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
  • [1] Deep Learning-Based Reconstruction Improves the Image Quality of Low-Dose CT Colonography
    Chen, Yanshan
    Huang, Zixuan
    Feng, Lijuan
    Zou, Wenbin
    Kong, Decan
    Zhu, Dongyun
    Dai, Guochao
    Zhao, Weidong
    Zhang, Yuanke
    Luo, Mingyue
    ACADEMIC RADIOLOGY, 2024, 31 (08) : 3191 - 3199
  • [2] Image quality improvement in low-dose chest CT with deep learning image reconstruction
    Tian, Qian
    Li, Xinyu
    Li, Jianying
    Cheng, Yannan
    Niu, Xinyi
    Zhu, Shumeng
    Xu, Wenting
    Guo, Jianxin
    JOURNAL OF APPLIED CLINICAL MEDICAL PHYSICS, 2022, 23 (12):
  • [3] Deep learning-based reconstruction improves the image quality of low-dose CT enterography in patients with inflammatory bowel disease
    He, Weitao
    Xu, Ping
    Zhang, Mengchen
    Xu, Rulin
    Shen, Xiaodi
    Mao, Ren
    Li, Xue-hua
    Sun, Can-hui
    Zhang, Ruo-nan
    Lin, Shaochun
    ABDOMINAL RADIOLOGY, 2024,
  • [4] Low-Dose CT Image Reconstruction With a Deep Learning Prior
    Park, Hyoung Suk
    Kim, Kyungsang
    Jeon, Kiwan
    IEEE ACCESS, 2020, 8 : 158647 - 158655
  • [5] Low-dose liver CT: image quality and diagnostic accuracy of deep learning image reconstruction algorithm
    Damiano Caruso
    Domenico De Santis
    Antonella Del Gaudio
    Gisella Guido
    Marta Zerunian
    Michela Polici
    Daniela Valanzuolo
    Dominga Pugliese
    Raffaello Persechino
    Antonio Cremona
    Luca Barbato
    Andrea Caloisi
    Elsa Iannicelli
    Andrea Laghi
    European Radiology, 2024, 34 : 2384 - 2393
  • [6] Low-dose liver CT: image quality and diagnostic accuracy of deep learning image reconstruction algorithm
    Caruso, Damiano
    De Santis, Domenico
    Del Gaudio, Antonella
    Guido, Gisella
    Zerunian, Marta
    Polici, Michela
    Valanzuolo, Daniela
    Pugliese, Dominga
    Persechino, Raffaello
    Cremona, Antonio
    Barbato, Luca
    Caloisi, Andrea
    Iannicelli, Elsa
    Laghi, Andrea
    EUROPEAN RADIOLOGY, 2024, 34 (04) : 2384 - 2393
  • [7] The Value of Deep Learning Image Reconstruction in Improving the Quality of Low-Dose Chest CT Images
    Jiang, Jiu-Ming
    Miao, Lei
    Liang, Xin
    Liu, Zhuo-Heng
    Zhang, Li
    Li, Meng
    DIAGNOSTICS, 2022, 12 (10)
  • [8] Application of deep learning image reconstruction in low-dose chest CT scan
    Wang, Huang
    Li, Lu-Lu
    Shang, Jin
    Song, Jian
    Liu, Bin
    BRITISH JOURNAL OF RADIOLOGY, 2022, 95 (1133):
  • [9] Image Quality and Diagnostic Performance of Low-Dose Liver CT with Deep Learning Reconstruction versus Standard-Dose CT
    Lee, Dong Ho
    Lee, Jeong Min
    Lee, Chang Hee
    Afat, Saif
    Othman, Ahmed
    RADIOLOGY-ARTIFICIAL INTELLIGENCE, 2024, 6 (02)
  • [10] Low-Dose Temporal Bone CT in Infants and Young Children: Effective Dose and Image Quality
    Nauer, C. B.
    Rieke, A.
    Zubler, C.
    Candreia, C.
    Arnold, A.
    Senn, P.
    AMERICAN JOURNAL OF NEURORADIOLOGY, 2011, 32 (08) : 1375 - 1380