scholarly journals FDK Half-Scan with a Heuristic Weighting Scheme on a Flat Panel Detector-Based Cone Beam CT (FDKHSCW)

2006 ◽  
Vol 2006 ◽  
pp. 1-8 ◽  
Author(s):  
Dong Yang ◽  
Ruola Ning
Keyword(s):  
Attenuation Coefficient ◽  
Weighting Function ◽  
Cone Beam Ct ◽  
Cone Angle ◽  
Weighting Scheme ◽  
Cone Beam ◽  
Projection Data ◽  
Imaging Method ◽  
Redundant Data ◽  
Beam Geometry

A cone beam circular half-scan scheme is becoming an attractive imaging method in cone beam CT since it improves the temporal resolution. Traditionally, the redundant data in the circular half-scan range is weighted by a central scanning plane-dependent weighting function; FDK algorithm is then applied on the weighted projection data for reconstruction. However, this scheme still suffers the attenuation coefficient drop inherited with FDK when the cone angle becomes large. A new heuristic cone beam geometry-dependent weighting scheme is proposed based on the idea that there exists less redundancy for the projection data away from the central scanning plane. The performance of FDKHSCW scheme is evaluated by comparing it to the FDK full-scan (FDKFS) scheme and the traditional FDK half-scan scheme with Parker's fan beam weighting function (FDKHSFW). Computer simulation is employed and conducted on a 3D Shepp-Logan phantom. The result illustrates a correction of FDKHSCW to the attenuation coefficient drop in the off-scanning plane associated with FDKFS and FDKHSFW while maintaining the same spatial 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 A Method to Improve Electron Density Measurement of Cone-Beam CT Using Dual Energy Technique

2015 ◽  
Vol 2015 ◽  
pp. 1-8 ◽  
Author(s):  
Kuo Men ◽  
Jian-Rong Dai ◽  
Ming-Hui Li ◽  
Xin-Yuan Chen ◽  
Ke Zhang ◽  
...  
Keyword(s):  
Electron Density ◽  
Cone Beam Ct ◽  
Density Measurement ◽  
Dual Energy ◽  
Cone Beam ◽  
Imaging Method ◽  
Basis Material ◽  
X Ray ◽  
Dual Energy Imaging ◽  
Set Up

Purpose. To develop a dual energy imaging method to improve the accuracy of electron density measurement with a cone-beam CT (CBCT) device.Materials and Methods. The imaging system is the XVI CBCT system on Elekta Synergy linac. Projection data were acquired with the high and low energy X-ray, respectively, to set up a basis material decomposition model. Virtual phantom simulation and phantoms experiments were carried out for quantitative evaluation of the method. Phantoms were also scanned twice with the high and low energy X-ray, respectively. The data were decomposed into projections of the two basis material coefficients according to the model set up earlier. The two sets of decomposed projections were used to reconstruct CBCT images of the basis material coefficients. Then, the images of electron densities were calculated with these CBCT images.Results. The difference between the calculated and theoretical values was within 2% and the correlation coefficient of them was about 1.0. The dual energy imaging method obtained more accurate electron density values and reduced the beam hardening artifacts obviously.Conclusion. A novel dual energy CBCT imaging method to calculate the electron densities was developed. It can acquire more accurate values and provide a platform potentially for dose calculation.

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.

Multi-x-ray source array for stationary tomosynthesisor multi-cone angle cone beam CT

2019 ◽  
Author(s):  
John M. Boone ◽  
Amy E. Becker ◽  
Andrew M. Hernandez ◽  
James T. Dobbins ◽  
Paul Schwoebelc
Keyword(s):  
Cone Beam Ct ◽  
Cone Angle ◽  
Cone Beam ◽  
X Ray ◽  
Source Array

scholarly journals GPU-based fast cone beam CT reconstruction from undersampled and noisy projection data via total variation

2010 ◽  
Vol 37 (4) ◽  
pp. 1757-1760 ◽  
Author(s):  
Xun Jia ◽  
Yifei Lou ◽  
Ruijiang Li ◽  
William Y. Song ◽  
Steve B. Jiang
Keyword(s):  
Total Variation ◽  
Cone Beam Ct ◽  
Cone Beam ◽  
Projection Data ◽  
Ct Reconstruction ◽  
Noisy Projection

scholarly journals A General Local Reconstruction Approach Based on a Truncated Hilbert Transform

2007 ◽  
Vol 2007 ◽  
pp. 1-8 ◽  
Author(s):  
Yangbo Ye ◽  
Hengyong Yu ◽  
Yuchuan Wei ◽  
Ge Wang
Keyword(s):  
Image Reconstruction ◽  
Hilbert Transform ◽  
Region Of Interest ◽  
Cone Beam ◽  
Projection Data ◽  
Partial Knowledge ◽  
Practical Applications ◽  
Beam Geometry ◽  
Volume Of Interest ◽  
Simulation Results

Exact image reconstruction from limited projection data has been a central topic in the computed tomography (CT) field. In this paper, we present a general region-of-interest/volume-of-interest (ROI/VOI) reconstruction approach using a truly truncated Hilbert transform on a line-segment inside a compactly supported object aided by partial knowledge on one or both neighboring intervals of that segment. Our approach and associated new data sufficient condition allows the most flexible ROI/VOI image reconstruction from the minimum account of data in both the fan-beam and cone-beam geometry. We also report primary numerical simulation results to demonstrate the correctness and merits of our finding. Our work has major theoretical potentials and innovative practical applications.

TH-E-330A-05: Reducing Metal Artifacts in Cone-Beam CT by Tracking and Eliminating Metal Shadows in Raw Projection Data

2006 ◽  
Vol 33 (6Part23) ◽  
pp. 2288-2288 ◽  
Author(s):  
Y Zhang ◽  
L Zhang ◽  
RX Zhu ◽  
M Chambers ◽  
L Dong
Keyword(s):  
Cone Beam Ct ◽  
Cone Beam ◽  
Projection Data ◽  
Metal Artifacts

scholarly journals Cone-beam CT functional imaging method by using volume integral model

2018 ◽  
Vol 173 ◽  
pp. 03030
Author(s):  
Ying Qian ◽  
Boying Zheng
Keyword(s):  
Correlation Coefficient ◽  
Functional Imaging ◽  
Optimal Parameter ◽  
Optimization Method ◽  
Cone Beam ◽  
Integral Model ◽  
Projection Data ◽  
Imaging Method ◽  
Volume Integral ◽  
Dynamic Contrast Enhancement

In order to improve the precision of functional imaging of cone beam computed tomography (CBCT), this paper firstly uses the dynamic contrast enhancement tomography (DCE-CT) of the white rabbit as the measured object and establishes volume integral model to obtain the projection data. Then the optimization method is used to solve the optimal parameter pairs of the voxel time density curve (TDC). Finally, the results of the perfusion are obtained by the deconvolution method. The results show that the TDC correlation coefficient is 83.99% after, and the maximum of Spearman correlation coefficient of the perfusion parameter is 0.5125, and the projection time consumption is 7.633 seconds through the volume integral model. It can be seen that the volume integral model is closer to the real projection and it can obtain more accurate perfusion data.

scholarly journals Accurate helical cone-beam CT reconstruction with redundant data

2009 ◽  
Vol 54 (15) ◽  
pp. 4625-4644 ◽  
Author(s):  
Harald Schöndube ◽  
Karl Stierstorfer ◽  
Frédéric Noo
Keyword(s):  
Cone Beam Ct ◽  
Cone Beam ◽  
Ct Reconstruction ◽  
Redundant Data

The Improvement of Katsevich Reconstruction Algorithm Based on Cone-Beam CT

2012 ◽  
Vol 239-240 ◽  
pp. 1148-1151
Author(s):  
Li Fang Wang
Keyword(s):  
Image Quality ◽  
Polynomial Interpolation ◽  
Cone Beam Ct ◽  
Reconstruction Algorithm ◽  
Experimental Results ◽  
Cone Beam ◽  
Projection Data ◽  
Interpolation Function ◽  
Reconstruction Process ◽  
Derivatives Of

The Katsevich reconstruction algorithm based on cone-beam must compute the derivative of projection data in the reconstruction process, but projection data are discrete and haven’t derivative. So the derivatives of the polynomial interpolation function are as approximation of the derivative of projection data. To verify the effectiveness of this method, 3D Shepp-Logan model is reconstructed by the method and the average gradient is used to measure the clarity of image. The experimental results show that this method enables image clearer and improves image quality

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