Cardiac motion-corrected iterative cone-beam CT reconstruction using a semi-automatic minimum cost path-based coronary centerline extraction

2012 ◽  
Vol 36 (3) ◽  
pp. 215-226 ◽  
Author(s):  
A.A. Isola ◽  
C.T. Metz ◽  
M. Schaap ◽  
S. Klein ◽  
M. Grass ◽  
...  
Keyword(s):  
Cone Beam Ct ◽  
Minimum Cost ◽  
Cardiac Motion ◽  
Cone Beam ◽  
Ct Reconstruction ◽  
Centerline Extraction ◽  
Coronary Centerline

Dental cone-beam CT reconstruction from limited-angle view data based on compressed-sensing (CS) theory for fast, low-dose X-ray imaging

2014 ◽  
Vol 64 (12) ◽  
pp. 1907-1911
Author(s):  
Uikyu Je ◽  
Hyosung Cho ◽  
Minsik Lee ◽  
Jieun Oh ◽  
Yeonok Park ◽  
...  
Keyword(s):  
Compressed Sensing ◽  
Low Dose ◽  
Cone Beam Ct ◽  
Cone Beam ◽  
Ct Reconstruction ◽  
X Ray ◽  
X Ray Imaging ◽  
Limited Angle

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.

scholarly journals Limited-View Cone-Beam CT Reconstruction Based on an Adversarial Autoencoder Network With Joint Loss

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 7104-7116 ◽  
Author(s):  
Xiubin Dai ◽  
Jianan Bai ◽  
Tianliang Liu ◽  
Lizhe Xie
Keyword(s):  
Cone Beam Ct ◽  
Cone Beam ◽  
Ct Reconstruction

Five-dimensional motion compensation for respiratory and cardiac motion with cone-beam CT of the thorax region

2016 ◽  
Author(s):  
Sebastian Sauppe ◽  
Andreas Hahn ◽  
Marcus Brehm ◽  
Pascal Paysan ◽  
Dieter Seghers ◽  
...  
Keyword(s):  
Motion Compensation ◽  
Cone Beam Ct ◽  
Cardiac Motion ◽  
Cone Beam
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