Journal of X-Ray Science and Technology最新文献_第4页

Multi-axis transformer based U-Net with class balanced ensemble model for lung disease classification using X-ray images. 基于多轴变压器的U-Net类平衡集成模型用于肺部疾病x射线图像分类。

IF 1.7 3区医学

Journal of X-Ray Science and Technology Pub Date : 2025-05-01 Epub Date: 2025-02-26 DOI: 10.1177/08953996251317416

Suresh Maruthai, Tamilvizhi Thanarajan, T Ramesh, Surendran Rajendran

{"title":"Multi-axis transformer based U-Net with class balanced ensemble model for lung disease classification using X-ray images.","authors":"Suresh Maruthai, Tamilvizhi Thanarajan, T Ramesh, Surendran Rajendran","doi":"10.1177/08953996251317416","DOIUrl":"https://doi.org/10.1177/08953996251317416","url":null,"abstract":"Background: Chest X-rays are an essential diagnostic tool for identifying chest disorders because of its high sensitivity in detecting pathological anomalies in the lungs. Classification models based on conventional Convolutional Neural Networks (CNNs) are adversely affected due to their localization bias. Objective: In this paper, a new Multi-Axis Transformer based U-Net with Class Balanced Ensemble (MaxTU-CBE) is proposed to improve multi-label classification performance. Methods: This may be the first attempt to simultaneously integrate the benefits of hierarchical Multi-Axis Transformer into the encoder and decoder of the traditional U-shaped structure for improving the semantic segmentation superiority of lung image. Results: A key element of MaxTU-CBE is the Contextual Fusion Engine (CFE), which uses the self-attention mechanism to efficiently create global interdependence between features of various scales. Also, deep CNN incorporate ensemble learning to address the issue of class unbalanced learning. Conclusions: According to experimental findings, our suggested MaxTU-CBE outperforms the competing BiDLSTM classifier by 1.42% and CBIR-CSNN techniques by 5.2% in multi-label classification performance.","PeriodicalId":49948,"journal":{"name":"Journal of X-Ray Science and Technology","volume":"33 3","pages":"540-552"},"PeriodicalIF":1.7,"publicationDate":"2025-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144022613","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}

引用次数: 0

New method for diffusion-weighted images denoising based on patch-matching with higher-order singular value decomposition. 基于高阶奇异值分解的补丁匹配扩散加权图像去噪新方法。

IF 1.7 3区医学

Journal of X-Ray Science and Technology Pub Date : 2025-05-01 Epub Date: 2025-02-26 DOI: 10.1177/08953996241313321

Liming Yang, Yuanjun Wang

{"title":"New method for diffusion-weighted images denoising based on patch-matching with higher-order singular value decomposition.","authors":"Liming Yang, Yuanjun Wang","doi":"10.1177/08953996241313321","DOIUrl":"https://doi.org/10.1177/08953996241313321","url":null,"abstract":"BackgroundDiffusion-weighted imaging (DWI) is an important technique to study brain microstructure. However, diffusion-weighted (DW) images suffer from severe low signal-to-noise ratio (SNR) problem, affecting subsequent diffusion analysis.ObjectiveThe goal of this paper is to develop advanced DWI denoising technique to effectively reduce noise while improving the accuracy and reliability of subsequent diffusion model fitting and diffusion analysis, thereby facilitating the research and analysis of brain science.MethodsWe propose a new method for denoising DW images based on patch-matching with higher-order singular value decomposition (HOSVD) by combined with the variance-stabilizing transformation technique. It starts with introducing a novel non-local mean algorithm as a prefiltering stage, and then denoises the noisy data using a local HOSVD algorithm based on the HOSVD bases learned from prefiltered images.ResultsExperiments are performed on simulation, HCP and in vivo brain DWI datasets. Results show that the proposed method significantly reduces spatially invariant and variant noise, improving the most reliable diffusion analysis compared with the different denoising methods.ConclusionsThe proposed method achieves state-of-the-art performance which can improve image quality and enable accurate diffusion analysis.","PeriodicalId":49948,"journal":{"name":"Journal of X-Ray Science and Technology","volume":"33 3","pages":"526-539"},"PeriodicalIF":1.7,"publicationDate":"2025-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144047743","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}

引用次数: 0

Radiomics from dual-energy CT-derived iodine maps for predicting lymph node metastases in patients with resectable rectal cancer. 双能ct衍生碘图放射组学预测可切除直肠癌患者淋巴结转移。

IF 1.7 3区医学

Journal of X-Ray Science and Technology Pub Date : 2025-05-01 Epub Date: 2025-02-25 DOI: 10.1177/08953996241313322

Xia Liu, Yi Yuan, Xiao-Li Chen, Zhu Fang, Si-Yun Liu, Hong Pu, Hang Li

{"title":"Radiomics from dual-energy CT-derived iodine maps for predicting lymph node metastases in patients with resectable rectal cancer.","authors":"Xia Liu, Yi Yuan, Xiao-Li Chen, Zhu Fang, Si-Yun Liu, Hong Pu, Hang Li","doi":"10.1177/08953996241313322","DOIUrl":"https://doi.org/10.1177/08953996241313322","url":null,"abstract":"BackgroundLymph node metastasis (LNM) is a poor prognostic predictor and is highly correlated with local recurrence in rectal cancer patients.ObjectiveTo investigate the value of radiomics from dual-energy CT-derived iodine maps for the preoperative prediction of LNM in rectal cancer patients.MethodsA total of 176 patients were enrolled in this study (training group, n = 123; validation group, n = 53). A radiomic signature was constructed via support vector machine (SVM) modeling. Seven models, including a clinical feature model (Model 1), an arterial model (Model 2), a venous model (Model 3), an arterial-venous model (Model 4), an arterial-clinical model (Model 5), a venous-clinical model (Model 6) and an arterial-venous-clinical model (Model 7), were established via logistic regression modeling. Diagnostic performance was assessed via receiver operating characteristic (ROC) curves.ResultsTumor location and carcinoembryonic antigen levels were used to construct Model 1 (training group, AUC [area under the ROC curve] = 0.721, 95% CI [confidence intervals], 0.630-0.813; validation group, AUC = 0.729, 95% CI, 0.593-0.865). Model 6 and Model 7 further improved the discriminatory performance in the training (AUC = 0.850 and 0.869, 95% CI, 0.782-0.919 and 0.807-0.932, respectively; p = 0.250) and validation groups (AUC = 0.780 and 0.716, 95% CI, 0.653-0.906 and 0.576-0.856, respectively; p = 0.115). Moreover, decision curve analysis revealed a greater net benefit with Model 6.ConclusionsThe combination of radiomic features based on dual-energy CT-derived iodine maps and clinical features provides better diagnostic performance for predicting LNM in rectal cancer patients.","PeriodicalId":49948,"journal":{"name":"Journal of X-Ray Science and Technology","volume":"33 3","pages":"553-564"},"PeriodicalIF":1.7,"publicationDate":"2025-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144024937","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}

引用次数: 0

Cpi-awHOTV: A CAD prior improved adaptive-weighted high order TV algorithm for orthogonal translation CL. Cpi-awHOTV：一种CAD先验改进的正交平移CL自适应加权高阶TV算法。

IF 1.7 3区医学

Journal of X-Ray Science and Technology Pub Date : 2025-05-01 Epub Date: 2025-03-17 DOI: 10.1177/08953996241299988

Yarui Xi, Yufang Cai, Guorong Zhu, Haijun Yu, Wei Yuan, Zhiwei Qiao, Fenglin Liu

{"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":"10.1177/08953996241299988","url":null,"abstract":"BackgroundOrthogonal 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.ObjectiveOne 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.MethodsTo 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.ResultsTo 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.ConclusionsVisual 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.","PeriodicalId":49948,"journal":{"name":"Journal of X-Ray Science and Technology","volume":" ","pages":"621-636"},"PeriodicalIF":1.7,"publicationDate":"2025-05-01","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}

引用次数: 0

Feasibility exploration of myocardial blood flow synthesis from a simulated static myocardial computed tomography perfusion via a deep neural network. 通过深度神经网络从模拟静态心肌计算机断层扫描灌注合成心肌血流的可行性探索。

IF 1.7 3区医学

Journal of X-Ray Science and Technology Pub Date : 2025-05-01 Epub Date: 2025-03-03 DOI: 10.1177/08953996251317412

Jun Dong, Runjianya Ling, Zhenxing Huang, Yidan Xu, Haiyan Wang, Zixiang Chen, Meiyong Huang, Vladimir Stankovic, Jiayin Zhang, Zhanli Hu

{"title":"Feasibility exploration of myocardial blood flow synthesis from a simulated static myocardial computed tomography perfusion via a deep neural network.","authors":"Jun Dong, Runjianya Ling, Zhenxing Huang, Yidan Xu, Haiyan Wang, Zixiang Chen, Meiyong Huang, Vladimir Stankovic, Jiayin Zhang, Zhanli Hu","doi":"10.1177/08953996251317412","DOIUrl":"10.1177/08953996251317412","url":null,"abstract":"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.","PeriodicalId":49948,"journal":{"name":"Journal of X-Ray Science and Technology","volume":" ","pages":"578-590"},"PeriodicalIF":1.7,"publicationDate":"2025-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143537911","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}

引用次数: 0

Innovations in artificial intelligence for pet/mr imaging: Application and performance analysis. 人工智能在pet/mr成像中的创新：应用和性能分析。

IF 1.7 3区医学

Journal of X-Ray Science and Technology Pub Date : 2025-05-01 Epub Date: 2025-02-25 DOI: 10.1177/08953996241313122

Hanzhong Wang, Yue Wang, Xing Chen, Zheng Zhang, Zengping Lin, Biao Li, Guowei Feng, Qiu Huang

{"title":"Innovations in artificial intelligence for pet/mr imaging: Application and performance analysis.","authors":"Hanzhong Wang, Yue Wang, Xing Chen, Zheng Zhang, Zengping Lin, Biao Li, Guowei Feng, Qiu Huang","doi":"10.1177/08953996241313122","DOIUrl":"https://doi.org/10.1177/08953996241313122","url":null,"abstract":"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.","PeriodicalId":49948,"journal":{"name":"Journal of X-Ray Science and Technology","volume":"33 3","pages":"516-525"},"PeriodicalIF":1.7,"publicationDate":"2025-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144025002","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}

引用次数: 0

Lung disease classification in chest X-ray images using optimal cross stage partial bidirectional long short term memory. 基于最佳交叉分期部分双向长短期记忆的胸部x线图像肺部疾病分类。

IF 1.7 3区医学

Journal of X-Ray Science and Technology Pub Date : 2025-05-01 Epub Date: 2025-02-20 DOI: 10.1177/08953996241304987

T Babu, G V Sam Kumar, L Kartheesan, Surendran Rajendran

{"title":"Lung disease classification in chest X-ray images using optimal cross stage partial bidirectional long short term memory.","authors":"T Babu, G V Sam Kumar, L Kartheesan, Surendran Rajendran","doi":"10.1177/08953996241304987","DOIUrl":"https://doi.org/10.1177/08953996241304987","url":null,"abstract":"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.","PeriodicalId":49948,"journal":{"name":"Journal of X-Ray Science and Technology","volume":"33 3","pages":"501-515"},"PeriodicalIF":1.7,"publicationDate":"2025-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144047742","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}

引用次数: 0

Multimodal model for knee osteoarthritis KL grading from plain radiograph. 膝关节骨性关节炎x线平片KL分级的多模态模型。

IF 1.7 3区医学

Journal of X-Ray Science and Technology Pub Date : 2025-05-01 Epub Date: 2025-03-17 DOI: 10.1177/08953996251314765

Mohammad Khaleel Sallam Ma'aitah, Abdulkader Helwan, Abdelrahman Radwan, Adnan Mohammad Salem Manasreh, Esam Alsadiq Alshareef

{"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":"10.1177/08953996251314765","url":null,"abstract":"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%.","PeriodicalId":49948,"journal":{"name":"Journal of X-Ray Science and Technology","volume":" ","pages":"608-620"},"PeriodicalIF":1.7,"publicationDate":"2025-05-01","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}

引用次数: 0

Advancing lung cancer diagnosis: Combining 3D auto-encoders and attention mechanisms for CT scan analysis. 推进肺癌诊断：结合三维自动编码器和注意力机制进行 CT 扫描分析。

IF 1.7 3区医学

Journal of X-Ray Science and Technology Pub Date : 2025-03-01 Epub Date: 2025-01-28 DOI: 10.1177/08953996241313120

Meng Wang, Zi Yang, Ruifeng Zhao

{"title":"Advancing lung cancer diagnosis: Combining 3D auto-encoders and attention mechanisms for CT scan analysis.","authors":"Meng Wang, Zi Yang, Ruifeng Zhao","doi":"10.1177/08953996241313120","DOIUrl":"10.1177/08953996241313120","url":null,"abstract":"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.","PeriodicalId":49948,"journal":{"name":"Journal of X-Ray Science and Technology","volume":" ","pages":"376-392"},"PeriodicalIF":1.7,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143460403","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}

引用次数: 0

An efficient and high-quality scheme for cone-beam CT reconstruction from sparse-view data. 一种高效、高质量的稀疏视图锥形束CT重建方案。

IF 1.7 3区医学

Journal of X-Ray Science and Technology Pub Date : 2025-03-01 Epub Date: 2025-02-04 DOI: 10.1177/08953996241313121

Shunli Zhang, Mingxiu Tuo, Siyu Jin, Yikuan Gu

{"title":"An efficient and high-quality scheme for cone-beam CT reconstruction from sparse-view data.","authors":"Shunli Zhang, Mingxiu Tuo, Siyu Jin, Yikuan Gu","doi":"10.1177/08953996241313121","DOIUrl":"10.1177/08953996241313121","url":null,"abstract":"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.","PeriodicalId":49948,"journal":{"name":"Journal of X-Ray Science and Technology","volume":" ","pages":"420-435"},"PeriodicalIF":1.7,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143460360","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}

引用次数: 0