scholarly journals Implementation and evaluation of two helical CT reconstruction algorithms in CIVA

2016 ◽  
Author(s):  
H. Banjak ◽  
M. Costin ◽  
C. Vienne ◽  
V. Kaftandjian
Keyword(s):  
Helical Ct ◽  
Ct Reconstruction ◽  
Reconstruction Algorithms

Convergence and stability analysis of the half thresholding based few-view CT reconstruction

2020 ◽  
Vol 28 (6) ◽  
pp. 829-847
Author(s):  
Hua Huang ◽  
Chengwu Lu ◽  
Lingli Zhang ◽  
Weiwei Wang
Keyword(s):  
Noise Suppression ◽  
Reconstructed Image ◽  
Reference Image ◽  
Projection Data ◽  
Ct Reconstruction ◽  
Reconstruction Algorithms ◽  
Ill Posed ◽  
Thresholding Algorithm ◽  
The Stability ◽  
Well Posed

AbstractThe projection data obtained using the computed tomography (CT) technique are often incomplete and inconsistent owing to the radiation exposure and practical environment of the CT process, which may lead to a few-view reconstruction problem. Reconstructing an object from few projection views is often an ill-posed inverse problem. To solve such problems, regularization is an effective technique, in which the ill-posed problem is approximated considering a family of neighboring well-posed problems. In this study, we considered the {\ell_{1/2}} regularization to solve such ill-posed problems. Subsequently, the half thresholding algorithm was employed to solve the {\ell_{1/2}} regularization-based problem. The convergence analysis of the proposed method was performed, and the error bound between the reference image and reconstructed image was clarified. Finally, the stability of the proposed method was analyzed. The result of numerical experiments demonstrated that the proposed method can outperform the classical reconstruction algorithms in terms of noise suppression and preserving the details of the reconstructed image.

High Pitch Helical CT Reconstruction

2021 ◽  
pp. 1-1
Author(s):  
John W. Hayes ◽  
Juan Montoya ◽  
Adam Budde ◽  
Chengzhu Zhang ◽  
Yinsheng Li ◽  
...  
Keyword(s):  
Helical Ct ◽  
Ct Reconstruction ◽  
High Pitch

scholarly journals Phantom and clinical assessment of small pulmonary nodules using Q.Clear reconstruction on a silicon-photomultiplier-based time-of-flight PET/CT system

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Zhifang Wu ◽  
Binwei Guo ◽  
Bin Huang ◽  
Xinzhong Hao ◽  
Ping Wu ◽  
...  
Keyword(s):  
Time Of Flight ◽  
Pulmonary Nodules ◽  
Medium Size ◽  
Ct Reconstruction ◽  
Reconstruction Algorithms ◽  
Silicon Photomultiplier ◽  
Standardized Uptake Values ◽  
Pet Ct ◽  
Quantification Accuracy ◽  
Small Pulmonary Nodules

AbstractTo evaluate the quantification accuracy of different positron emission tomography-computed tomography (PET/CT) reconstruction algorithms, we measured the recovery coefficient (RC) and contrast recovery (CR) in phantom studies. The results played a guiding role in the partial-volume-effect correction (PVC) for following clinical evaluations. The PET images were reconstructed with four different methods: ordered subsets expectation maximization (OSEM), OSEM with time-of-flight (TOF), OSEM with TOF and point spread function (PSF), and Bayesian penalized likelihood (BPL, known as Q.Clear in the PET/CT of GE Healthcare). In clinical studies, SUVmax and SUVmean (the maximum and mean of the standardized uptake values, SUVs) of 75 small pulmonary nodules (sub-centimeter group: < 10 mm and medium-size group: 10–25 mm) were measured from 26 patients. Results show that Q.Clear produced higher RC and CR values, which can improve quantification accuracy compared with other methods (P < 0.05), except for the RC of 37 mm sphere (P > 0.05). The SUVs of sub-centimeter fludeoxyglucose (FDG)-avid pulmonary nodules with Q.Clear illustrated highly significant differences from those reconstructed with other algorithms (P < 0.001). After performing the PVC, highly significant differences (P < 0.001) still existed in the SUVmean measured by Q.Clear comparing with those measured by the other algorithms. Our results suggest that the Q.Clear reconstruction algorithm improved the quantification accuracy towards the true uptake, which potentially promotes the diagnostic confidence and treatment response evaluations with PET/CT imaging, especially for the sub-centimeter pulmonary nodules. For small lesions, PVC is essential.

scholarly journals X-RAY CT Reconstruction by using Spatially Non Homogeneous ICD Optimization

2019 ◽  
Vol 8 (6S3) ◽  
pp. 2043-2046
Keyword(s):  
Search Algorithm ◽  
Computational Cost ◽  
Selection Criterion ◽  
Closed Form Solution ◽  
Helical Ct ◽  
Reconstruction Algorithm ◽  
Form Solution ◽  
Ct Reconstruction ◽  
Practical Applications ◽  
Voxel Selection

Recent applications of conventional iterative coordinate descent (ICD) algorithms to multislice helical CT reconstructions have shown that conventional ICD can greatly improve image quality by increasing resolution as well as reducing noise and some artifacts. However, high computational cost and long reconstruction times remain as a barrier to the use of conventional algorithm in the practical applications. Among the various iterative methods that have been studied for conventional, ICD has been found to have relatively low overall computational requirements due to its fast convergence. This paper presents a fast model-based iterative reconstruction algorithm using spatially nonhomogeneous ICD (NH-ICD) optimization. The NH-ICD algorithm speeds up convergence by focusing computation where it is most needed. The NH-ICD algorithm has a mechanism that adaptively selects voxels for update. First, a voxel selection criterion VSC determines the voxels in greatest need of update. Then a voxel selection algorithm VSA selects the order of successive voxel updates based upon the need for repeated updates of some locations, while retaining characteristics for global convergence. In order to speed up each voxel update, we also propose a fast 3-D optimization algorithm that uses a quadratic substitute function to upper bound the local 3-D objective function, so that a closed form solution can be obtained rather than using a computationally expensive line search algorithm. The experimental results show that the proposed method accelerates the reconstructions by roughly a factor of three on average for typical 3-D multislice geometries.

SU-C-141-05: Impact of Respiratory-Correlated CT Reconstruction Algorithms in the Choice of Margin Definition for Free Breathing Lung Treatment

2013 ◽  
Vol 40 (6Part2) ◽  
pp. 92-92
Author(s):  
S Thengumpallil ◽  
J-F Germond ◽  
O Matzinger ◽  
J Bourhis ◽  
F Bochud ◽  
...  
Keyword(s):  
Free Breathing ◽  
Ct Reconstruction ◽  
Reconstruction Algorithms

scholarly journals Penalized PET/CT Reconstruction Algorithms with Automatic Realignment for Anatomical Priors

2020 ◽  
pp. 1-1
Author(s):  
Yu-Jung Tsai ◽  
Alexandre Bousse ◽  
Simon Arridge ◽  
Charles W. Stearns ◽  
Brian F. Hutton ◽  
...  
Keyword(s):  
Ct Reconstruction ◽  
Reconstruction Algorithms ◽  
Pet Ct ◽  
Anatomical Priors
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