Quantitative Evaluation of Noise Texture of Deep-Learning-Based CT Reconstruction with An Anthropomorphic Phantom

被引:0
|
作者
Yang, K. [1 ]
Cao, J. [1 ]
Pisuchpen, N. [1 ]
Marschall, T. [1 ]
Li, X. [1 ]
Gupta, R. [1 ]
Kambadakone, A. [1 ]
Liu, B. [1 ]
机构
[1] Massachusetts Gen Hosp, Boston, MA 02114 USA
关键词
D O I
暂无
中图分类号
R8 [特种医学]; R445 [影像诊断学];
学科分类号
1002 ; 100207 ; 1009 ;
摘要
TU-IePD-TR
引用
收藏
页数:1
相关论文
共 50 条
  • [1] Development of an anthropomorphic multimodality pelvic phantom for quantitative evaluation of a deep-learning-based synthetic computed tomography generation technique
    Jin, Hyeongmin
    Lee, Sung Young
    An, Hyun Joon
    Choi, Chang Heon
    Chie, Eui Kyu
    Wu, Hong-Gyun
    Park, Jong Min
    Park, Sukwon
    Kim, Jung-in
    JOURNAL OF APPLIED CLINICAL MEDICAL PHYSICS, 2022, 23 (08):
  • [2] A Deep-Learning-Based Quality Control Evaluation Method for CT Phantom Images
    Hwang, Hoseong
    Kim, Donghyun
    Kim, Hochul
    APPLIED SCIENCES-BASEL, 2024, 14 (05):
  • [3] Quantitative Evaluation of Image Quality of Deep-Learning-Based CT Reconstruction Using Structural SIMilarity (SSIM)
    Yang, K.
    Parakh, A.
    Gupta, R.
    Kambadakone, A.
    Li, X.
    Liu, B.
    MEDICAL PHYSICS, 2020, 47 (06) : E549 - E550
  • [4] Deep-Learning-Based CT Imaging in the Quantitative Evaluation of Chronic Kidney Diseases
    Fu, Xu
    Liu, Huaiqin
    Bi, Xiaowang
    Gong, Xiao
    JOURNAL OF HEALTHCARE ENGINEERING, 2021, 2021
  • [5] Influence of deep learning reconstruction on task-based model observer performance in CT: an anthropomorphic head phantom study
    Hernandez-Giron, Irene
    Kaasalainen, Touko
    Makela, Teemu
    Peltonen, Juha
    Kortesniemi, Mika
    MEDICAL IMAGING 2022: IMAGE PERCEPTION, OBSERVER PERFORMANCE, AND TECHNOLOGY ASSESSMENT, 2022, 12035
  • [6] Deep-learning-based porous media microstructure quantitative characterization and reconstruction method
    Huang, Yubo
    Xiang, Zhong
    Qian, Miao
    PHYSICAL REVIEW E, 2022, 105 (01)
  • [7] An anthropomorphic phantom for quantitative evaluation of breast MRI
    Freed, Melanie
    de Zwart, Jacco A.
    Loud, Jennifer T.
    El Khouli, Riham H.
    Myers, Kyle J.
    Greene, Mark H.
    Duyn, Jeff H.
    Badano, Aldo
    MEDICAL PHYSICS, 2011, 38 (02) : 743 - 753
  • [8] Deep Learning Reconstruction at CT: Phantom Study of the Image Characteristics
    Higaki, Toru
    Nakamura, Yuko
    Zhou, Jian
    Yu, Zhou
    Nemoto, Takuya
    Tatsugami, Fuminari
    Awai, Kazuo
    ACADEMIC RADIOLOGY, 2020, 27 (01) : 82 - 87
  • [9] Quantitative Yttrium-90 PET/CT acquisition and reconstruction optimization: An anthropomorphic phantom validation.
    Gnesin, S.
    Paterne, L.
    Boubaker, A.
    Adib, S.
    Pappon, M.
    Kosinski, M.
    Prior, J.
    Baechler, S.
    Verdun, F.
    EUROPEAN JOURNAL OF NUCLEAR MEDICINE AND MOLECULAR IMAGING, 2015, 42 : S150 - S150
  • [10] Deep-Learning-Based Image Reconstruction and Enhancement in Optical Microscopy
    de Haan, Kevin
    Rivenson, Yair
    Wu, Yichen
    Ozcan, Aydogan
    PROCEEDINGS OF THE IEEE, 2020, 108 (01) : 30 - 50