Journal of X-Ray Science and Technology最新文献

{"title":"PET/CT radiomics for non-invasive prediction of immunotherapy efficacy in cervical cancer.","authors":"Tianming Du, Chen Li, Marcin Grzegozek, Xinyu Huang, Md Rahaman, Xinghao Wang, Hongzan Sun","doi":"10.1177/08953996251367203","DOIUrl":"https://doi.org/10.1177/08953996251367203","url":null,"abstract":"<p><p>PurposeThe prediction of immunotherapy efficacy in cervical cancer patients remains a critical clinical challenge. This study aims to develop and validate a deep learning-based automatic tumor segmentation method on PET/CT images, extract texture features from the tumor regions in cervical cancer patients, and investigate their correlation with PD-L1 expression. Furthermore, a predictive model for immunotherapy efficacy will be constructed.MethodsWe retrospectively collected data from 283 pathologically confirmed cervical cancer patients who underwent ¹⁸F-FDG PET/CT examinations, divided into three subsets. Subset-I (n = 97) was used to develop a deep learning-based segmentation model using Attention-UNet and region-growing methods on co-registered PET/CT images. Subset-II (n = 101) was used to explore correlations between radiomic features and PD-L1 expression. Subset-III (n = 85) was used to construct and validate a radiomic model for predicting immunotherapy response.ResultsUsing Subset-I, a segmentation model was developed. The segmentation model achieved optimal performance at the 94th epoch with an IoU of 0.746 in the validation set. Manual evaluation confirmed accurate tumor localization. Sixteen features demonstrated excellent reproducibility (ICC > 0.75). Using Subset-II, PD-L1-correlated features were extracted and identified. In Subset-II, 183 features showed significant correlations with PD-L1 expression (P < 0.05).Using these features in Subset-III, a predictive model for immunotherapy efficacy was constructed and evaluated. In Subset-III, the SVM-based radiomic model achieved the best predictive performance with an AUC of 0.935.ConclusionWe validated, respectively in Subset-I, Subset-II, and Subset-III, that deep learning models incorporating medical prior knowledge can accurately and automatically segment cervical cancer lesions, that texture features extracted from ¹⁸F-FDG PET/CT are significantly associated with PD-L1 expression, and that predictive models based on these features can effectively predict the efficacy of PD-L1 immunotherapy. This approach offers a non-invasive, efficient, and cost-effective tool for guiding individualized immunotherapy in cervical cancer patients and may help reduce patient burden, accelerate treatment planning.</p>","PeriodicalId":49948,"journal":{"name":"Journal of X-Ray Science and Technology","volume":" ","pages":"8953996251367203"},"PeriodicalIF":1.4,"publicationDate":"2025-08-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144976716","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":"Anatomy-aware transformer-based model for precise rectal cancer detection and localization in MRI scans.","authors":"Shanshan Li, Yu Zhang, Yao Hong, Wei Yuan, Jihong Sun","doi":"10.1177/08953996251370580","DOIUrl":"https://doi.org/10.1177/08953996251370580","url":null,"abstract":"<p><p>Rectal cancer is a major cause of cancer-related mortality, requiring accurate diagnosis via MRI scans. However, detecting rectal cancer in MRI scans is challenging due to image complexity and the need for precise localization. While transformer-based object detection has excelled in natural images, applying these models to medical data is hindered by limited medical imaging resources. To address this, we propose the Spatially Prioritized Detection Transformer (SP DETR), which incorporates a Spatially Prioritized (SP) Decoder to constrain anchor boxes to regions of interest (ROI) based on anatomical maps, focusing the model on areas most likely to contain cancer. Additionally, the SP cross-attention mechanism refines the learning of anchor box offsets. To improve small cancer detection, we introduce the Global Context-Guided Feature Fusion Module (GCGFF), leveraging a transformer encoder for global context and a Globally-Guided Semantic Fusion Block (GGSF) to enhance high-level semantic features. Experimental results show that our model significantly improves detection accuracy, especially for small rectal cancers, demonstrating the effectiveness of integrating anatomical priors with transformer-based models for clinical applications.</p>","PeriodicalId":49948,"journal":{"name":"Journal of X-Ray Science and Technology","volume":" ","pages":"8953996251370580"},"PeriodicalIF":1.4,"publicationDate":"2025-08-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144976725","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":"Generalization of parallel ghost imaging based on laboratory X-ray source.","authors":"Nixi Zhao, Junxiong Fang, Jie Tang, Changzhe Zhao, Jianwen Wu, Han Guo, Haipeng Zhang, Tiqiao Xiao","doi":"10.1177/08953996251367214","DOIUrl":"https://doi.org/10.1177/08953996251367214","url":null,"abstract":"<p><p>Ghost imaging is an imaging technique that achieves image reconstruction by measuring the intensity correlation function between the reference arm and the object arm. In parallel ghost imaging, each pixel of a position-sensitive detector is further regarded as a bucket detector, enabling the parallel acquisition of hundreds or thousands of ghost imaging subsystems in a single measurement, thus realizing high-resolution imaging with extremely low measurement counts. Relying on synchrotron radiation, we have achieved X-ray parallel ghost imaging with high pixel resolution, low dose, and ultra-large field of view. However, the dependence of X-ray parallel ghost imaging on synchrotron radiation has set extremely high thresholds for the dissemination and application of this technology. In this work, we broke away from synchrotron radiation facility and completed the pipeline-style acquisition of parallel ghost imaging using rough and inexpensive equipment in the most reproducible way for others. Eventually, we achieved ghost imaging with an effective pixel size of 8.03 μm, an image size of 2880 × 2280, and a minimum of 10 measurement numbers (a sampling rate of 0.62%) using a laboratory X-ray light source. It can be achieved merely by making minor modifications to any industrial CT device. With a total experimental cost of only $40, this work demonstrates great universality. We have put forward a comprehensive framework for the practical application of parallel ghost imaging, which is an essential prerequisite for the generalization of parallel ghost imaging to enter the commercial and practical arenas.</p>","PeriodicalId":49948,"journal":{"name":"Journal of X-Ray Science and Technology","volume":" ","pages":"8953996251367214"},"PeriodicalIF":1.4,"publicationDate":"2025-08-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144976657","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":"Radiomics meets transformers: A novel approach to tumor segmentation and classification in mammography for breast cancer.","authors":"Mohamed J Saadh, Qusay Mohammed Hussain, Rafid Jihad Albadr, Hardik Doshi, M M Rekha, Mayank Kundlas, Amrita Pal, Jasur Rizaev, Waam Mohammed Taher, Mariem Alwan, Mahmod Jasem Jawad, Ali M Ali Al-Nuaimi, Bagher Farhood","doi":"10.1177/08953996251351624","DOIUrl":"https://doi.org/10.1177/08953996251351624","url":null,"abstract":"<p><p>ObjectiveThis study aimed to develop a robust framework for breast cancer diagnosis by integrating advanced segmentation and classification approaches. Transformer-based and U-Net segmentation models were combined with radiomic feature extraction and machine learning classifiers to improve segmentation precision and classification accuracy in mammographic images.Materials and MethodsA multi-center dataset of 8000 mammograms (4200 normal, 3800 abnormal) was used. Segmentation was performed using Transformer-based and U-Net models, evaluated through Dice Coefficient (DSC), Intersection over Union (IoU), Hausdorff Distance (HD95), and Pixel-Wise Accuracy. Radiomic features were extracted from segmented masks, with Recursive Feature Elimination (RFE) and Analysis of Variance (ANOVA) employed to select significant features. Classifiers including Logistic Regression, XGBoost, CatBoost, and a Stacking Ensemble model were applied to classify tumors into benign or malignant. Classification performance was assessed using accuracy, sensitivity, F1 score, and AUC-ROC. SHAP analysis validated feature importance, and Q-value heatmaps evaluated statistical significance.ResultsThe Transformer-based model achieved superior segmentation results with DSC (0.94 ± 0.01 training, 0.92 ± 0.02 test), IoU (0.91 ± 0.01 training, 0.89 ± 0.02 test), HD95 (3.0 ± 0.3 mm training, 3.3 ± 0.4 mm test), and Pixel-Wise Accuracy (0.96 ± 0.01 training, 0.94 ± 0.02 test), consistently outperforming U-Net across all metrics. For classification, Transformer-segmented features with the Stacking Ensemble achieved the highest test results: 93% accuracy, 92% sensitivity, 93% F1 score, and 95% AUC. U-Net-segmented features achieved lower metrics, with the best test accuracy at 84%. SHAP analysis confirmed the importance of features like Gray-Level Non-Uniformity and Zone Entropy.ConclusionThis study demonstrates the superiority of Transformer-based segmentation integrated with radiomic feature selection and robust classification models. The framework provides a precise and interpretable solution for breast cancer diagnosis, with potential for scalability to 3D imaging and multimodal datasets.</p>","PeriodicalId":49948,"journal":{"name":"Journal of X-Ray Science and Technology","volume":" ","pages":"8953996251351624"},"PeriodicalIF":1.4,"publicationDate":"2025-07-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144734878","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":"Self-Assessment of acute rib fracture detection system from chest X-ray: Preliminary study for early radiological diagnosis.","authors":"Hong Kyu Lee, Hyoung Soo Kim, Sung Gyun Kim, Jae Yong Park","doi":"10.1177/08953996251361041","DOIUrl":"https://doi.org/10.1177/08953996251361041","url":null,"abstract":"<p><p>ObjectiveDetecting and accurately diagnosing rib fractures in chest radiographs is a challenging and time-consuming task for radiologists. This study presents a novel deep learning system designed to automate the detection and segmentation of rib fractures in chest radiographs.MethodsThe proposed method combines CenterNet with HRNet v2 for precise fracture region identification and HRNet-W48 with contextual representation to enhance rib segmentation. A dataset consisting of 1006 chest radiographs from a tertiary hospital in Korea was used, with a split of 7:2:1 for training, validation, and testing.ResultsThe rib fracture detection component achieved a sensitivity of 0.7171, indicating its effectiveness in identifying fractures. Additionally, the rib segmentation performance was measured by a dice score of 0.86, demonstrating its accuracy in delineating rib structures. Visual assessment results further highlight the model's capability to pinpoint fractures and segment ribs accurately.ConclusionThis innovative approach holds promise for improving rib fracture detection and rib segmentation, offering potential benefits in clinical practice for more efficient and accurate diagnosis in the field of medical image analysis.</p>","PeriodicalId":49948,"journal":{"name":"Journal of X-Ray Science and Technology","volume":" ","pages":"8953996251361041"},"PeriodicalIF":1.4,"publicationDate":"2025-07-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144734879","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":"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":"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":1.4,"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":"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":1.4,"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":"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":1.4,"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":"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":1.4,"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}