Application of Super-Resolution Convolutional Neural Network for Enhancing Image Resolution in Chest CT
書誌事項
- 公開日
- 2017-10-18
- 資源種別
- journal article
- 権利情報
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- http://www.springer.com/tdm
- DOI
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- 10.1007/s10278-017-0033-z
- 公開者
- Springer Science and Business Media LLC
この論文をさがす
説明
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.
収録刊行物
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- Journal of Digital Imaging
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Journal of Digital Imaging 31 (4), 441-450, 2017-10-18
Springer Science and Business Media LLC