Research of CT Reconstruction Algorithms Based on Compressed Sensing
YIN Juan1, FAN Yilu1, QIN Shaohua2
1. Department of Medical Engineering, Shandong Provincial Qianfoshan Hospital, Jinan Shandong 250014, China;
2. School of Physics and Electronics, Shandong Normal University, Jinan Shandong 250358, China
Abstract：Studies have shown that reducing the dose and Angle of CT scans can significantly reduce the harm of medical imaging
to patients. Under the condition of reduced projection data, traditional reconstruction algorithms can not meet the requirements of
clinical diagnosis in terms of reconstruction speed and accuracy. CT reconstruction algorithms based on compressed sensing can
effectively improve the quality of CT imaging under the condition of incomplete projection data. In this paper, the algorithms of CT
reconstruction based on compressed sensing was studied, and its mathematical model and reconstruction process were discussed. The
reconstruction methods based on two different regularization terms, sparse and prior images, were analyzed emphatically. Finally, the
impact of new technologies, such as deep learning networks and convolutional sparse coding, on CT reconstruction algorithms based
on compressed sensing were analyzed.
尹娟1，范医鲁1，秦绍华2. 基于压缩感知的CT重建算法的研究进展[J]. 中国医疗设备, 2019, 34(8): 166-169.
YIN Juan1, FAN Yilu1, QIN Shaohua2. Research of CT Reconstruction Algorithms Based on Compressed Sensing. China Medical Devices, 2019, 34(8): 166-169.
