Fourier Properties of Symmetric-Geometry Computed Tomography and Its Linogram Reconstruction With Neural Network

被引:9
作者
Zhang, Tao [1 ,2 ]
Zhang, Li [1 ,2 ]
Chen, Zhiqiang [1 ,2 ]
Xing, Yuxiang [1 ,2 ]
Gao, Hewei [1 ,2 ]
机构
[1] Tsinghua Univ, Dept Engn Phys, Beijing 100084, Peoples R China
[2] Tsinghua Univ, Minist Educ, Key Lab Particle & Radiat Imaging, Beijing 100084, Peoples R China
基金
中国国家自然科学基金;
关键词
Computed tomography; Image reconstruction; Fourier transforms; Detectors; Neural networks; Geometry; Symmetric-geometry computed tomography; Fourier property; Fourier slice theorem; Linogram; prior knowledge; neural network; IMAGE-RECONSTRUCTION; ALGORITHM; INVERSION;
D O I
10.1109/TMI.2020.3020720
中图分类号
TP39 [计算机的应用];
学科分类号
081203 ; 0835 ;
摘要
In this work, we investigate the Fourier properties of a symmetric-geometry computed tomography (SGCT) with linearly distributed source and detector in a stationary configuration. A linkage between the 1D Fourier Transform of a weighted projection from SGCT and the 2D Fourier Transform of a deformed object is established in a simple mathematical form (i.e., the Fourier slice theorem for SGCT). Based on its Fourier slice theorem and its unique data sampling in the Fourier space, a Linogram-based Fourier reconstruction method is derived for SGCT. We demonstrate that the entire Linogram reconstruction process can be embedded as known operators into an end-to-end neural network. As a learning-based approach, the proposed Linogram-Net has capability of improving CT image quality for non-ideal imaging scenarios, a limited-angle SGCT for instance, through combining weights learning in the projection domain and loss minimization in the image domain. Numerical simulations and physical experiments on an SGCT prototype platform showed that our proposed Linogram-based method can achieve accurate reconstruction from a dual-SGCT scan and can greatly reduce computational complexity when compared with the filtered backprojection type reconstruction. The Linogram-Net achieved accurate reconstruction when projection data are complete and significantly suppressed image artifacts from a limited-angle SGCT scan mimicked by using a clinical CT dataset, with the average CT number error in the selected regions of interest reduced from 67.7 Hounsfield Units (HU) to 28.7 HU, and the average normalized mean square error of overall images reduced from 4.21e-3 to 2.65e-3.
引用
收藏
页码:4445 / 4457
页数:13
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