NSTL国家科技图书文献中心

1. Quantifying Low-Velocity Impact Damage of UHMWPE/CNT Composites Qualitatively Using X-ray Computed Tomography NSTL国家科技图书文献中心

Omar Banabila | Haleimah Alabdouli ... - 《Computational and Experimental Simulations in Engineering,Volume 2》 - International Conference on Computational & Experimental Engineering and Sciences - 2025, - 210～220 - 共11页

摘要： microstructural evolutions were carried out using X-ray computed | tomography (XCT) to capture the damaged area and volume | The demand for flexible, lightweight body | armor made with Ultra-High Molecular Weight | Polyethylene (UHMWPE) is in an uptrend due to its superior

关键词： UHMWPE | MWCNT | Low velocity impact | X-ray computed tomography

2. Plug-and-Play Learned Proximal Trajectory for 3D Sparse-View X-Ray Computed Tomography NSTL国家科技图书文献中心

Romain Vo | Julie Escoda ... - 《Computer Vision - ECCV 2024,Part XXXV》 - European Conference on Computer Vision - 2025, - 221～238 - 共18页

摘要： sparse-view X-ray* computed tomography (CT). We propose* | scalability of our approach on two 3D Cone-Beam CT datasets | Plug-and-Play algorithms (PnP) have recently | emerged as a powerful framework for solving inverse | problems in imaging. They leverage the power of Gaussian

关键词： Deep learning | Sparse-View computed tomography | Inverse problem | Regularization | Plug-and-Play

3. Study of Volumetric Display using Computed Tomography Back-Projection Method NSTL国家科技图书文献中心

M. Yamada | M. Nishizawa ... - 《International Workshop on Advanced Imaging Technology (IWAIT) 2025》 - International Workshop on Advanced Imaging Technology - 2025, - 1351015.1～1351015.5 - 共5页

摘要：-projection method of Computed* Tomography, it is* | This study proposes a method for creating | projection images for projectors in a volumetric display | that presents 3D images by overlapping projection | rays. Conventional methods have limited the presented

关键词： Augmented reality | Fog screen | Volumetric display | Laser | Computed tomography

4. Patch-free Tooth Segmentation from Cone Beam Computed Tomography Dataset NSTL国家科技图书文献中心

Benxiang Jiang | Songze Zhang ... - 《Sixteenth International Conference on Graphics and Image Processing (ICGIP 2024)》 - International Conference on Graphics and Image Processing - 2025, - 135390L.1～135390L.6 - 共6页

摘要：Tooth segmentation from cone beam computed | tomography (CBCT) datasets is a crucial step in generating | three-dimensional tooth models. Mainstream tooth | segmentation methods randomly or uniformly cut CBCT datasets | into several regions without anatomical significance

关键词： Tooth segmentation | CBCT | Patch-free | Whole tooth region localization | U-Net

5. MsNet: Multi-stage Learning from Seldom Labeled Data for 3D Tooth Segmentation in Dental Cone Beam Computed Tomography NSTL国家科技图书文献中心

Xuewei Kang | Bingjiang Qiu ... - 《Semi-supervised Tooth Segmentation》 - MICCAI Challenge on Semi-supervised Tooth Segmentation | International Conference on Medical Image Computing and Computer Assisted Intervention - 2025, - 72～82 - 共11页

摘要： teeth in dental cone beam computed* tomography (CBCT* | Automatic and accurate 3D segmentation of | ) images is a prerequisite for computer-aided dental | analysis. However, due to variations in dental anatomy | , different imaging protocols, and limitations in accessing

关键词： Tooth segmentation | Semi-supervised learning | Cone beam computed tomography | Deep learning

6. Deep Learning Approach to Temporal Dimensionality Reduction of Volumetric Computed Tomography NSTL国家科技图书文献中心

Lucas Almeida da Sil... | Eulanda Miranda dos ...... - 《Intelligent Systems,Part I》 - Brazilian Conference on Intelligent Systems - 2025, - 296～309 - 共14页

摘要： computed tomography volumes by evaluating the activation | A common approach for analyzing medical images | on volumetric data is to employ deep 2D | convolutional neural networks (2D CNN) on each individual | slice of the volume. This is largely attributed to the

关键词： Pattern recognition | Feature selection | CT classification | CT slice selection

7. CTextureFusion: Advanced Texture Transfer with Multi-head Attention for Improving Lung CT Super Resolution NSTL国家科技图书文献中心

Mario Amoros | Manuel Curado ... - 《Pattern Recognition,Part XI》 - International Conference on Pattern Recognition - 2025, - 96～111 - 共16页

摘要： medical images are available, with computed tomography | (CT) standing out as effective tools for pathology | lung CT images by combining advanced super-resolution | Lung diseases cause millions of deaths per | year worldwide. For medical diagnosis, different

关键词： Super-resolution | Imaging | Computed tomography | Lung

8. Dual-ResShift: Dual-Input Separated Features Residual Shift Diffusion Model for CTA Image Super-Resolution NSTL国家科技图书文献中心

Feng Jiang | Jing Wen ... - 《Pattern Recognition,Part XXVII》 - International Conference on Pattern Recognition - 2025, - 219～233 - 共15页

摘要：Computed tomography angiography (CTA) scans | provide doctors with an accurate visualization of the | vascular system that helps them make diagnostic decisions | . To help doctors make a quick diagnosis, the | resolution of CTA images needs to be improved. With the

关键词： Super resolution | Computed tomography angiography | Residual diffusion

9. A Multi-stage Framework for 3D Individual Tooth Segmentation in Dental CBCT NSTL国家科技图书文献中心

Chunshi Wang | Bin Zhao ... - 《Semi-supervised Tooth Segmentation》 - MICCAI Challenge on Semi-supervised Tooth Segmentation | International Conference on Medical Image Computing and Computer Assisted Intervention - 2025, - 36～45 - 共10页

摘要：Cone beam computed* tomography (CBCT) is a* | common way of diagnosing dental related diseases | . Accurate segmentation of 3D tooth is of importance for | the treatment. Although deep learning based methods | have achieved convincing results in medical image

关键词： Cone beam computed tomography | Tooth segmentation | Semi-supervised learning

10. Multilabel Classification of Intracranial Hemorrhages Using Deep Learning and Preprocessing Techniques on Non-contrast CT Images NSTL国家科技图书文献中心

Rodrigo Salas | Juan Sebastian Castr...... - 《Progress in Pattern Recognition,Image Analysis,Computer Vision,and Applications,Part II》 - Iberoamerican Congress on Pattern Recognition - 2025, - 175～190 - 共16页

摘要： tomography (CT) images. The framework includes strategies | , subarachnoid, and subdural - using non-contrast computed | dataset of over 25,000 non-contrast CT scans, each | This study presents a comprehensive framework | that integrates a deep learning model with advanced

摘要： microstructural evolutions were carried out using X-ray computed | tomography (XCT) to capture the damaged area and volume | The demand for flexible, lightweight body | armor made with Ultra-High Molecular Weight | Polyethylene (UHMWPE) is in an uptrend due to its superior

关键词： UHMWPE | MWCNT | Low velocity impact | X-ray computed tomography

摘要： sparse-view X-ray* computed tomography (CT). We propose* | scalability of our approach on two 3D Cone-Beam CT datasets | Plug-and-Play algorithms (PnP) have recently | emerged as a powerful framework for solving inverse | problems in imaging. They leverage the power of Gaussian

关键词： Deep learning | Sparse-View computed tomography | Inverse problem | Regularization | Plug-and-Play

摘要：-projection method of Computed* Tomography, it is* | This study proposes a method for creating | projection images for projectors in a volumetric display | that presents 3D images by overlapping projection | rays. Conventional methods have limited the presented

关键词： Augmented reality | Fog screen | Volumetric display | Laser | Computed tomography

摘要：Tooth segmentation from cone beam computed | tomography (CBCT) datasets is a crucial step in generating | three-dimensional tooth models. Mainstream tooth | segmentation methods randomly or uniformly cut CBCT datasets | into several regions without anatomical significance

关键词： Tooth segmentation | CBCT | Patch-free | Whole tooth region localization | U-Net

摘要： teeth in dental cone beam computed* tomography (CBCT* | Automatic and accurate 3D segmentation of | ) images is a prerequisite for computer-aided dental | analysis. However, due to variations in dental anatomy | , different imaging protocols, and limitations in accessing

关键词： Tooth segmentation | Semi-supervised learning | Cone beam computed tomography | Deep learning

摘要： computed tomography volumes by evaluating the activation | A common approach for analyzing medical images | on volumetric data is to employ deep 2D | convolutional neural networks (2D CNN) on each individual | slice of the volume. This is largely attributed to the

关键词： Pattern recognition | Feature selection | CT classification | CT slice selection

摘要： medical images are available, with computed tomography | (CT) standing out as effective tools for pathology | lung CT images by combining advanced super-resolution | Lung diseases cause millions of deaths per | year worldwide. For medical diagnosis, different

关键词： Super-resolution | Imaging | Computed tomography | Lung

摘要：Computed tomography angiography (CTA) scans | provide doctors with an accurate visualization of the | vascular system that helps them make diagnostic decisions | . To help doctors make a quick diagnosis, the | resolution of CTA images needs to be improved. With the

关键词： Super resolution | Computed tomography angiography | Residual diffusion

摘要：Cone beam computed* tomography (CBCT) is a* | common way of diagnosing dental related diseases | . Accurate segmentation of 3D tooth is of importance for | the treatment. Although deep learning based methods | have achieved convincing results in medical image

关键词： Cone beam computed tomography | Tooth segmentation | Semi-supervised learning

摘要： tomography (CT) images. The framework includes strategies | , subarachnoid, and subdural - using non-contrast computed | dataset of over 25,000 non-contrast CT scans, each | This study presents a comprehensive framework | that integrates a deep learning model with advanced

关键词： Intracranial hemorrhage | Deep learning | Multilabel classification | Non-contrast computed tomography (CT) | Medical image processing

4008-161-200 800-990-8900

国家科技图书文献中心

摘要： microstructural evolutions were carried out using X-ray computed | tomography (XCT) to capture the damaged area and volume | The demand for flexible, lightweight body | armor made with Ultra-High Molecular Weight | Polyethylene (UHMWPE) is in an uptrend due to its superior

关键词： UHMWPE | MWCNT | Low velocity impact | X-ray computed tomography

摘要： sparse-view X-ray computed tomography (CT). We propose | scalability of our approach on two 3D Cone-Beam CT datasets | Plug-and-Play algorithms (PnP) have recently | emerged as a powerful framework for solving inverse | problems in imaging. They leverage the power of Gaussian

关键词： Deep learning | Sparse-View computed tomography | Inverse problem | Regularization | Plug-and-Play

摘要：-projection method of Computed Tomography, it is | This study proposes a method for creating | projection images for projectors in a volumetric display | that presents 3D images by overlapping projection | rays. Conventional methods have limited the presented

关键词： Augmented reality | Fog screen | Volumetric display | Laser | Computed tomography

摘要：Tooth segmentation from cone beam computed | tomography (CBCT) datasets is a crucial step in generating | three-dimensional tooth models. Mainstream tooth | segmentation methods randomly or uniformly cut CBCT datasets | into several regions without anatomical significance

关键词： Tooth segmentation | CBCT | Patch-free | Whole tooth region localization | U-Net

摘要： teeth in dental cone beam computed tomography (CBCT | Automatic and accurate 3D segmentation of | ) images is a prerequisite for computer-aided dental | analysis. However, due to variations in dental anatomy | , different imaging protocols, and limitations in accessing

关键词： Tooth segmentation | Semi-supervised learning | Cone beam computed tomography | Deep learning

摘要： computed tomography volumes by evaluating the activation | A common approach for analyzing medical images | on volumetric data is to employ deep 2D | convolutional neural networks (2D CNN) on each individual | slice of the volume. This is largely attributed to the

关键词： Pattern recognition | Feature selection | CT classification | CT slice selection

摘要： medical images are available, with computed tomography | (CT) standing out as effective tools for pathology | lung CT images by combining advanced super-resolution | Lung diseases cause millions of deaths per | year worldwide. For medical diagnosis, different

关键词： Super-resolution | Imaging | Computed tomography | Lung

摘要：Computed tomography angiography (CTA) scans | provide doctors with an accurate visualization of the | vascular system that helps them make diagnostic decisions | . To help doctors make a quick diagnosis, the | resolution of CTA images needs to be improved. With the

关键词： Super resolution | Computed tomography angiography | Residual diffusion

摘要：Cone beam computed tomography (CBCT) is a | common way of diagnosing dental related diseases | . Accurate segmentation of 3D tooth is of importance for | the treatment. Although deep learning based methods | have achieved convincing results in medical image

关键词： Cone beam computed tomography | Tooth segmentation | Semi-supervised learning

摘要： tomography (CT) images. The framework includes strategies | , subarachnoid, and subdural - using non-contrast computed | dataset of over 25,000 non-contrast CT scans, each | This study presents a comprehensive framework | that integrates a deep learning model with advanced

关键词： Intracranial hemorrhage | Deep learning | Multilabel classification | Non-contrast computed tomography (CT) | Medical image processing

4008-161-200 800-990-8900

国家科技图书文献中心

摘要： sparse-view X-ray* computed tomography (CT). We propose* | scalability of our approach on two 3D Cone-Beam CT datasets | Plug-and-Play algorithms (PnP) have recently | emerged as a powerful framework for solving inverse | problems in imaging. They leverage the power of Gaussian

摘要：-projection method of Computed* Tomography, it is* | This study proposes a method for creating | projection images for projectors in a volumetric display | that presents 3D images by overlapping projection | rays. Conventional methods have limited the presented

摘要： teeth in dental cone beam computed* tomography (CBCT* | Automatic and accurate 3D segmentation of | ) images is a prerequisite for computer-aided dental | analysis. However, due to variations in dental anatomy | , different imaging protocols, and limitations in accessing

摘要：Cone beam computed* tomography (CBCT) is a* | common way of diagnosing dental related diseases | . Accurate segmentation of 3D tooth is of importance for | the treatment. Although deep learning based methods | have achieved convincing results in medical image