Application of deep learning reconstruction at prone position chest scanning of early interstitial lung disease
Abstract Aim Timely intervention of interstitial lung disease (ILD) was promising for attenuating the lung function decline and improving clinical outcomes. The prone position HRCT is essential for early diagnosis of ILD, but limited by its high radiation exposure. This study was aimed to explore wh...
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| Format: | Article |
| Language: | English |
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BMC
2025-08-01
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| Series: | BMC Medical Imaging |
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| Online Access: | https://doi.org/10.1186/s12880-025-01871-2 |
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| author | Ruijie Zhao Yun Wang Jiaru Wang Zixing Wang Ran Xiao Ying Ming Sirong Piao Jinhua Wang Lan Song Yinghao Xu Zhuangfei Ma Peilin Fan Xin Sui Wei Song |
| author_facet | Ruijie Zhao Yun Wang Jiaru Wang Zixing Wang Ran Xiao Ying Ming Sirong Piao Jinhua Wang Lan Song Yinghao Xu Zhuangfei Ma Peilin Fan Xin Sui Wei Song |
| author_sort | Ruijie Zhao |
| collection | DOAJ |
| description | Abstract Aim Timely intervention of interstitial lung disease (ILD) was promising for attenuating the lung function decline and improving clinical outcomes. The prone position HRCT is essential for early diagnosis of ILD, but limited by its high radiation exposure. This study was aimed to explore whether deep learning reconstruction (DLR) could keep the image quality and reduce the radiation dose compared with hybrid iterative reconstruction (HIR) in prone position scanning for patients of early-stage ILD. Methods This study prospectively enrolled 21 patients with early-stage ILD. All patients underwent high-resolution CT (HRCT) and low-dose CT (LDCT) scans. HRCT images were reconstructed with HIR using standard settings, and LDCT images were reconstructed with DLR (lung/bone kernel) in a mild, standard, or strong setting. Overall image quality, image noise, streak artifacts, and visualization of normal and abnormal ILD features were analysed. Results The effective dose of LDCT was 1.22 ± 0.09 mSv, 63.7% less than the HRCT dose. The objective noise of the LDCT DLR images was 35.9–112.6% that of the HRCT HIR images. The LDCT DLR was comparable to the HRCT HIR in terms of overall image quality. LDCT DLR (bone, strong) visualization of bronchiectasis and/or bronchiolectasis was significantly weaker than that of HRCT HIR (p = 0.046). The LDCT DLR (all settings) did not significantly differ from the HRCT HIR in the evaluation of other abnormal features, including ground glass opacities (GGOs), architectural distortion, reticulation and honeycombing. Conclusion With 63.7% reduction of radiation dose, the overall image quality of LDCT DLR was comparable to HRCT HIR in prone scanning for early ILD patients. This study supported that DLR was promising for maintaining image quality under a lower radiation dose in prone scanning, and it offered valuable insights for the selection of images reconstruction algorithms for the diagnosis and follow-up of early ILD. |
| format | Article |
| id | doaj-art-9bb13b7511d24bc28772bc561b44af0a |
| institution | Kabale University |
| issn | 1471-2342 |
| language | English |
| publishDate | 2025-08-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Medical Imaging |
| spelling | doaj-art-9bb13b7511d24bc28772bc561b44af0a2025-08-24T11:57:38ZengBMCBMC Medical Imaging1471-23422025-08-0125111010.1186/s12880-025-01871-2Application of deep learning reconstruction at prone position chest scanning of early interstitial lung diseaseRuijie Zhao0Yun Wang1Jiaru Wang2Zixing Wang3Ran Xiao4Ying Ming5Sirong Piao6Jinhua Wang7Lan Song8Yinghao Xu9Zhuangfei Ma10Peilin Fan11Xin Sui12Wei Song13Department of Radiology, Peking Union Medical College Hospital, Chinese Academy of Medical SciencesDepartment of Radiology, Peking Union Medical College Hospital, Chinese Academy of Medical SciencesDepartment of Radiology, Peking Union Medical College Hospital, Chinese Academy of Medical SciencesDepartment of Epidemiology and Biostatistics, Institute of Basic Medical Sciences, School of Basic Medicine, Chinese Academy of Medical Sciences, Peking Union Medical CollegeDepartment of Radiology, Peking Union Medical College Hospital, Chinese Academy of Medical SciencesDepartment of Radiology, Peking Union Medical College Hospital, Chinese Academy of Medical SciencesDepartment of Radiology, Peking Union Medical College Hospital, Chinese Academy of Medical SciencesDepartment of Radiology, Peking Union Medical College Hospital, Chinese Academy of Medical SciencesDepartment of Radiology, Peking Union Medical College Hospital, Chinese Academy of Medical SciencesCanon Medical System (China)Canon Medical System (China)Department of Radiology, Peking Union Medical College Hospital, Chinese Academy of Medical SciencesDepartment of Radiology, Peking Union Medical College Hospital, Chinese Academy of Medical SciencesDepartment of Radiology, Peking Union Medical College Hospital, Chinese Academy of Medical SciencesAbstract Aim Timely intervention of interstitial lung disease (ILD) was promising for attenuating the lung function decline and improving clinical outcomes. The prone position HRCT is essential for early diagnosis of ILD, but limited by its high radiation exposure. This study was aimed to explore whether deep learning reconstruction (DLR) could keep the image quality and reduce the radiation dose compared with hybrid iterative reconstruction (HIR) in prone position scanning for patients of early-stage ILD. Methods This study prospectively enrolled 21 patients with early-stage ILD. All patients underwent high-resolution CT (HRCT) and low-dose CT (LDCT) scans. HRCT images were reconstructed with HIR using standard settings, and LDCT images were reconstructed with DLR (lung/bone kernel) in a mild, standard, or strong setting. Overall image quality, image noise, streak artifacts, and visualization of normal and abnormal ILD features were analysed. Results The effective dose of LDCT was 1.22 ± 0.09 mSv, 63.7% less than the HRCT dose. The objective noise of the LDCT DLR images was 35.9–112.6% that of the HRCT HIR images. The LDCT DLR was comparable to the HRCT HIR in terms of overall image quality. LDCT DLR (bone, strong) visualization of bronchiectasis and/or bronchiolectasis was significantly weaker than that of HRCT HIR (p = 0.046). The LDCT DLR (all settings) did not significantly differ from the HRCT HIR in the evaluation of other abnormal features, including ground glass opacities (GGOs), architectural distortion, reticulation and honeycombing. Conclusion With 63.7% reduction of radiation dose, the overall image quality of LDCT DLR was comparable to HRCT HIR in prone scanning for early ILD patients. This study supported that DLR was promising for maintaining image quality under a lower radiation dose in prone scanning, and it offered valuable insights for the selection of images reconstruction algorithms for the diagnosis and follow-up of early ILD.https://doi.org/10.1186/s12880-025-01871-2Deep learning reconstructionInterstitial lung diseaseProne positionLow doseImage quality |
| spellingShingle | Ruijie Zhao Yun Wang Jiaru Wang Zixing Wang Ran Xiao Ying Ming Sirong Piao Jinhua Wang Lan Song Yinghao Xu Zhuangfei Ma Peilin Fan Xin Sui Wei Song Application of deep learning reconstruction at prone position chest scanning of early interstitial lung disease BMC Medical Imaging Deep learning reconstruction Interstitial lung disease Prone position Low dose Image quality |
| title | Application of deep learning reconstruction at prone position chest scanning of early interstitial lung disease |
| title_full | Application of deep learning reconstruction at prone position chest scanning of early interstitial lung disease |
| title_fullStr | Application of deep learning reconstruction at prone position chest scanning of early interstitial lung disease |
| title_full_unstemmed | Application of deep learning reconstruction at prone position chest scanning of early interstitial lung disease |
| title_short | Application of deep learning reconstruction at prone position chest scanning of early interstitial lung disease |
| title_sort | application of deep learning reconstruction at prone position chest scanning of early interstitial lung disease |
| topic | Deep learning reconstruction Interstitial lung disease Prone position Low dose Image quality |
| url | https://doi.org/10.1186/s12880-025-01871-2 |
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