Projection-to-image transform frame: a lightweight block reconstruction network for computed tomography

Ma, Genwei1; Zhao, Xing1,2; Zhu, Yining1,2; Zhang, Huitao1,2

2022-02-07

10.1088/1361-6560/ac4122

PHYSICS IN MEDICINE AND BIOLOGY   影响因子和分区

0031-9155

1361-6560

Several reconstruction networks have been invented to solve the problem of learning-based computed tomography (CT) reconstruction. However, the application of neural networks to tomographic reconstruction remains challenging due to unacceptable memory space requirements. In this study, we present a novel lightweight block reconstruction network (LBRN), which transforms the reconstruction operator into a deep neural network by unrolling the filter back-projection (FBP) method. Specifically, the proposed network contains two main modules, which respectively correspond to the filter and back-projection of the FBP method. The first module of the LBRN decouples the relationship of the Radon transform between the reconstructed image and the projection data. Therefore, the following module, block back-projection, can use the block reconstruction strategy. Because each image block is only connected with part-filtered projection data, the network structure is greatly simplified and the parameters of the whole network are dramatically reduced. Moreover, this approach is trained end-to-end, working directly from raw projection data, and does not depend on any initial images. Five reconstruction experiments are conducted to evaluate the performance of the proposed LBRN: full angle, low-dose CT, region of interest, metal artifact reduction and a real data experiment. The results of the experiments show that the LBRN can be effectively introduced into the reconstruction process and has outstanding advantages in terms of different reconstruction problems.

computed tomography image reconstruction block reconstruction network

SCI ; EI

英语

通讯

National Natural Science Foundation of China["61 827 809","61 971 293"] ; National Key Research and Development Program of China[2020YFA0712200]

Engineering ; Radiology, Nuclear Medicine & Medical Imaging

Engineering, Biomedical ; Radiology, Nuclear Medicine & Medical Imaging

WOS:000749840400001

IOP Publishing Ltd

20220911732713

Data reduction ; Deep neural networks ; Image reconstruction ; Image segmentation

Ergonomics and Human Factors Engineering:461.4 ; Data Processing and Image Processing:723.2 ; Computer Applications:723.5

MOLECULAR BIOLOGY & GENETICS

Web of Science

1.Capital Normal Univ, Sch Math Sci, Beijing, Peoples R China
2.Southern Univ Sci & Technol, Shenzhen Natl Appl Math Ctr, Shenzhen, Peoples R China

Ma, Genwei,Zhao, Xing,Zhu, Yining,et al. Projection-to-image transform frame: a lightweight block reconstruction network for computed tomography[J]. PHYSICS IN MEDICINE AND BIOLOGY,2022,67(3).

Ma, Genwei,Zhao, Xing,Zhu, Yining,&Zhang, Huitao.(2022).Projection-to-image transform frame: a lightweight block reconstruction network for computed tomography.PHYSICS IN MEDICINE AND BIOLOGY,67(3).

Ma, Genwei,et al."Projection-to-image transform frame: a lightweight block reconstruction network for computed tomography".PHYSICS IN MEDICINE AND BIOLOGY 67.3(2022).