection, and product development. In subsequent processes such as analysis, simulation, and editing, three-dimensional volume data models often need to be converted into mesh models, making effective meshing of volume data essential for expanding the application scenarios and scope of industrial CT. However, the existing Marching Cubes algorithm has issues with low efficiency and poor mesh quality during the volume data meshing process. To overcome these limitations, this study proposes an innovative method for industrial CT volume data meshing based on the Iterative Smooth Signed Surface Distance (iSSD) algorithm. This method first refines the segmented voxel model, accurately extracts boundary voxels, and constructs a high-quality point cloud model. By randomly initializing the normals of the point cloud and iteratively updating the point cloud normals, the mesh is reconstructed using the SSD algorithm after each iteration update, ultimately achieving high-quality, watertight, and smooth mesh model reconstruction, ensuring the accuracy and reliability of the reconstructed mesh. Qualitative and quantitative analyses with other methods have further highlighted the excellent performance of the method proposed in this paper. This study not only improves the efficiency and quality of volume data meshing but also provides a solid foundation for subsequent three-dimensional analysis, simulation, and editing, and has important industrial application prospects and academic value.