| Wang Mingyue,Wu Yan,Zhou Yue,Dong Junqiang,Gao Jianbo.Application of deep learning reconstruction algorithm combined with low-dose CT for screening opportunistic osteoporosis[J].Chinese Journal of Radiological Medicine and Protection,2023,43(11):923-928 |
| Application of deep learning reconstruction algorithm combined with low-dose CT for screening opportunistic osteoporosis |
| Received:May 06, 2023 |
| DOI：10.3760/cma.j.cn112271-20230506-00224 |
| KeyWords:Quantitative CT Bone mineral density Deep learning Image quality |
| FundProject:河南省高端外国专家引进计划项目(HNGD2022033) |
| Author Name | Affiliation | E-mail | | Wang Mingyue | Department of Radiology, First Affiliated Hospital of Zhengzhou University, Zhengzhou 450052, China | | | Wu Yan | Department of Radiology, First Affiliated Hospital of Zhengzhou University, Zhengzhou 450052, China | | | Zhou Yue | Department of Radiology, First Affiliated Hospital of Zhengzhou University, Zhengzhou 450052, China | | | Dong Junqiang | Department of Radiology, First Affiliated Hospital of Zhengzhou University, Zhengzhou 450052, China | | | Gao Jianbo | Department of Radiology, First Affiliated Hospital of Zhengzhou University, Zhengzhou 450052, China | cjr.gaojianbo@vip.163.com |
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| Abstract:: |
| Objective To explore the influence of deep learning reconstruction algorithm combined with low-dose CT on image quality and bone mineral density measurement and the application value in opportunistic osteoporosis screening.Methods A total of 119 patients (aged ≥40 years) who underwent a combined chest and upper abdominal low-dose scan were prospectively included. All the images were reconstructed using filtered back projection(FBP) alogrithm, hybrid model-based adaptive statistical iterative reconstruction (ASIR-V) 50% and three levels of deep learning reconstruction algorithm respectively. Bone mineral density (BMD) values for different reconstruction conditions were measured and compared using asynchronous quantitative CT software. The signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) of descending aorta, liver and spleen were calculated, and the image noise was the standard deviation of anterior abdominal wall fat and the image quality was objectively evaluated by using the five-point subjective evaluation method. The objective and subjective image quality of different body parts with different reconstruction method was compared.Results There was no statistical difference in BMD with different reconstruction method (P > 0.05). Compared with ASIR-V 50%, the SNRs of high level deep learning image reconstruction (DLIR-H)in descending aorta, latissimus dorsi, liver and spleen were increased by 103.88%, 125.09% and 136.13% respectively, and the image noise was decreased by 55.98%. Both the CNR and subjective scores (except the ability to display lung lesions) of DLIR-H were better than those of DLIR-L and ASIR-V 50% (χ2 =158.31-275.35, P<0.001).Conclusions The deep learning algorithm does not affect the accuracy of bone mineral density measurement, and the image quality is better than that of ASIR-V 5%. Deep learning algorithm combined with low-dose CT can be used for opportunistic osteoporosis screening. |
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