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A novel strategy to develop deep learning for image super-resolution using original ultra-high-resolution computed tomography images of lung as training dataset.
http://hdl.handle.net/10422/00013055
http://hdl.handle.net/10422/000130559f870ff7-b35e-47cc-a3ce-ba3ae151961d
| Item type | 学術雑誌論文 / Journal Article(1) | |||||
|---|---|---|---|---|---|---|
| 公開日 | 2021-08-11 | |||||
| タイトル | ||||||
| タイトル | A novel strategy to develop deep learning for image super-resolution using original ultra-high-resolution computed tomography images of lung as training dataset. | |||||
| 言語 | ||||||
| 言語 | eng | |||||
| キーワード | ||||||
| 言語 | en | |||||
| 主題Scheme | Other | |||||
| 主題 | Artificial intelligence | |||||
| キーワード | ||||||
| 言語 | en | |||||
| 主題Scheme | Other | |||||
| 主題 | Cadaveric lung | |||||
| キーワード | ||||||
| 言語 | en | |||||
| 主題Scheme | Other | |||||
| 主題 | Convolutional neural networks | |||||
| キーワード | ||||||
| 言語 | en | |||||
| 主題Scheme | Other | |||||
| 主題 | Deep learning | |||||
| キーワード | ||||||
| 言語 | en | |||||
| 主題Scheme | Other | |||||
| 主題 | Ultra-high-resolution computed tomography | |||||
| 資源タイプ | ||||||
| 資源タイプ識別子 | http://purl.org/coar/resource_type/c_6501 | |||||
| 資源タイプ | journal article | |||||
| アクセス権 | ||||||
| アクセス権 | metadata only access | |||||
| アクセス権URI | http://purl.org/coar/access_right/c_14cb | |||||
| 著者 |
KITAHARA, Hitoshi
× KITAHARA, Hitoshi× NAGATANI, Yukihiro× OTANI, Hideji× NAKAYAMA, Ryohei× KIDA, Yukako× SONODA, Akinaga× WATANABE, Yoshiyuki |
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| 著者別名 |
北原, 均
× 北原, 均× 永谷, 幸裕× 大谷, 秀司× 木田, 友佳子× 園田, 明永× 渡邉, 嘉之 |
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| 抄録 | ||||||
| 内容記述タイプ | Abstract | |||||
| 内容記述 | Purpose: To improve the image quality of inflated fixed cadaveric human lungs by utilizing ultra-high-resolution computed tomography (U-HRCT) as a training dataset for super-resolution processing using deep learning (SR-DL). |
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| 抄録 | ||||||
| 内容記述タイプ | Abstract | |||||
| 内容記述 | Materials and methods: Image data of nine cadaveric human lungs were acquired using U-HRCT. Three different matrix images of U-HRCT images were obtained with two acquisition modes: normal mode (512-matrix image) and super-high-resolution mode (1024- and 2048-matrix image). SR-DL used 512- and 1024-matrix images as training data for deep learning. The virtual 2048-matrix images were acquired by applying SR-DL to the 1024-matrix images. Three independent observers scored normal anatomical structures and abnormal computed tomography (CT) findings of both types of 2048-matrix images on a 3-point scale compared to 1024-matrix images. The image noise values were quantitatively calculated. Moreover, the edge rise distance (ERD) and edge rise slope (ERS) were also calculated using the CT attenuation profile to evaluate margin sharpness. |
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| 抄録 | ||||||
| 内容記述タイプ | Abstract | |||||
| 内容記述 | Results: The virtual 2048-matrix images significantly improved visualization of normal anatomical structures and abnormal CT findings, except for consolidation and nodules, compared with the conventional 2048-matrix images (p < 0.01). Quantitative noise values were significantly lower in the virtual 2048-matrix images than in the conventional 2048-matrix images (p < 0.001). ERD was significantly shorter in the virtual 2048-matrix images than in the conventional 2048-matrix images (p < 0.01). ERS was significantly higher in the virtual 2048-matrix images than in the conventional 2048-matrix images (p < 0.01). |
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| 抄録 | ||||||
| 内容記述タイプ | Abstract | |||||
| 内容記述 | Conclusion: SR-DL using original U-HRCT images as a training dataset might be a promising tool for image enhancement in terms of margin sharpness and image noise reduction. By applying trained SR-DL to U-HRCT SHR mode images, virtual ultra-high-resolution images were obtained which surpassed the image quality of unmodified SHR mode images. |
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| 書誌情報 |
en : Japanese Journal of Radiology 発行日 2021-07-28 |
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| 出版者 | ||||||
| 出版者 | Springer Nature | |||||
| ISSN | ||||||
| 収録物識別子タイプ | ISSN | |||||
| 収録物識別子 | 1867-1071 | |||||
| PMID | ||||||
| 識別子タイプ | PMID | |||||
| 関連識別子 | 34318444 | |||||
| PMCID | ||||||
| 識別子タイプ | URI | |||||
| 関連識別子 | http://www.ncbi.nlm.nih.gov/pmc/articles/pmc8315896/ | |||||
| 関連名称 | PMC8315896 | |||||
| DOI | ||||||
| 識別子タイプ | DOI | |||||
| 関連識別子 | https://doi.org/10.1007/s11604-021-01184-8 | |||||
| 関連名称 | 10.1007/s11604-021-01184-8 | |||||
| 権利 | ||||||
| 権利情報 | © 2021. Japan Radiological Society. | |||||
| 資源タイプ | ||||||
| 内容記述タイプ | Other | |||||
| 内容記述 | Journal Article | |||||
