A new weighting function to achieve high temporal resolution in circular cone-beam CT with shifted detectors

2008 ◽  
Vol 35 (12) ◽  
pp. 5898-5909 ◽  
Author(s):  
Clemens Maaß ◽  
Michael Knaup ◽  
Robert Lapp ◽  
Marek Karolczak ◽  
Willi A. Kalender ◽  
...  
Keyword(s):  
Temporal Resolution ◽  
Weighting Function ◽  
Cone Beam Ct ◽  
Circular Cone ◽  
Cone Beam ◽  
High Temporal Resolution

High temporal resolution cardiac cone-beam CT using a slowly rotating C-arm gantry

2009 ◽  
Author(s):  
Guang-Hong Chen ◽  
Jie Tang ◽  
Brian Nett ◽  
Shuai Leng ◽  
Joseph Zambelli ◽  
...  
Keyword(s):  
Temporal Resolution ◽  
Cone Beam Ct ◽  
Cone Beam ◽  
High Temporal Resolution

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 GPU-Based 3D Cone-Beam CT Image Reconstruction for Large Data Volume

2009 ◽  
Vol 2009 ◽  
pp. 1-8 ◽  
Author(s):  
Xing Zhao ◽  
Jing-jing Hu ◽  
Peng Zhang
Keyword(s):  
Image Reconstruction ◽  
Cone Beam Ct ◽  
Large Data ◽  
Circular Cone ◽  
Cone Beam ◽  
Projection Data ◽  
Ct Image ◽  
Reconstruction Process ◽  
Data Volume ◽  
Ct Image Reconstruction

Currently, 3D cone-beam CT image reconstruction speed is still a severe limitation for clinical application. The computational power of modern graphics processing units (GPUs) has been harnessed to provide impressive acceleration of 3D volume image reconstruction. For extra large data volume exceeding the physical graphic memory of GPU, a straightforward compromise is to divide data volume into blocks. Different from the conventional Octree partition method, a new partition scheme is proposed in this paper. This method divides both projection data and reconstructed image volume into subsets according to geometric symmetries in circular cone-beam projection layout, and a fast reconstruction for large data volume can be implemented by packing the subsets of projection data into the RGBA channels of GPU, performing the reconstruction chunk by chunk and combining the individual results in the end. The method is evaluated by reconstructing 3D images from computer-simulation data and real micro-CT data. Our results indicate that the GPU implementation can maintain original precision and speed up the reconstruction process by 110–120 times for circular cone-beam scan, as compared to traditional CPU implementation.

Calibration for Circular Cone-Beam CT Based on Consistency Conditions

2017 ◽  
Vol 1 (6) ◽  
pp. 517-526 ◽  
Author(s):  
Jerome Lesaint ◽  
Simon Rit ◽  
Rolf Clackdoyle ◽  
Laurent Desbat
Keyword(s):  
Cone Beam Ct ◽  
Circular Cone ◽  
Cone Beam ◽  
Consistency Conditions
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