Journal of X-Ray Science and Technology最新文献

{"title":"Corrigendum to \"Promptable segmentation of CT lung lesions based on improved U-Net and Segment Anything model (SAM)\".","authors":"","doi":"10.1177/08953996251358389","DOIUrl":"https://doi.org/10.1177/08953996251358389","url":null,"abstract":"","PeriodicalId":49948,"journal":{"name":"Journal of X-Ray Science and Technology","volume":" ","pages":"8953996251358389"},"PeriodicalIF":1.7,"publicationDate":"2025-07-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144676350","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":"A multi-stage training and deep supervision based segmentation approach for 3D abdominal multi-organ segmentation.","authors":"Panpan Wu, Peng An, Ziping Zhao, Runpeng Guo, Xiaofeng Ma, Yue Qu, Yurou Xu, Hengyong Yu","doi":"10.1177/08953996251355806","DOIUrl":"https://doi.org/10.1177/08953996251355806","url":null,"abstract":"<p><p>Accurate X-ray Computed tomography (CT) image segmentation of the abdominal organs is fundamental for diagnosing abdominal diseases, planning cancer treatment, and formulating radiotherapy strategies. However, the existing deep learning based models for three-dimensional (3D) CT image abdominal multi-organ segmentation face challenges, including complex organ distribution, scarcity of labeled data, and diversity of organ structures, leading to difficulties in model training and convergence and low segmentation accuracy. To address these issues, a novel multi-stage training and a deep supervision model based segmentation approach is proposed. It primary integrates multi-stage training, pseudo- labeling technique, and a developed deep supervision model with attention mechanism (DLAU-Net), specifically designed for 3D abdominal multi-organ segmentation. The DLAU-Net enhances segmentation performance and model adaptability through an improved network architecture. The multi-stage training strategy accelerates model convergence and enhances generalizability, effectively addressing the diversity of abdominal organ structures. The introduction of pseudo-labeling training alleviates the bottleneck of labeled data scarcity and further improves the model's generalization performance and training efficiency. Experiments were conducted on a large dataset provided by the FLARE 2023 Challenge. Comprehensive ablation studies and comparative experiments were conducted to validate the effectiveness of the proposed method. Our method achieves an average organ accuracy (AVG) of 90.5% and a Dice Similarity Coefficient (DSC) of 89.05% and exhibits exceptional performance in terms of training speed and handling data diversity, particularly in the segmentation tasks of critical abdominal organs such as the liver, spleen, and kidneys, significantly outperforming existing comparative methods.</p>","PeriodicalId":49948,"journal":{"name":"Journal of X-Ray Science and Technology","volume":" ","pages":"8953996251355806"},"PeriodicalIF":1.7,"publicationDate":"2025-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144651078","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":"Statistical cone-beam CT noise reduction with multiscale decomposition and penalized weighted least squares in the projection domain.","authors":"Shaojie Tang, Jin Liu, Guo Li, Zhiwei Qiao, Yang Chen, Xuanqin Mou","doi":"10.1177/08953996251337889","DOIUrl":"https://doi.org/10.1177/08953996251337889","url":null,"abstract":"<p><strong>Purposes: </strong> Suppressing noise can effectively promote image quality and save radiation dose in clinical imaging with x-ray computed tomography (CT). To date, numerous statistical noise reduction approaches have ever been proposed in image domain, projection domain or both domains. Especially, a multiscale decomposition strategy can be exploited to enhance the performance of noise suppression while preserving image sharpness. Recognizing the inherent advantage of noise suppression in the projection domain, we have previously proposed a projection domain multiscale penalized weighted least squares (PWLS) method for fan-beam CT imaging, wherein the sampling intervals are explicitly taken into account for the possible variation of sampling rates. In this work, we extend our previous method into cone-beam (CB) CT imaging, which is more relevant to practical imaging applications.</p><p><strong>Methods: </strong> The projection domain multiscale PWLS method is derived for CBCT imaging by converting an isotropic diffusion partial differential equation (PDE) in the three-dimensional (3D) image domain into its counterpart in the CB projection domain. With adoption of the Markov random field (MRF) objective function, the CB projection domain multiscale PWLS method suppresses noise at each scale. The performance of the proposed method for statistical noise reduction in CBCT imaging is experimentally evaluated and verified using the projection data acquired by an actual micro-CT scanner.</p><p><strong>Results: </strong> The preliminary result shows that the proposed CB projection domain multiscale PWLS method outperforms the CB projection domain single-scale PWLS, the 3D image domain discriminative feature representation (DFR), and the 3D image domain multiscale nonlinear diffusion methods in noise reduction. Moreover, the proposed method can preserve image sharpness effectively while avoiding generation of novel artifacts.</p><p><strong>Conclusions: </strong> Since the sampling intervals are explicitly taken into account in the projection domain multiscale decomposition, the proposed method would be beneficial to advanced applications where the CBCT imaging is employed and the sampling rates vary.</p>","PeriodicalId":49948,"journal":{"name":"Journal of X-Ray Science and Technology","volume":" ","pages":"8953996251337889"},"PeriodicalIF":1.7,"publicationDate":"2025-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144638504","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":"An improved U-NET3+ with transformer and adaptive attention map for lung segmentation.","authors":"V Joseph Raj, P Christopher","doi":"10.1177/08953996251351623","DOIUrl":"https://doi.org/10.1177/08953996251351623","url":null,"abstract":"<p><p>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.</p>","PeriodicalId":49948,"journal":{"name":"Journal of X-Ray Science and Technology","volume":" ","pages":"8953996251351623"},"PeriodicalIF":1.7,"publicationDate":"2025-07-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144627591","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":"Ultra-sparse view lung CT image reconstruction using generative adversarial networks and compressed sensing.","authors":"Zhaoguang Li, Zhengxiang Sun, Lin Lv, Yuhan Liu, Xiuying Wang, Jingjing Xu, Jianping Xing, Paul Babyn, Feng-Rong Sun","doi":"10.1177/08953996251329214","DOIUrl":"10.1177/08953996251329214","url":null,"abstract":"<p><p>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.</p>","PeriodicalId":49948,"journal":{"name":"Journal of X-Ray Science and Technology","volume":" ","pages":"803-816"},"PeriodicalIF":1.7,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144028776","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-scale geometric transformer for sparse-view X-ray 3D foot reconstruction.","authors":"Wei Wang, Li An, Gengyin Han","doi":"10.1177/08953996251319194","DOIUrl":"10.1177/08953996251319194","url":null,"abstract":"<p><strong>Background: </strong>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.</p><p><strong>Objective: </strong>This paper presents a novel method using a multi-scale geometric Transformer to enhance reconstruction accuracy and detail representation.</p><p><strong>Methods: </strong>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.</p><p><strong>Results: </strong>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.</p><p><strong>Conclusions: </strong>The proposed method advances medical image reconstruction, significantly improving the accuracy and detail preservation of 3D foot reconstructions from sparse-view X-ray images.</p>","PeriodicalId":49948,"journal":{"name":"Journal of X-Ray Science and Technology","volume":" ","pages":"776-787"},"PeriodicalIF":1.7,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144012077","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":"An iterative-FBP dual-spectral CT reconstruction algorithm based on scatter modeling.","authors":"Jingna Zhang, Wenfeng Xu, Ran An, Huitao Zhang, Yunsong Zhao, Xing Zhao","doi":"10.1177/08953996251332472","DOIUrl":"10.1177/08953996251332472","url":null,"abstract":"<p><p>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.</p>","PeriodicalId":49948,"journal":{"name":"Journal of X-Ray Science and Technology","volume":" ","pages":"788-802"},"PeriodicalIF":1.7,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144028814","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":"Enhanced boundary-directed lightweight approach for digital pathological image analysis in critical oncological diagnostics.","authors":"Ou Luo, Jing Zhou, Fangfang Gou","doi":"10.1177/08953996251325092","DOIUrl":"10.1177/08953996251325092","url":null,"abstract":"<p><p>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<b>.</b>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.</p>","PeriodicalId":49948,"journal":{"name":"Journal of X-Ray Science and Technology","volume":" ","pages":"760-775"},"PeriodicalIF":1.7,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144051671","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":"The deep radon prior-based stationary CT image reconstruction algorithm for two phase flow inspection.","authors":"Jiahao Chang, Shuo Xu, Zirou Jiang, Yucheng Zhang, Yuewen Sun","doi":"10.1177/08953996251322078","DOIUrl":"https://doi.org/10.1177/08953996251322078","url":null,"abstract":"<p><p>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.</p>","PeriodicalId":49948,"journal":{"name":"Journal of X-Ray Science and Technology","volume":"33 4","pages":"726-741"},"PeriodicalIF":1.7,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144545828","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":"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":"695-712"},"PeriodicalIF":1.7,"publicationDate":"2025-07-01","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}