| Cui Jinjin,Liu Guanzhong,Hu Xinghe,Han Shaojun,Sun Hong,Wang Xinjiang,Yao Hongxiang.Feasibility study on deep learning image reconstruction algorithm to improve the quality of low-dose CT images of the brain[J].Chinese Journal of Radiological Medicine and Protection,2023,43(9):736-740 |
| Feasibility study on deep learning image reconstruction algorithm to improve the quality of low-dose CT images of the brain |
| Received:June 02, 2023 |
| DOI：10.3760/cma.j.cn112271-20230602-00174 |
| KeyWords:Deep learning image reconstruction Adaptive statistical iterative reconstruction-V Low radiation dose Image quality Lacunar infarction |
| FundProject:国家自然科学基金面上项目（82172018）；军队保健专项课题（21BJZ21）；科技创新2030（2022ZD0211600） |
| Author Name | Affiliation | E-mail | | Cui Jinjin | National Clinical Research Center for Geriatric Diseases, Department of Radiology, the Second Medical Center of the PLA General Hospital, Beijing 100853, China | | | Liu Guanzhong | National Clinical Research Center for Geriatric Diseases, Department of Radiology, the Second Medical Center of the PLA General Hospital, Beijing 100853, China | | | Hu Xinghe | National Clinical Research Center for Geriatric Diseases, Department of Radiology, the Second Medical Center of the PLA General Hospital, Beijing 100853, China | | | Han Shaojun | National Clinical Research Center for Geriatric Diseases, Department of Radiology, the Second Medical Center of the PLA General Hospital, Beijing 100853, China | | | Sun Hong | National Clinical Research Center for Geriatric Diseases, Department of Radiology, the Second Medical Center of the PLA General Hospital, Beijing 100853, China | | | Wang Xinjiang | National Clinical Research Center for Geriatric Diseases, Department of Radiology, the Second Medical Center of the PLA General Hospital, Beijing 100853, China | | | Yao Hongxiang | National Clinical Research Center for Geriatric Diseases, Department of Radiology, the Second Medical Center of the PLA General Hospital, Beijing 100853, China | yaohx301@163.com |
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| Abstract:: |
| Objective To explore the effectiveness of deep learning image reconstruction (DLIR) algorithm compared to adaptive statistical iterative reconstruction (ASIR-V) algorithm in improving the quality of low-dose brain CT images.Methods Retrospective inclusion of patients who underwent brain CT examination in the People's Liberation Army General Hospital from November 2021 to August 2022. Four different algorithms were used to reconstruct low-dose CT scans of all patients to obtain 30% intensity ASIR-V (ASIR-V-30%) images, low intensity DLIR (DLIR-L) images, medium intensity DLIR (DLIR-M) images, and high intensity DLIR (DLIR-H) images. The regions of interest were selected from four sets of images, including superficial white matter, superficial gray matter, deep white matter, and deep gray matter, and their CT values and standard deviations were measured for calculating signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR).Subjective evaluation of image quality was conducted by three neuroimaging physicians based on the Likert 5-component scale. The objective and subjective scores of the 4 groups of images were analyzed using ANOVA or Kruskal Wallis. If there are overall differences, pairwise comparisons were conducted within the group.Results A total of 109 patients were enrolled, including 104 males and 5 females, aged 65-110 years (89.16 ±9.53) years. The radiation exposure of brain CT low-dose scanning was (0.93 ±0.01)mSv, significantly lower than that of conventional scanning (2.92 ±0.01) mSv (t=56.15, P < 0.05). The differences in objective image quality analysis of ASIR-V-30%, DLIR-L, DLIR-M, and DLIR-H images of low-dose CT in SNRdeep gray matter, SNR deep white matter, SNR superficial gray matter, SNR superficial white matter, CNR deep gray white matter, and CNRsuperficial gray white matter were statistically significant(F=98.23, 72.95, 68.43, 58.24, 241.13, 289.91, P < 0.05). Among them, DLIR-H images had the lowest noise in deep gray matter, deep white matter, superficial gray matter, and superficial white matter, and had statistically significant differences compared to other image groups (t=167.43, 275.46, 182.32, 361.54, P < 0.05). The subjective score of DLIR-H image quality was superior to ASIR-V-30%, DLIR-L, and DLIR-M, with the statistically significant difference (t=7.25, 8.32, 9.63, P < 0.05).Conclusions Compared with ASIR-V, DLIR algorithm can effectively reduce image noise and artifacts in low-dose brain CT, and improve SNR and CNR. The subjective and objective image quality evaluation of DLIR-H is the best. |
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