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.

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.

Fan-beam and cone-beam image reconstruction via filtering the backprojection image of differentiated projection data

2004 ◽  
Vol 49 (24) ◽  
pp. 5489-5503 ◽  
Author(s):  
Tingliang Zhuang ◽  
Shuai Leng ◽  
Brian E Nett ◽  
Guang-Hong Chen
Keyword(s):  
Image Reconstruction ◽  
Cone Beam ◽  
Projection Data ◽  
Beam Image

A simplified approach for the generation of projection data for cone beam geometry

Pramana ◽  
2011 ◽  
Vol 76 (4) ◽  
pp. 667-679
Author(s):  
TUSHAR ROY ◽  
P S SARKAR ◽  
AMAR SINHA
Keyword(s):  
Cone Beam ◽  
Projection Data ◽  
Simplified Approach ◽  
Beam Geometry

scholarly journals A cone-beam X-ray computed tomography data collection designed for machine learning

2019 ◽  
Vol 6 (1) ◽  
Author(s):  
Henri Der Sarkissian ◽  
Felix Lucka ◽  
Maureen van Eijnatten ◽  
Giulia Colacicco ◽  
Sophia Bethany Coban ◽  
...  
Keyword(s):  
Machine Learning ◽  
Computed Tomography ◽  
Image Reconstruction ◽  
Data Collection ◽  
Cone Angle ◽  
Open Data ◽  
Cone Beam ◽  
Projection Data ◽  
X Ray ◽  
Artefact Reduction

Abstract Unlike previous works, this open data collection consists of X-ray cone-beam (CB) computed tomography (CT) datasets specifically designed for machine learning applications and high cone-angle artefact reduction. Forty-two walnuts were scanned with a laboratory X-ray set-up to provide not only data from a single object but from a class of objects with natural variability. For each walnut, CB projections on three different source orbits were acquired to provide CB data with different cone angles as well as being able to compute artefact-free, high-quality ground truth images from the combined data that can be used for supervised learning. We provide the complete image reconstruction pipeline: raw projection data, a description of the scanning geometry, pre-processing and reconstruction scripts using open software, and the reconstructed volumes. Due to this, the dataset can not only be used for high cone-angle artefact reduction but also for algorithm development and evaluation for other tasks, such as image reconstruction from limited or sparse-angle (low-dose) scanning, super resolution, or segmentation.

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.

SU-FF-I-43: Iterative Methods of Cone-Beam CT Image Reconstruction for Under-Sampled and Truncated Projection Data

2009 ◽  
Vol 36 (6Part3) ◽  
pp. 2444-2444
Author(s):  
I Yeung ◽  
L Dawson ◽  
Y Cho ◽  
D Moseley ◽  
R Case ◽  
...  
Keyword(s):  
Image Reconstruction ◽  
Iterative Methods ◽  
Cone Beam Ct ◽  
Cone Beam ◽  
Projection Data ◽  
Ct Image ◽  
Ct Image Reconstruction
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