Journal of X-Ray Science and Technology最新文献

{"title":"LungNet-ViT: Efficient lung disease classification using a multistage vision transformer model from chest radiographs.","authors":"V Padmavathi, Kavitha Ganesan","doi":"10.1177/08953996251320262","DOIUrl":"https://doi.org/10.1177/08953996251320262","url":null,"abstract":"<p><p>This research introduces a Multistage-Vision Transformer (Multistage-ViT) model for precisely classifying various lung diseases using chest radiographic (CXR) images. The dataset in the proposed method includes four classes: Normal, COVID-19, Viral Pneumonia and Lung Opacity. This model demonstrates its efficacy on imbalanced and balanced datasets by enhancing classifier accuracy through deep feature extraction. It integrates backbone models with the ViT architecture, creating rigorously hybrid configurations compared to their standalone counterparts. These hybrid models utilize optimized features for classification, significantly improving their performance. Notably, the multistage-ViT model achieved accuracies of 99.93% on an imbalanced dataset and 99.97% on a balanced dataset using the InceptionV3 combined with the ViT model. These findings highlight the superior accuracy and robustness of multistage-ViT models, underscoring their potential to enhance lung disease classification through advanced feature extraction and model integration techniques. The proposed model effectively demonstrates the benefits of employing ViT for deep feature extraction from CXR images.</p>","PeriodicalId":49948,"journal":{"name":"Journal of X-Ray Science and Technology","volume":" ","pages":"8953996251320262"},"PeriodicalIF":1.7,"publicationDate":"2025-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143732745","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Performance of a focused 2D anti-scatter grid for industrial X-ray computed tomography.","authors":"Joseph John Lifton, Zheng Jie Tan, Christian Filemon","doi":"10.1177/08953996251325072","DOIUrl":"https://doi.org/10.1177/08953996251325072","url":null,"abstract":"<p><p>X-ray computed tomography (XCT) is increasingly being used for the measurement and inspection of large dense metallic engineering components. When scanning such components, the quality of the data is degraded by the presence of scattered radiation. In this work, the performance of a focused 2D anti-scatter grid (ASG) is investigated for scanning samples made from cobalt chrome and Inconel on a 450 kV cone-beam XCT system. The devised scatter correction method requires one additional scan of the sample, and for projections to be algorithmically processed prior to reconstruction. The results show that the ASG based scatter correction method increases the contrast-to-noise of the data by 14.5% and 61.5% for the cobalt chrome and Inconel samples, respectively. Furthermore, the method increases edge sharpness by 6% and 16.9% for outer and inner edges, respectively.</p>","PeriodicalId":49948,"journal":{"name":"Journal of X-Ray Science and Technology","volume":" ","pages":"8953996251325072"},"PeriodicalIF":1.7,"publicationDate":"2025-03-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143702009","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Inclusion of spatio-energetic charge sharing effect model for accurate photon counting CT simulation.","authors":"Jiabing Sheng, Dong Zeng, Zhaoying Bian, Mingqiang Li, Yongle Wu, Xin Li, YongShuai Ge, Jianhua Ma","doi":"10.1177/08953996251323725","DOIUrl":"https://doi.org/10.1177/08953996251323725","url":null,"abstract":"<p><strong>Background: </strong> 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.</p><p><strong>Objective: </strong> 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.</p><p><strong>Methods: </strong> 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.</p><p><strong>Results: </strong> 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.</p><p><strong>Conclusion: </strong> We analyze charge sharing effects in photon counting CT, a comprehensive analytical model, and finally simulate CT images with charge sharing effects for evaluation.</p>","PeriodicalId":49948,"journal":{"name":"Journal of X-Ray Science and Technology","volume":" ","pages":"8953996251323725"},"PeriodicalIF":1.7,"publicationDate":"2025-03-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143702002","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Dual-threshold sample selection with latent tendency difference for label-noise-robust pneumoconiosis staging.","authors":"Shuming Zhang, Xueting Ren, Yan Qiang, Juanjuan Zhao, Ying Qiao, Huajie Yue","doi":"10.1177/08953996251319652","DOIUrl":"https://doi.org/10.1177/08953996251319652","url":null,"abstract":"<p><p>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 <i>progressive feature tendency</i>. 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 <i>clean</i> and <i>hard</i> 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.</p>","PeriodicalId":49948,"journal":{"name":"Journal of X-Ray Science and Technology","volume":" ","pages":"8953996251319652"},"PeriodicalIF":1.7,"publicationDate":"2025-03-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143701998","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"RNAF: Regularization neural attenuation fields for sparse-view CBCT reconstruction.","authors":"Chunjie Xia, Tianyun Gu, Nan Zheng, Hongjiang Wei, Tsung-Yuan Tsai","doi":"10.1177/08953996241301661","DOIUrl":"https://doi.org/10.1177/08953996241301661","url":null,"abstract":"<p><p>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.</p>","PeriodicalId":49948,"journal":{"name":"Journal of X-Ray Science and Technology","volume":" ","pages":"8953996241301661"},"PeriodicalIF":1.7,"publicationDate":"2025-03-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143702029","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Cone-beam computed laminography frequency domain information distribution and missing model.","authors":"Hui Han, Yu Han, Yanmin Sun, Liyang Zhang, Xiaoqi Xi, Lei Li, Bin Yan","doi":"10.1177/08953996251325786","DOIUrl":"https://doi.org/10.1177/08953996251325786","url":null,"abstract":"<p><p>The objective of this study is to analyse and validate the distribution and missing regions in the frequency domain space of the projection information obtained from Cone-beam Computed Laminography (CBCL) scanned samples. Furthermore, the aim is to establish a frequency domain information distribution and missing model for CBCL. This paper employs the Fourier slice theorem to ascertain the spatial region of the frequency domain wherein the CBCL projection information is situated. To this end, the geometrical structure of the CL system and the spatial propagation characteristics of the cone-beam rays are subjected to analysis. Furthermore, the veracity of the model for the missing information in the CBCL frequency domain is validated through an iterative reconstruction process, whereby different regions of the frequency domain space are reconstructed through an iterative reconstruction algorithm that takes only the projection information as a constraint. The CBCL frequency domain missing information model can be employed as a priori information in the frequency domain space to facilitate further optimisation and improvement of image reconstruction.</p>","PeriodicalId":49948,"journal":{"name":"Journal of X-Ray Science and Technology","volume":" ","pages":"8953996251325786"},"PeriodicalIF":1.7,"publicationDate":"2025-03-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143701990","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Multi-limited-angle spectral CT image reconstruction based on average image induced relative total variation model.","authors":"Zhaoqiang Shen, Yumeng Guo","doi":"10.1177/08953996251314771","DOIUrl":"https://doi.org/10.1177/08953996251314771","url":null,"abstract":"<p><p>In recent years, spectral computed tomography (CT) has attracted extensive attention. The purpose of this study is to achieve a low-cost and fast energy spectral CT reconstruction algorithm by implementing multi-limited-angle scans. General spectral CT projection data are collected over a full-angular range of 360 degrees. We simulate multi-source spectral CT by using a pair of X-ray source/detector. To speed up scanning, multi-limited-angle scanning was used in each energy channel. On this basis, an average image induced relative total variation (Aii-RTV) with multi-limited-angle spectral CT image reconstruction model is proposed. The iterative algorithm is used to solve Aii-RTV. Before iteration, the weighted average projection data of the multi-limited-angle energy spectral is carried out. In each step of the iterative algorithm flow is as follows: First, the relative total variation (RTV) reconstruction model is used to reconstruct the average image using average projection data. Then, the partial derivative of the average image is used to calculate the inherent variation in RTV model due to the integrity of the average image, and take its reciprocal as the weight coefficient of the windowing total variation of each energy channel reconstruction image. Finally, the average energy image is used to guide the multi-limited-angle projection data to reconstruct the image of each energy channel so as to suppress the limited-angle artifact of each energy channel image. In addition, we also discuss the influence of parameter selection on reconstructed image quality, which is important for regularization model. Through the reconstruction of multi-limited-angle spectral CT projection data, quantitative results and reconstructed images show that our algorithm has better performance than prior image constrained compressed sensing (PICCS) and RTV. The average PSNR of our reconstruction results in different channels was 35.6273, 4.533 and 2.301 higher than RTV (31.0943) and PICCS (33.3263), respectively.</p>","PeriodicalId":49948,"journal":{"name":"Journal of X-Ray Science and Technology","volume":" ","pages":"8953996251314771"},"PeriodicalIF":1.7,"publicationDate":"2025-03-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143651687","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Cpi-awHOTV: A CAD prior improved adaptive-weighted high order TV algorithm for orthogonal translation CL.","authors":"Yarui Xi, Yufang Cai, Guorong Zhu, Haijun Yu, Wei Yuan, Zhiwei Qiao, Fenglin Liu","doi":"10.1177/08953996241299988","DOIUrl":"https://doi.org/10.1177/08953996241299988","url":null,"abstract":"<p><strong>Background: </strong>Orthogonal translation computed laminography (OTCL) has great potential for tiny fault detection in laminated structure thin-plate parts. It offers a larger magnification ratio but generates limited projection data, which would result in aliasing artifacts in the reconstructed image.</p><p><strong>Objective: </strong>One way to minimize these artifacts is to use prior information, such as the piecewise constant property and prior image information. This work was inspired by the adaptive-weighted high order total variation (awHOTV) model, which is known for its ability to protect edge and detail information. Meanwhile, the laminated structure thin-plate parts are printed using computer-aided design (CAD) images, which provide structural information.</p><p><strong>Methods: </strong>To create a reliable CAD information beforehand, we adopted a two-in-one estimation method. Therefore, combining the CAD information with the awHOTV model, we propose an improved adaptive weighted higher-order TV (Cpi-awHOTV) model based on the CAD prior and use the adaptive steepest descent projection onto convex set (ASD-POCS) algorithm to solve the imaging model.</p><p><strong>Results: </strong>To evaluate the performance of our algorithm, we compared it with existing filtered back projection (FBP), simultaneous algebraic reconstruction technique (SART), total variation (TV), adaptive-weighted TV (awTV), and high order TV (HOTV)algorithms on phantom1 and phantom2 with various scanning angle ranges. Additionally, we used the phantom2 as the CAD prior in real data experiments. The results show that, the Cpi-awHOTV algorithm can obtain high-quality reconstructed images and better quantitative evaluation indicators.</p><p><strong>Conclusions: </strong>Visual inspection and quantitative analysis of reconstructed images demonstrate that the Cpi-awHOTV algorithm effectively protects edge information, and reduces aliasing artifacts due to interference from adjacent slice structures.</p>","PeriodicalId":49948,"journal":{"name":"Journal of X-Ray Science and Technology","volume":" ","pages":"8953996241299988"},"PeriodicalIF":1.7,"publicationDate":"2025-03-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143651683","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Multimodal model for knee osteoarthritis KL grading from plain radiograph.","authors":"Mohammad Khaleel Sallam Ma'aitah, Abdulkader Helwan, Abdelrahman Radwan, Adnan Mohammad Salem Manasreh, Esam Alsadiq Alshareef","doi":"10.1177/08953996251314765","DOIUrl":"https://doi.org/10.1177/08953996251314765","url":null,"abstract":"<p><p>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%.</p>","PeriodicalId":49948,"journal":{"name":"Journal of X-Ray Science and Technology","volume":" ","pages":"8953996251314765"},"PeriodicalIF":1.7,"publicationDate":"2025-03-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143651690","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"KBA-PDNet: A primal-dual unrolling network with kernel basis attention for low-dose CT reconstruction.","authors":"Rongfeng Li, Dalin Wang","doi":"10.1177/08953996241308759","DOIUrl":"https://doi.org/10.1177/08953996241308759","url":null,"abstract":"<p><p>Computed tomography (CT) image reconstruction is faced with challenge of balancing image quality and radiation dose. Recent unrolled optimization methods address low-dose CT image quality issues using convolutional neural networks or self-attention mechanisms as regularization operators. However, these approaches have limitations in adaptability, computational efficiency, or preservation of beneficial inductive biases. They also depend on initial reconstructions, potentially leading to information loss and error propagation. To overcome these limitations, Kernel Basis Attention Primal-Dual Network (KBA-PDNet) is proposed. The method unrolls multiple iterations of the proximal primal-dual optimization process, replacing traditional proximal operators with Kernel Basis Attention (KBA) modules. This design enables direct training from raw measurement data without relying on preliminary reconstructions. The KBA module achieves adaptability by learning and dynamically fusing kernel bases, generating customized convolution kernels for each spatial location. This approach maintains computational efficiency while preserving beneficial inductive biases of convolutions. By training end-to-end from raw projection data, KBA-PDNet fully utilizes all original information, potentially capturing details lost in preliminary reconstructions. Experiments on simulated and clinical datasets demonstrate that KBA-PDNet outperforms existing approaches in both image quality and computational efficiency.</p>","PeriodicalId":49948,"journal":{"name":"Journal of X-Ray Science and Technology","volume":" ","pages":"8953996241308759"},"PeriodicalIF":1.7,"publicationDate":"2025-03-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143537915","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}