ArticleJournal of digital imaging2018
Application of Super-Resolution Convolutional Neural Network for Enhancing Image Resolution in Chest CT.
Article in Journal of digital imaging, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 52 papers, 1 of them a synthesis that pooled it.
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Who cites it
52 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Image Quality Standardization in Radiomics: A Systematic Review of Artifacts, Variability, and Feature Stability.Sensors (Basel, Switzerland) · 2026Pooled it
- Convolutional Neural Networks in Radiology: Principles, Clinical Applications, and a Practical Framework for Radiologists.Medical principles and practice : international journal of the Kuwait University, Health Science Centre · 2026Review
- Soft Robotics and Advanced Technologies for Minimally Invasive Bioprinting: The Future of Internal Organ Repair.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Review
- Evaluating Super-Resolution Models in Biomedical Imaging: Applications and Performance in Segmentation and Classification.Journal of imaging · 2025Article
- Finite Element Method-Based Hybrid MRI/CBCT Generation to Improve Liver Stereotactic Body Radiation Therapy Targets Localization Accuracy.IEEE transactions on radiation and plasma medical sciences · 2025Article
- [The challenging patient-recommendations and solutions].Radiologie (Heidelberg, Germany) · 2024Review
- An AI-Based Low-Risk Lung Health Image Visualization Framework Using LR-ULDCT.Journal of imaging informatics in medicine · 2024Article
- Super-resolution deep-learning reconstruction for cardiac CT: impact of radiation dose and focal spot size on task-based image quality.Physical and engineering sciences in medicine · 2024Article
- Deep Learning and Neural Architecture Search for Optimizing Binary Neural Network Image Super Resolution.Biomimetics (Basel, Switzerland) · 2024Article
- Super-resolution techniques for biomedical applications and challenges.Biomedical engineering letters · 2024Review
- Sixty-four-fold data reduction of chest radiographs using a super-resolution convolutional neural network.The British journal of radiology · 2024Article
- ACL-DUNet: A tumor segmentation method based on multiple attention and densely connected breast ultrasound images.PloS one · 2024Article
- Super-Resolution Reconstruction of CT Images Based on Multi-scale Information Fused Generative Adversarial Networks.Annals of biomedical engineering · 2024Article
- Applicability Evaluation of Full-Reference Image Quality Assessment Methods for Computed Tomography Images.Journal of digital imaging · 2023Article
- Improving image quality with super-resolution deep-learning-based reconstruction in coronary CT angiography.European radiology · 2023Article
- Improved Detection of Urolithiasis Using High-Resolution Computed Tomography Images by a Vision Transformer Model.International neurourology journal · 2023Article
- A Super-Resolution Diffusion Model for Recovering Bone Microstructure from CT Images.Radiology. Artificial intelligence · 2023Article
- Breast Ultrasound Images Augmentation and Segmentation Using GAN with Identity Block and Modified U-Net 3.Sensors (Basel, Switzerland) · 2023Article
- Milestones in CT: Past, Present, and Future.Radiology · 2023Review
- Deep learning-based computed tomographic image super-resolution via wavelet embedding.Radiation physics and chemistry (Oxford, England : 1993) · 2023Article
Corrections and comments
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3 authors.
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Abstract
In this study, the super-resolution convolutional neural network (SRCNN) scheme, which is the emerging deep-learning-based super-resolution method for enhancing image resolution in chest CT images, was applied and evaluated using the post-processing approach. For evaluation, 89 chest CT cases were sampled from The Cancer Imaging Archive. The 89 CT cases were divided randomly into 45 training cases and 44 external test cases. The SRCNN was trained using the training dataset. With the trained SRCNN, a high-resolution image was reconstructed from a low-resolution image, which was down-sampled from an original test image. For quantitative evaluation, two image quality metrics were measured and compared to those of the conventional linear interpolation methods. The image restoration quality of the SRCNN scheme was significantly higher than that of the linear interpolation methods (p < 0.001 or p < 0.05). The high-resolution image reconstructed by the SRCNN scheme was highly restored and comparable to the original reference image, in particular, for a ×2 magnification. These results indicate that the SRCNN scheme significantly outperforms the linear interpolation methods for enhancing image resolution in chest CT images. The results also suggest that SRCNN may become a potential solution for generating high-resolution CT images from standard CT images.
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