Filtering path variable FDK (v-FDK) reconstruction algorithm for circular cone-beam CT

2020 ◽  
Author(s):  
Chen Wang ◽  
Bo-Zhong Tan ◽  
Qing-Guo Yang ◽  
Ze-Ren Li
Keyword(s):  
Cone Beam Ct ◽  
Reconstruction Algorithm ◽  
Circular Cone ◽  
Cone Beam

A curve-filtered FDK (C-FDK) reconstruction algorithm for circular cone-beam CT

2011 ◽  
Vol 19 (3) ◽  
pp. 355-371 ◽  
Author(s):  
Liang Li ◽  
Yuxiang Xing ◽  
Zhiqiang Chen ◽  
Li Zhang ◽  
Kejun Kang
Keyword(s):  
Cone Beam Ct ◽  
Reconstruction Algorithm ◽  
Circular Cone ◽  
Cone Beam

A new weighting scheme for arc based circle cone-beam CT reconstruction

2021 ◽  
pp. 1-19
Author(s):  
Wei Wang ◽  
Xiang-Gen Xia ◽  
Chuanjiang He ◽  
Zemin Ren ◽  
Jian Lu
Keyword(s):  
Characteristic Function ◽  
Weighting Function ◽  
Cone Beam Ct ◽  
Reconstruction Algorithm ◽  
Cone Beam ◽  
Characteristic Functions ◽  
Projection Data ◽  
Ct Reconstruction ◽  
Scanning Angle ◽  
Beam Geometry

In this paper, we present an arc based fan-beam computed tomography (CT) reconstruction algorithm by applying Katsevich’s helical CT image reconstruction formula to 2D fan-beam CT scanning data. Specifically, we propose a new weighting function to deal with the redundant data. Our weighting function ϖ ( x _ , λ ) is an average of two characteristic functions, where each characteristic function indicates whether the projection data of the scanning angle contributes to the intensity of the pixel x _ . In fact, for every pixel x _ , our method uses the projection data of two scanning angle intervals to reconstruct its intensity, where one interval contains the starting angle and another contains the end angle. Each interval corresponds to a characteristic function. By extending the fan-beam algorithm to the circle cone-beam geometry, we also obtain a new circle cone-beam CT reconstruction algorithm. To verify the effectiveness of our method, the simulated experiments are performed for 2D fan-beam geometry with straight line detectors and 3D circle cone-beam geometry with flat-plan detectors, where the simulated sinograms are generated by the open-source software “ASTRA toolbox.” We compare our method with the other existing algorithms. Our experimental results show that our new method yields the lowest root-mean-square-error (RMSE) and the highest structural-similarity (SSIM) for both reconstructed 2D and 3D fan-beam CT images.

Investigation and Validation of the Reconstruction Algorithm for Small Animal Cone-beam CT Imaging

2010 ◽  
Vol 39 (9) ◽  
pp. 1588-1593
Author(s):  
缪辉 MIAO Hui ◽  
王秋殷 WANG Qiu-yin ◽  
赵会娟 ZHAO Hui-juan ◽  
王婷婷 WANG Ting-ting ◽  
高峰 GAO Feng
Keyword(s):  
Cone Beam Ct ◽  
Small Animal ◽  
Reconstruction Algorithm ◽  
Ct Imaging ◽  
Cone Beam
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