TY - JOUR
T1 - A comprehensive survey on deep learning techniques in CT image quality improvement
AU - Li, Disen
AU - Ma, Limin
AU - Li, Jining
AU - Qi, Shouliang
AU - Yao, Yudong
AU - Teng, Yueyang
N1 - Publisher Copyright:
© 2022, International Federation for Medical and Biological Engineering.
PY - 2022/10
Y1 - 2022/10
N2 - High-quality computed tomography (CT) images are key to clinical diagnosis. However, the current quality of an image is limited by reconstruction algorithms and other factors and still needs to be improved. When using CT, a large quantity of imaging data, including intermediate data and final images, that can reflect important physical processes in a statistical sense are accumulated. However, traditional imaging techniques cannot make full use of them. Recently, deep learning, in which the large quantity of imaging data can be utilized and patterns can be learned by a hierarchical structure, has provided new ideas for CT image quality improvement. Many researchers have proposed a large number of deep learning algorithms to improve CT image quality, especially in the field of image postprocessing. This survey reviews these algorithms and identifies future directions. Graphical abstract: [Figure not available: see fulltext.]
AB - High-quality computed tomography (CT) images are key to clinical diagnosis. However, the current quality of an image is limited by reconstruction algorithms and other factors and still needs to be improved. When using CT, a large quantity of imaging data, including intermediate data and final images, that can reflect important physical processes in a statistical sense are accumulated. However, traditional imaging techniques cannot make full use of them. Recently, deep learning, in which the large quantity of imaging data can be utilized and patterns can be learned by a hierarchical structure, has provided new ideas for CT image quality improvement. Many researchers have proposed a large number of deep learning algorithms to improve CT image quality, especially in the field of image postprocessing. This survey reviews these algorithms and identifies future directions. Graphical abstract: [Figure not available: see fulltext.]
KW - Deep learning
KW - Image denoising
KW - Metal artifact correction
KW - Super-resolution imaging
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U2 - 10.1007/s11517-022-02631-y
DO - 10.1007/s11517-022-02631-y
M3 - Review article
AN - SCOPUS:85136104510
SN - 0140-0118
VL - 60
SP - 2757
EP - 2770
JO - Medical and Biological Engineering and Computing
JF - Medical and Biological Engineering and Computing
IS - 10
ER -