Application of deep learning reconstruction of ultra-low-dose abdominal CT in the diagnosis of renal calculi
Abstract Background Renal calculi are a common and recurrent urological disease and are usually detected by CT. In this study, we evaluated the diagnostic capability, image quality, and radiation dose of abdominal ultra-low-dose CT (ULDCT) with deep learning reconstruction (DLR) for detecting renal...
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| Format: | Article |
| Language: | English |
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SpringerOpen
2022-10-01
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| Series: | Insights into Imaging |
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| Online Access: | https://doi.org/10.1186/s13244-022-01300-w |
| _version_ | 1797996075782504448 |
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| author | Xiaoxiao Zhang Gumuyang Zhang Lili Xu Xin Bai Jiahui Zhang Min Xu Jing Yan Daming Zhang Zhengyu Jin Hao Sun |
| author_facet | Xiaoxiao Zhang Gumuyang Zhang Lili Xu Xin Bai Jiahui Zhang Min Xu Jing Yan Daming Zhang Zhengyu Jin Hao Sun |
| author_sort | Xiaoxiao Zhang |
| collection | DOAJ |
| description | Abstract Background Renal calculi are a common and recurrent urological disease and are usually detected by CT. In this study, we evaluated the diagnostic capability, image quality, and radiation dose of abdominal ultra-low-dose CT (ULDCT) with deep learning reconstruction (DLR) for detecting renal calculi. Methods Sixty patients with suspected renal calculi were prospectively enrolled. Low-dose CT (LDCT) images were reconstructed with hybrid iterative reconstruction (LD-HIR) and was regarded as the standard for stone and lesion detection. ULDCT images were reconstructed with HIR (ULD-HIR) and DLR (ULD-DLR). We then compared stone detection rate, abdominal lesion detection rate, image quality and radiation dose between LDCT and ULDCT. Results A total of 130 calculi were observed on LD-HIR images. Stone detection rates of ULD-HIR and ULD-DLR images were 93.1% (121/130) and 95.4% (124/130). A total of 129 lesions were detected on the LD-HIR images. The lesion detection rate on ULD-DLR images was 92.2%, with 10 cysts < 5 mm in diameter missed. The CT values of organs on ULD-DLR were similar to those on LD-HIR and lower than those on ULD-HIR. Signal-to-noise ratio was highest and noise lowest on ULD-DLR. The subjective image quality of ULD-DLR was similar to that of LD-HIR and better than that of ULD-HIR. The effective radiation dose of ULDCT (0.64 ± 0.17 mSv) was 77% lower than that of LDCT (2.75 ± 0.50 mSv). Conclusion ULDCT combined with DLR could significantly reduce radiation dose while maintaining suitable image quality and stone detection rate in the diagnosis of renal calculi. |
| first_indexed | 2024-04-11T10:11:49Z |
| format | Article |
| id | doaj.art-6fa787cefc824464a8c1e57659bdf0ee |
| institution | Directory Open Access Journal |
| issn | 1869-4101 |
| language | English |
| last_indexed | 2024-04-11T10:11:49Z |
| publishDate | 2022-10-01 |
| publisher | SpringerOpen |
| record_format | Article |
| series | Insights into Imaging |
| spelling | doaj.art-6fa787cefc824464a8c1e57659bdf0ee2022-12-22T04:30:04ZengSpringerOpenInsights into Imaging1869-41012022-10-011311910.1186/s13244-022-01300-wApplication of deep learning reconstruction of ultra-low-dose abdominal CT in the diagnosis of renal calculiXiaoxiao Zhang0Gumuyang Zhang1Lili Xu2Xin Bai3Jiahui Zhang4Min Xu5Jing Yan6Daming Zhang7Zhengyu Jin8Hao Sun9Department of Radiology, State Key Laboratory of Complex Severe and Rare Disease, Peking Union Medical College Hospital, Peking Union Medical College, Chinese Academy of Medical SciencesDepartment of Radiology, State Key Laboratory of Complex Severe and Rare Disease, Peking Union Medical College Hospital, Peking Union Medical College, Chinese Academy of Medical SciencesDepartment of Radiology, State Key Laboratory of Complex Severe and Rare Disease, Peking Union Medical College Hospital, Peking Union Medical College, Chinese Academy of Medical SciencesDepartment of Radiology, State Key Laboratory of Complex Severe and Rare Disease, Peking Union Medical College Hospital, Peking Union Medical College, Chinese Academy of Medical SciencesDepartment of Radiology, State Key Laboratory of Complex Severe and Rare Disease, Peking Union Medical College Hospital, Peking Union Medical College, Chinese Academy of Medical SciencesCanon Medical System (China)Canon Medical System (China)Department of Radiology, State Key Laboratory of Complex Severe and Rare Disease, Peking Union Medical College Hospital, Peking Union Medical College, Chinese Academy of Medical SciencesDepartment of Radiology, State Key Laboratory of Complex Severe and Rare Disease, Peking Union Medical College Hospital, Peking Union Medical College, Chinese Academy of Medical SciencesDepartment of Radiology, State Key Laboratory of Complex Severe and Rare Disease, Peking Union Medical College Hospital, Peking Union Medical College, Chinese Academy of Medical SciencesAbstract Background Renal calculi are a common and recurrent urological disease and are usually detected by CT. In this study, we evaluated the diagnostic capability, image quality, and radiation dose of abdominal ultra-low-dose CT (ULDCT) with deep learning reconstruction (DLR) for detecting renal calculi. Methods Sixty patients with suspected renal calculi were prospectively enrolled. Low-dose CT (LDCT) images were reconstructed with hybrid iterative reconstruction (LD-HIR) and was regarded as the standard for stone and lesion detection. ULDCT images were reconstructed with HIR (ULD-HIR) and DLR (ULD-DLR). We then compared stone detection rate, abdominal lesion detection rate, image quality and radiation dose between LDCT and ULDCT. Results A total of 130 calculi were observed on LD-HIR images. Stone detection rates of ULD-HIR and ULD-DLR images were 93.1% (121/130) and 95.4% (124/130). A total of 129 lesions were detected on the LD-HIR images. The lesion detection rate on ULD-DLR images was 92.2%, with 10 cysts < 5 mm in diameter missed. The CT values of organs on ULD-DLR were similar to those on LD-HIR and lower than those on ULD-HIR. Signal-to-noise ratio was highest and noise lowest on ULD-DLR. The subjective image quality of ULD-DLR was similar to that of LD-HIR and better than that of ULD-HIR. The effective radiation dose of ULDCT (0.64 ± 0.17 mSv) was 77% lower than that of LDCT (2.75 ± 0.50 mSv). Conclusion ULDCT combined with DLR could significantly reduce radiation dose while maintaining suitable image quality and stone detection rate in the diagnosis of renal calculi.https://doi.org/10.1186/s13244-022-01300-wAbdominal CTUltra-low-dose CTRenal calculiDeep learning reconstruction |
| spellingShingle | Xiaoxiao Zhang Gumuyang Zhang Lili Xu Xin Bai Jiahui Zhang Min Xu Jing Yan Daming Zhang Zhengyu Jin Hao Sun Application of deep learning reconstruction of ultra-low-dose abdominal CT in the diagnosis of renal calculi Insights into Imaging Abdominal CT Ultra-low-dose CT Renal calculi Deep learning reconstruction |
| title | Application of deep learning reconstruction of ultra-low-dose abdominal CT in the diagnosis of renal calculi |
| title_full | Application of deep learning reconstruction of ultra-low-dose abdominal CT in the diagnosis of renal calculi |
| title_fullStr | Application of deep learning reconstruction of ultra-low-dose abdominal CT in the diagnosis of renal calculi |
| title_full_unstemmed | Application of deep learning reconstruction of ultra-low-dose abdominal CT in the diagnosis of renal calculi |
| title_short | Application of deep learning reconstruction of ultra-low-dose abdominal CT in the diagnosis of renal calculi |
| title_sort | application of deep learning reconstruction of ultra low dose abdominal ct in the diagnosis of renal calculi |
| topic | Abdominal CT Ultra-low-dose CT Renal calculi Deep learning reconstruction |
| url | https://doi.org/10.1186/s13244-022-01300-w |
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