图像质量
计算机科学
人工智能
深度学习
质量(理念)
领域(数学)
图像(数学)
钥匙(锁)
质量管理
计算机断层摄影术
机器学习
计算机视觉
医学物理学
模式识别(心理学)
数据挖掘
医学
数学
放射科
工程类
认识论
哲学
计算机安全
管理制度
纯数学
运营管理
作者
Disen Li,Limin Ma,Jining Li,Shouliang Qi,Yudong Yao,Yueyang Teng
标识
DOI:10.1007/s11517-022-02631-y
摘要
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.
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