Deep learning reconstruction improves the image quality of low-dose temporal bone CT with otitis media and mastoiditis patients

被引:1
|
作者
Wang, Tian-Jiao [1 ]
Wang, Yun [1 ]
Zhang, Zhu-Hua [1 ]
Wang, Ming [1 ]
Wang, Man [1 ]
Su, Tong [1 ]
Xu, Ying-Hao [2 ]
Ma, Zhuang-Fei [2 ]
Wang, Jian [2 ]
Chen, Yu [1 ]
Jin, Zheng-Yu [1 ]
机构
[1] Chinese Acad Med Sci, Peking Union Med Coll Hosp, Dept Radiol, 1 Shuai Fu Yuan, Beijing 100730, Peoples R China
[2] Canon Med Syst China CO LTD, Bldg 205,Yard A10,JiuXianQiao North Rd, Beijing 100015, Peoples R China
基金
中国国家自然科学基金;
关键词
Deep learning; Temporal bone; Radiation dosage; Multidetector computed tomography; Otitis media; Mastoiditis; ITERATIVE RECONSTRUCTION; STRATEGIES;
D O I
10.1016/j.heliyon.2023.e22810
中图分类号
O [数理科学和化学]; P [天文学、地球科学]; Q [生物科学]; N [自然科学总论];
学科分类号
07 ; 0710 ; 09 ;
摘要
Objective: To evaluate the image quality of low-dose temporal bone computed tomography (CT) in otitis media and mastoiditis patients by using deep learning reconstruction (DLR).Materials and methods: A total of ninety-seven temporal bones from 53 consecutive adult patients who had suspected otitis media and mastoiditis and underwent temporal bone CT were prospectively enrolled. All patients underwent high resolution CT protocol (group A) and an additional low-dose protocol (group B). In group A, high resolution data were reconstructed by filter back projection (FBP). In group B, low-dose data were reconstructed by DLR mild (B1), DLR standard (B2) and DLR strong (B3). The objective image quality was analyzed by measuring the CT value and image noise on the transverse image and calculating the signal-to-noise ratio (SNR) on incudomallear joint, retroauricular muscle, vestibule and subcutaneous fat. Subjective image quality was analyzed by using a five-point scale to evaluate nine anatomical structures of middle and inner ear. The number of temporal bone lesions which involved in five structures of middle ear were assessed in group A, B1, B2 and B3 images.Results: There were no significant differences in the CT values of the four reconstruction methods at four structures (all p > 0.05). The DLR group B1, B2 and B3 had significantly less image noise and a significantly higher SNR than group A at four structures (all p < 0.001). The group B1 had comparable subjective image quality as group A in nine structures (all p > 0.05), however, the group B3 had lower subjective image quality than group A in modiolus, spiral osseous lamina and stapes (all p < 0.001), the group B2 had lower subjective image quality than group A in modiolus and spiral osseous lamina (both p < 0.05). The number of temporal bone lesions which involved in five structures for group A, B1 and B2 images were no significant difference (all p > 0.05), however, the number of temporal bone lesions which involved in mastoid for group B3 images were significantly more than group A (p < 0.05). The radiation dose of high resolution CT protocol and low-dose protocol were 0.55 mSv and 0.11 mSv, respectively.Conclusion: Compared with high resolution CT protocol, in the low-dose protocol of temporal bone CT, DLR mild and standard could improve the objective image quality, maintain good subjective image quality and satisfy clinical diagnosis of otitis media and mastoiditis patients.
引用
收藏
页数:10
相关论文
共 50 条
  • [31] A feasibility study of realizing low-dose abdominal CT using deep learning image reconstruction algorithm
    Li, Lu-Lu
    Wang, Huang
    Song, Jian
    Shang, Jin
    Zhao, Xiao-Ying
    Liu, Bin
    JOURNAL OF X-RAY SCIENCE AND TECHNOLOGY, 2021, 29 (02) : 361 - 372
  • [32] Low-dose CT urography using deep learning image reconstruction: a prospective study for comparison with conventional CT urography
    Cheng, Yannan
    Han, Yangyang
    Li, Jianying
    Fan, Ganglian
    Cao, Le
    Li, Junjun
    Jia, Xiaoqian
    Yang, Jian
    Guo, Jianxin
    BRITISH JOURNAL OF RADIOLOGY, 2021, 94 (1120):
  • [33] Image Quality Assessment of Low-Dose CT using Hybrid Iterative Reconstruction
    Zhang, Qiao
    Sheng, Jinhua
    Chen, Bin
    JOURNAL OF IMAGING SCIENCE AND TECHNOLOGY, 2021, 65 (06)
  • [34] Impact of iterative reconstruction on image quality of low-dose CT of the lumbar spine
    Alshamari, Muhammed
    Geijer, Mats
    Norrman, Eva
    Liden, Mats
    Krauss, Wolfgang
    Jendeberg, Johan
    Magnuson, Anders
    Geijer, Hakan
    ACTA RADIOLOGICA, 2017, 58 (06) : 702 - 709
  • [35] A Self-supervised Deep Learning Network for Low-Dose CT Reconstruction
    Liang, Kaichao
    Zhang, Li
    Yang, Yirong
    Yang, Hongkai
    Xing, Yuxiang
    2018 IEEE NUCLEAR SCIENCE SYMPOSIUM AND MEDICAL IMAGING CONFERENCE PROCEEDINGS (NSS/MIC), 2018,
  • [36] Low-dose CT Image Reconstruction via Total Variation and Dictionary Learning
    Zhao, XianYu
    Guo, JinXu
    PROCEEDINGS OF 2019 IEEE 9TH INTERNATIONAL CONFERENCE ON ELECTRONICS INFORMATION AND EMERGENCY COMMUNICATION (ICEIEC 2019), 2019, : 248 - 251
  • [37] Adversarial EM for variational deep learning: Application to semi-supervised image quality enhancement in low-dose PET and low-dose CT
    Sharma, Vatsala
    Awate, Suyash P.
    MEDICAL IMAGE ANALYSIS, 2024, 97
  • [38] Unbiased statistical image reconstruction in low-dose CT
    Hayes, John
    Zhang, Ran
    Zhang, Chengzhu
    Gomez-Cardona, Daniel
    Chen, Guang-Hong
    MEDICAL IMAGING 2019: PHYSICS OF MEDICAL IMAGING, 2019, 10948
  • [39] Delineation of temporal bone anatomy: feasibility of low-dose 64-row CT in regard to image quality
    Jürgen Lutz
    Vanessa Jäger
    Martin John Hempel
    Sudesh Srivastav
    Maximilian Reiser
    Lorenz Jäger
    European Radiology, 2007, 17 : 2638 - 2645
  • [40] Delineation of temporal bone anatomy:: feasibility of low-dose 64-row CT in regard to image quality
    Lutz, Juergen
    Jaeger, Vanessa
    Hempel, Martin John
    Srivastav, Sudesh
    Reiser, Maximilian
    Jaeger, Lorenz
    EUROPEAN RADIOLOGY, 2007, 17 (10) : 2638 - 2645