Evaluation of State-of-the-Art Hardware Architectures for Fast Cone-Beam CT Reconstruction

2011 ◽  
Author(s):  
Holger Scherl
Keyword(s):  
State Of The Art ◽  
Cone Beam Ct ◽  
Cone Beam ◽  
Ct Reconstruction ◽  
Hardware Architectures

Evaluation of state-of-the-art hardware architectures for fast cone-beam CT reconstruction

2012 ◽  
Vol 38 (3) ◽  
pp. 111-124 ◽  
Author(s):  
Holger Scherl ◽  
Markus Kowarschik ◽  
Hannes G. Hofmann ◽  
Benjamin Keck ◽  
Joachim Hornegger
Keyword(s):  
State Of The Art ◽  
Cone Beam Ct ◽  
Cone Beam ◽  
Ct Reconstruction ◽  
Hardware Architectures

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

scholarly journals Fully Convolutional Network for Consistent Voxel-Wise Correspondence

2020 ◽  
Vol 34 (07) ◽  
pp. 12935-12942 ◽  
Author(s):  
Yungeng Zhang ◽  
Yuru Pei ◽  
Yuke Guo ◽  
Gengyu Ma ◽  
Tianmin Xu ◽  
...  
Keyword(s):  
Vector Fields ◽  
State Of The Art ◽  
Displacement Vector ◽  
Cone Beam Ct ◽  
Deformable Registration ◽  
Cone Beam ◽  
Nonlinear Mapping ◽  
Convolutional Network ◽  
Mapping Functions ◽  
Fully Convolutional Network

In this paper, we propose a fully convolutional network-based dense map from voxels to invertible pair of displacement vector fields regarding a template grid for the consistent voxel-wise correspondence. We parameterize the volumetric mapping using a convolutional network and train it in an unsupervised way by leveraging the spatial transformer to minimize the gap between the warped volumetric image and the template grid. Instead of learning the unidirectional map, we learn the nonlinear mapping functions for both forward and backward transformations. We introduce the combinational inverse constraints for the volumetric one-to-one maps, where the pairwise and triple constraints are utilized to learn the cycle-consistent correspondence maps between volumes. Experiments on both synthetic and clinically captured volumetric cone-beam CT (CBCT) images show that the proposed framework is effective and competitive against state-of-the-art deformable registration techniques.

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
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