Journal of X-Ray Science and Technology最新文献

{"title":"Comparison of treatment plans for patients with high-grade brain tumors using two different treatment planning systems in radiotherapy.","authors":"Elif Yeşil, Meryem Aktan, Ahmet Şahin, Hikmettin Demir","doi":"10.1177/08953996261444398","DOIUrl":"https://doi.org/10.1177/08953996261444398","url":null,"abstract":"<p><strong>Objective: </strong>To compare treatment plans created using the Eclipse and Prowess treatment planning systems for patients diagnosed with high-grade brain tumors in the left temporal lobe and to evaluate target volume coverage and doses delivered to critical organs using intensity-modulated radiotherapy (IMRT) technique.</p><p><strong>Materials and methods: </strong>This retrospective comparative planning study included 15 patients. Plans were created on CT images using step-and-shoot IMRT with 6 MV photons. Eclipse used the Pencil Beam Convolution (PBC) algorithm, while Prowess used the Collapsed Cone Convolution (CCCS) algorithm. A total dose of 60 Gy in 30 fractions was prescribed. Dosimetric parameters were analyzed statistically.</p><p><strong>Results: </strong>Both TPSs achieved comparable target coverage, with no significant differences in conformity and homogeneity indices (p = 0.075 and p = 0.590, respectively) or dose-volume parameters (D95, D98, D2; p > 0.05). Eclipse provided significantly lower doses to the ipsilateral lens, brainstem, left optic nerve, cochlea, and whole brain V40 (p < 0.05).</p><p><strong>Conclusion: </strong>Both TPSs demonstrated adequate target coverage; however, Eclipse achieved statistically significant dose reductions in several critical structures, which may have clinical relevance in reducing toxicity risk.</p>","PeriodicalId":49948,"journal":{"name":"Journal of X-Ray Science and Technology","volume":" ","pages":"8953996261444398"},"PeriodicalIF":1.4,"publicationDate":"2026-04-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147786922","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":"Quantum-inspired cardiac risk assessment using hybrid CSAGGO-Q-SpinalNet algorithm for precise heart disease prediction.","authors":"W Ancy Breen, S Muthu Vijaya Pandian, M Muthukrishnaveni","doi":"10.1177/08953996261439087","DOIUrl":"https://doi.org/10.1177/08953996261439087","url":null,"abstract":"<p><p>Cardiovascular disease remains the leading cause of mortality globally, with escalating risk factors and increasing pressure on the healthcare system. Despite the critical importance of early diagnosis, it is impeded by challenges, such as data imbalance, feature complexity, and variability in diagnostic processes. These challenges necessitate the development of sophisticated intelligent systems to ensure the accurate and timely prediction of heart disease. This work presents an intelligent system that integrates advanced machine learning techniques for heart disease prediction, employing the capuchin search algorithm graylag goal optimization (CSAGGO) and Quantum-SpinalNet (Q-SpinalNet) for enhanced classification. The methodology begins with data preprocessing using Principal Component Analysis (PCA) and synthetic minority oversampling technique (SMOTE) to address issues of dimensionality and class imbalance. An improved Fuzzy C-Means Gaussian Mixture Model (FCM-GMM) was utilized for clustering, while the Least Absolute Shrinkage and Selection Operator (LASSO) identified the most informative features. To enhance interpretability, SHapley Additive exPlanations (SHAP) values were employed to elucidate the influence of individual features on predictions, providing actionable insights for healthcare professionals. The hybrid CSAGGO-Q-SpinalNet framework surpasses the existing methods, offering a robust, efficient, and explainable solution for heart disease prediction. The proposed system achieved exceptional performance metrics, including 98.44% accuracy, 96.89% sensitivity, 96.83% specificity, and 96.22% precision on the Cleveland dataset. Additionally, the model demonstrated low error rates with a 4.24% false positive rate (FPR), 4.38% false negative rate (FNR), and 4.05% false discovery rate (FDR). This system holds significant promise for real-world clinical applications by facilitating early diagnosis and personalized treatment strategies.</p>","PeriodicalId":49948,"journal":{"name":"Journal of X-Ray Science and Technology","volume":" ","pages":"8953996261439087"},"PeriodicalIF":1.4,"publicationDate":"2026-04-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147786920","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":"Time-resolved tomography algorithm using one projection per time step: Non-monotonic case.","authors":"Maxim Grigoriev, Alexey Buzmakov","doi":"10.1177/08953996261439073","DOIUrl":"https://doi.org/10.1177/08953996261439073","url":null,"abstract":"<p><p>This paper presents an iterative algorithm for reconstruction of dynamic non-monotonic process in a three-dimensional object (4D tomography) using only one projection per each time step. In this case, a priori knowledge about the unchanging (initial) structure of the object is used, and the processing of intermediate data at each iteration is performed in the projection data space. The proposed algorithm is compared with the previously proposed algorithm for monotonic processes and its extensions for non-monotonic processes, where the processing of intermediate data in the space of reconstructed objects is used. Virtual experiments have been conducted to reconstruct the dynamics of voxels values changes in time. The advantage of the new approach in both speed and quality of reconstruction is shown. The influence of the internal structure of dynamic regions on the reconstruction quality is investigated. The new 4D tomography algorithm presented in this paper shows the possibility of reconstructing a dynamic process in a three-dimensional volume using only one projection per time step, which can be useful in different fields of science such as materials science, geology, medicine, etc. The proposed algorithm is demonstrated on an unambiguous mathematical phantom, serving as a proof of concept. Its application to more complex or real experimental data will require further study and adaptation.</p>","PeriodicalId":49948,"journal":{"name":"Journal of X-Ray Science and Technology","volume":" ","pages":"8953996261439073"},"PeriodicalIF":1.4,"publicationDate":"2026-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147693279","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":"Two-stage universal liver cancer segmentation network for 3D dual-modality abdominal nuclear medical images based on mixed-label and multi-type training strategy.","authors":"Ziran Chen, Yingjian Yang, Mengting Feng, Jie Zheng, Zongbo Dai, Longyu Li, Zewei Wu, Asim Zaman, Guangtao Huang, Yang Liu, Yun Liu, Peng Guo, Jialin Zhang, Huai Chen, Yan Kang","doi":"10.1177/08953996261439100","DOIUrl":"https://doi.org/10.1177/08953996261439100","url":null,"abstract":"<p><p>BackgroundExisting methods for segmenting liver cancer from single-modal medical images fail to effectively leverage potential correlations across modalities. These correlations between the anatomical structures of liver cancer and the liver are also crucial for accurate liver cancer segmentation. These challenges not only limit the performance and scalability of liver cancer segmentation models but also pose significant challenges for researchers seeking to develop multimodal, low-annotation-dependent solutions. Therefore, it is necessary to propose a universal liver cancer segmentation network for abdominal computed tomography (CT) and magnetic resonance (MR) medical images.MethodBased on the above, we propose a two-stage universal liver cancer segmentation network for 3D dual-modality abdominal nuclear medical images using a mixed-label, multi-type training strategy. In stage 1, two CT and MR liver segmentation models are trained to generate liver mask images for CT and MR multimodal abdominal images without liver mask label images, thereby solving the laborious technical problem of liver labeling. In stage 2, a mixed-label strategy is proposed, where a mixed-label pool is constructed from CT and MR liver mask images generated by the aforementioned liver segmentation models, along with liver label images and their corresponding liver cancer label images. Subsequently, a universal liver cancer segmentation model is trained using a mixed-label, multi-type training strategy that fully considers potential correlations among different medical imaging modalities, liver cancer, and the liver.ResultsThe proposed liver cancer segmentation model, based on Medformer and the proposed mixed-label, multi-type abdominal-image training strategy, performs best, validating the effectiveness of the proposed strategy.DiscussionThe proposed two-stage universal liver cancer segmentation network, based on a mixed-label and multi-type training strategy, can effectively segment liver cancer in different 3D dual-modality abdominal CT and MR images, which may become an indispensable quantitative analysis tool for liver cancer in clinical practice.</p>","PeriodicalId":49948,"journal":{"name":"Journal of X-Ray Science and Technology","volume":" ","pages":"8953996261439100"},"PeriodicalIF":1.4,"publicationDate":"2026-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147693313","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":"UMamba-Dual: A dual-branch model based on UMamba for cesarean scar disorder segmentation.","authors":"Jing Zhou, Jian Zhang, Xinran Wang, Yong Lin, Wei Xia","doi":"10.1177/08953996261439080","DOIUrl":"https://doi.org/10.1177/08953996261439080","url":null,"abstract":"<p><strong>Introduction and hypothesis: </strong>Accurate segmentation of cesarean scar disorder (CSDi) in ultrasound images is crucial for clinical diagnosis, disease monitoring, and personalized treatment. However, the ambiguous boundaries and complex anatomical structures of CSDi pose significant challenges. To address this, we propose UMamba-Dual, a dual-branch model derived from UMamba, designed to enhance segmentation performance in CSDi regions and provide reliable imaging support for clinical decision-making.</p><p><strong>Methods: </strong>UMamba-Dual integrates the strengths of two enhanced branches: Dual-Bot, incorporating squeeze-and-excitation (SE) attention, and Dual-Enc, employing a feature pyramid network (FPN) for improved feature representation and multi-scale perception. The training dataset included 1200 augmented 2D ultrasound images from 300 originals via flipping and rotation. An independent test set of 32 images was randomly selected and excluded from training and validation. Model performance was evaluated using Dice similarity coefficient, Intersection over Union (IoU), and Normalized Surface Dice (NSD), and compared with classical segmentation models such as nnUNet and VM-UNet.</p><p><strong>Results: </strong>UMamba-Dual achieved superior performance with Dice = 0.832, IoU = 0.782, and NSD = 0.788, consistently outperforming both classical models (UNet, nnUNet) and the recent VM-UNet, as well as the internal baselines (UMamba_Bot, UMamba_Enc).</p><p><strong>Conclusions: </strong>UMamba-Dual enables more accurate and robust segmentation of CSDi regions in ultrasound images, particularly in cases characterized by ambiguous boundaries or irregular anatomical structures. These results highlight its potential for reliable clinical application.</p>","PeriodicalId":49948,"journal":{"name":"Journal of X-Ray Science and Technology","volume":" ","pages":"8953996261439080"},"PeriodicalIF":1.4,"publicationDate":"2026-04-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147678141","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":"Branchless distance-driven and hybrid projectors in iterative cone beam CT.","authors":"Ville-Veikko Wettenhovi, Ari Hietanen, Nargiza Djurabekova, Kati Niinimäki, Marko Vauhkonen, Ville Kolehmainen","doi":"10.1177/08953996261433875","DOIUrl":"https://doi.org/10.1177/08953996261433875","url":null,"abstract":"<p><p>PurposeIterative model-based image reconstruction algorithms in cone beam computed tomography (CBCT) require repetitive forward and backward projection operations. We compare the quality of the branchless distance-driven (BDD) projector in iterative CBCT reconstruction with ray- and voxel-based methods in both regular and low-dose examinations, and introduce a hybrid approach that aims at faster computation by using the BDD as the backprojector only. We also demonstrate the potential of the BDD in FDK reconstructions.ApproachTwo measured and one simulated datasets are used. Contrast-to-noise ratio (CNR) and modulation transfer function values are computed for one measured dataset. The structural similarity index (SSIM) and peak signal-to-noise ratio (PSNR) are computed with the simulated data.ResultsBased on our results, BDD has reduced noise and better CNR compared to the other methods, with the quality dependent on the scanner geometry. The CNR is improved by 21% with the BDD and 4% with the hybrid method. BDD improves SSIM values by approximately 3.3% in the lowest dose case and 1% in the highest dose case, while for PSNR the values are 5% - 10% better. For the hybrid method, the SSIM improvements range from 0.8% - 2.2%, and the PSNR from 3.7% - 6.6 %. The hybrid method with the BDD as a backprojector can be computationally twice faster with similar image quality.ConclusionsThe hybrid projector is a good choice as a compromise between image quality and computation time. Furthermore, BDD and the hybrid projector are better choices in low-dose CBCT reconstructions.</p>","PeriodicalId":49948,"journal":{"name":"Journal of X-Ray Science and Technology","volume":" ","pages":"8953996261433875"},"PeriodicalIF":1.4,"publicationDate":"2026-04-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147678100","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":"Image quality and radiation dose assessment of Thai<b>-</b>made 2D and 3D dental extraoral imaging scanners.","authors":"Saowapak S Thongvigitmanee, Kittipong Kasantikul, Chalinee Thanasupsombat, Walita Narkbuakaew, Atthasak Kiang-Ia, Sorapong Aootaphao, Parinya Junhunee, Kongyot Wangkaoom, Satid Rukkhong, Duangkamol Banarsarn, Saowanee Iamsiri, Nattawut Sinsuebphon, Pairash Thajchayapong","doi":"10.1177/08953996261439086","DOIUrl":"https://doi.org/10.1177/08953996261439086","url":null,"abstract":"<p><p>In comparison to conventional medical computed tomography (CT), cone-beam CT (CBCT) has become widely used in dental and maxillofacial applications due to its accurate 3D information, high resolution, minimal radiation dose, and affordable machine cost. In this study, we investigated the image quality and radiation doses of dental CBCT and X-ray machines developed in Thailand. Our in-house reconstruction algorithm including artifact reduction was based on GPU calculations of filtered backprojection and was significantly faster than a CPU-based algorithm. The image quality aspects for CBCT were evaluated in terms of high contrast resolution, gray value uniformity, noise, and geometric accuracy, while image quality assessment for 2D images included high contrast resolution, low contrast levels, and distortion rate. Radiation doses were measured and calculated for the dose-area product (DAP). The technical image quality and radiation dose assessment was compared with those of other commercial extraoral imaging machines. The findings demonstrate that, when compared to other units, the proposed 2D and 3D extraoral imaging systems yielded comparable technical image quality and radiation doses. Based on these results, the Thai-made 2D and 3D extraoral imaging machines appear suitable for further clinical evaluation.</p>","PeriodicalId":49948,"journal":{"name":"Journal of X-Ray Science and Technology","volume":" ","pages":"8953996261439086"},"PeriodicalIF":1.4,"publicationDate":"2026-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147647289","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 X-ray image denoising via the synergy of linear attention and convolution.","authors":"Yue Fei, Xiaolong Zheng, Wangyang Tong, Ji Hu, Huanhuan Wu, Liang Zheng","doi":"10.1177/08953996261439076","DOIUrl":"https://doi.org/10.1177/08953996261439076","url":null,"abstract":"<p><p>X-ray imaging technology, as the core non-invasive inspection method, plays an irreplaceable role in industrial non-destructive testing and medical diagnosis. However, during signal acquisition, the imaging system faces multiple interferences, such as the quantum effect and electronic noise. This leads to a significant decrease in the image's signal-to-noise ratio, seriously affecting the accuracy of hazardous material identification and lesion detection. Existing X-ray image denoising methods have two major limitations. First, in physical model-driven denoising methods, the existing noise models deviate significantly from realistic ones, resulting in poor denoising results. Second, in mainstream deep learning-based methods, Convolutional Neural Networks (CNNs) have limitations in capturing long-range dependencies, while the Transformer model with a global receptive field has high computational complexity. To address these challenges, a physically grounded noise model is designed for synthesizing realistic X-ray images, trained on the public mainstream X-ray image security inspection datasets and augmented with hybrid real-synthetic data. Based on this, a novel denoising model, XDenoiser, is proposed in this paper. It incorporates a linear attention complexity Receptance Weighted Key-Value (RWKV) into a Transformer-based image restoration structure and combines it with CNNs to support both global and local receptive fields. Experiments on the expanded mainstream X-ray image security inspection datasets demonstrate the reasonableness and effectiveness of the XDenoiser algorithm.</p>","PeriodicalId":49948,"journal":{"name":"Journal of X-Ray Science and Technology","volume":" ","pages":"8953996261439076"},"PeriodicalIF":1.4,"publicationDate":"2026-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147647301","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":"Improved L1/L2 minimization algorithm for segmental limited-angle CT reconstruction.","authors":"Changcheng Gong, Hongxia Wang, Jie Chen","doi":"10.1177/08953996261439063","DOIUrl":"https://doi.org/10.1177/08953996261439063","url":null,"abstract":"<p><p>Computed tomography (CT) technology is widely used in medical imaging, industrial non-destructive testing, and archaeological exploration. However, its application is often limited by issues such as high radiation doses or long scanning time. This paper utilizes the segmental limited-angle (SLA) sampling strategy to address these issues. However, SLA projections inherit limited-angle sampling properties, and shading artifacts still exist in the reconstructed images. To address this issue, we introduce the L1/L2 ratio of image gradients as a regularization term into SLA CT to construct a reconstruction model. The L1/L2 ratio is scale-invariant and can better approximate the L0 norm, shows great potential for improving image quality. To solve this model, we propose an improved L1/L2 minimization algorithm. First linearize the data fidelity term, and then use the Fast Fourier Transform (FFT) to accelerate the computation process. Finally, we employ the alternating direction method to obtain the reconstructed image. Numerical simulations and real CT data experiments demonstrate that the L1/L2 method outperforms other competing methods, and it can effectively preserve image structures and some details.</p>","PeriodicalId":49948,"journal":{"name":"Journal of X-Ray Science and Technology","volume":" ","pages":"8953996261439063"},"PeriodicalIF":1.4,"publicationDate":"2026-04-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147640326","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":"Low-dose CT reconstruction network based on the unfoldment of second-order TGV.","authors":"Hangqi Wu, Pengcheng Zhang, Shu Li, Jing Lu, Yi Liu, Jiaqi Kang, Zhiguo Gui","doi":"10.1177/08953996261440879","DOIUrl":"https://doi.org/10.1177/08953996261440879","url":null,"abstract":"<p><p>BackgroundTotal generalized variation (TGV) based CT iterative reconstruction algorithm has the ability to effectively suppress the staircase effects caused by the piecewise constant assumption of total variation regularization. By unrolling the model-based iterative reconstruction to networks, the deep unrolling approach can further improve image quality within a finite number of iterations by data-driven training. However, most deep unrolling approaches focus on unrolling the data fidelity term into deep neural networks, which limit the performance of the deep unrolling approach.ObjectiveTo address this issue, we unrolled both the data fidelity term and the TGV term to construct a novel low-dose CT reconstruction network, called TGV based deep unrolling approach (TGV-DU).MethodsThe Chambolle-Pock algorithm was employed to solve the TGV based CT iterative reconstruction problem to obtain a single-loop CT iterative reconstruction algorithm, which is easy to be unrolled to neural networks. In the proposed algorithm, the parameterized mapping that updates primal variables and dual variables across successive iterations was implemented by convolutional neural networks and was dynamically learned from big data.ResultsTo validate the effectiveness of our proposed algorithm, we perform the experiment on the \"Low-Does CT Image and Projection Data\" dataset. The results show that the proposed TGV-DU outperforms other state-of-the-art methods quantitatively and qualitatively.ConclusionsExperiments show that our proposed algorithm can effectively alleviate the piecewise smoothness while preserve more structural details.</p>","PeriodicalId":49948,"journal":{"name":"Journal of X-Ray Science and Technology","volume":" ","pages":"8953996261440879"},"PeriodicalIF":1.4,"publicationDate":"2026-04-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147640377","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}