scholarly journals An Image Reconstruction Method Based on Total Variation and Wavelet Tight Frame for Limited-Angle CT

IEEE Access ◽  
2018 ◽  
Vol 6 ◽  
pp. 1461-1470 ◽  
Author(s):  
Xiaoqiang Luo ◽  
Wei Yu ◽  
Chengxiang Wang
Keyword(s):  
Image Reconstruction ◽  
Total Variation ◽  
Tight Frame ◽  
Reconstruction Method ◽  
Image Reconstruction Method ◽  
Limited Angle

Anisotropic structure property based image reconstruction method for limited-angle computed tomography

2021 ◽  
pp. 1-24
Author(s):  
Changcheng Gong ◽  
Li Zeng
Keyword(s):  
Computed Tomography ◽  
Image Reconstruction ◽  
Projection Data ◽  
Reconstruction Technique ◽  
Reconstruction Method ◽  
Anisotropic Structure ◽  
Structure Property ◽  
Image Reconstruction Method ◽  
Ct Data ◽  
Limited Angle

Limited-angle computed tomography (CT) may appear in restricted CT scans. Since the available projection data is incomplete, the images reconstructed by filtered back-projection (FBP) or algebraic reconstruction technique (ART) often encounter shading artifacts. However, using the anisotropy property of the shading artifacts that coincide with the characteristic of limited-angle CT images can reduce the shading artifacts. Considering this concept, we combine the anisotropy property of the shading artifacts with the anisotropic structure property of an image to develop a new algorithm for image reconstruction. Specifically, we propose an image reconstruction method based on adaptive weighted anisotropic total variation (AwATV). This method, termed as AwATV method for short, is designed to preserve image structures and then remove the shading artifacts. It characterizes both of above properties. The anisotropy property of the shading artifacts accounts for reducing artifacts, and the anisotropic structure property of an image accounts for preserving structures. In order to evaluate the performance of AwATV, we use the simulation projection data of FORBILD head phantom and real CT data for image reconstruction. Experimental results show that AwATV can always reconstruct images with higher SSIM and PSNR, and smaller RMSE, which means that AwATV enables to reconstruct images with higher quality in term of artifact reduction and structure preservation.

Image reconstruction method for exterior circular cone-beam CT based on weighted directional total variation in cylindrical coordinates

2020 ◽  
Vol 28 (2) ◽  
pp. 155-172
Author(s):  
Yumeng Guo ◽  
Li Zeng ◽  
Jiaxi Wang ◽  
Zhaoqiang Shen
Keyword(s):  
Image Reconstruction ◽  
Total Variation ◽  
Cone Beam ◽  
Projection Data ◽  
Reconstruction Method ◽  
X Rays ◽  
Cylindrical Coordinates ◽  
Edge Preserving ◽  
Exterior Region ◽  
Image Reconstruction Method

AbstractThe exterior cone-beam computed tomography (CBCT) appears when the x-rays can only pass through the exterior region of an object due to the restriction of the size of the detector, the energy of x-rays and many other factors. The exterior CBCT is an ill-posed inverse problem due to the missing projection data. The distribution of artifacts in exterior CBCT is highly related to the direction of missing projection data. In order to reduce artifacts and reconstruct high quality image, an image reconstruction method based on weighted directional total variation in cylindrical coordinates (cWDTV)is presented in this paper. The directional total variation is calculated according to the direction of missing projection data. The weights are set to reduce artifacts and preserve edges. The convexity of cWDTV and the relationship between cWDTV and classical TV are also illustrated to explain the advantages of our method. Simulated experiments show that our method can improve the performance on artifact reduction and edge preserving.

Image Reconstruction with the Fourier Coefficients for Magnetic Induction Tomography

2020 ◽  
Vol 16 (2) ◽  
pp. 156-163
Author(s):  
Jingwen Wang ◽  
Xu Wang ◽  
Dan Yang ◽  
Kaiyang Wang
Keyword(s):  
Inverse Problem ◽  
Image Reconstruction ◽  
Magnetic Induction ◽  
Fourier Coefficients ◽  
Signal Reconstruction ◽  
Reconstruction Algorithm ◽  
Reconstruction Method ◽  
Measurement Equation ◽  
Image Reconstruction Method ◽  
Magnetic Induction Tomography

Background: Image reconstruction of magnetic induction tomography (MIT) is a typical ill-posed inverse problem, which means that the measurements are always far from enough. Thus, MIT image reconstruction results using conventional algorithms such as linear back projection and Landweber often suffer from limitations such as low resolution and blurred edges. Methods: In this paper, based on the recent finite rate of innovation (FRI) framework, a novel image reconstruction method with MIT system is presented. Results: This is achieved through modeling and sampling the MIT signals in FRI framework, resulting in a few new measurements, namely, fourier coefficients. Because each new measurement contains all the pixel position and conductivity information of the dense phase medium, the illposed inverse problem can be improved, by rebuilding the MIT measurement equation with the measurement voltage and the new measurements. Finally, a sparsity-based signal reconstruction algorithm is presented to reconstruct the original MIT image signal, by solving this new measurement equation. Conclusion: Experiments show that the proposed method has better indicators such as image error and correlation coefficient. Therefore, it is a kind of MIT image reconstruction method with high accuracy.

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