TY - JOUR
T1 - Iterator-Net
T2 - sinogram-based CT image reconstruction
AU - Ma, Limin
AU - Yao, Yudong
AU - Teng, Yueyang
N1 - Publisher Copyright:
© 2022 the Author(s), licensee AIMS Press.
PY - 2022
Y1 - 2022
N2 - Image reconstruction is extremely important for computed tomography (CT) imaging, so it is significant to be continuously improved. The unfolding dynamics method combines a deep learning model with a traditional iterative algorithm. It is interpretable and has a fast reconstruction speed, but the essence of the algorithm is to replace the approximation operator in the optimization objective with a learning operator in the form of a convolutional neural network. In this paper, we firstly design a new iterator network (iNet), which is based on the universal approximation theorem and tries to simulate the functional relationship between the former and the latter in the maximum-likelihood expectation maximization (MLEM) algorithm. To evaluate the effectiveness of the method, we conduct experiments on a CT dataset, and the results show that our iNet method improves the quality of reconstructed images.
AB - Image reconstruction is extremely important for computed tomography (CT) imaging, so it is significant to be continuously improved. The unfolding dynamics method combines a deep learning model with a traditional iterative algorithm. It is interpretable and has a fast reconstruction speed, but the essence of the algorithm is to replace the approximation operator in the optimization objective with a learning operator in the form of a convolutional neural network. In this paper, we firstly design a new iterator network (iNet), which is based on the universal approximation theorem and tries to simulate the functional relationship between the former and the latter in the maximum-likelihood expectation maximization (MLEM) algorithm. To evaluate the effectiveness of the method, we conduct experiments on a CT dataset, and the results show that our iNet method improves the quality of reconstructed images.
KW - computed tomography (CT)
KW - image reconstruction
KW - maximum-likelihood expectation maximization (MLEM)
UR - http://www.scopus.com/inward/record.url?scp=85138144850&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85138144850&partnerID=8YFLogxK
U2 - 10.3934/mbe.2022609
DO - 10.3934/mbe.2022609
M3 - Article
C2 - 36654034
AN - SCOPUS:85138144850
SN - 1547-1063
VL - 19
SP - 13050
EP - 13061
JO - Mathematical Biosciences and Engineering
JF - Mathematical Biosciences and Engineering
IS - 12
ER -