scholarly journals EP-1826: An empirical post-reconstruction method for beam hardening correction in CT reconstruction

2016 ◽  
Vol 119 ◽  
pp. S856-S857
Author(s):  
B. Yang ◽  
H. Geng ◽  
W.W. Lam ◽  
K.Y. Cheung ◽  
S.K. Yu
2015 ◽  
Author(s):  
Wenlei Liu ◽  
Junyan Rong ◽  
Peng Gao ◽  
Qimei Liao ◽  
HongBing Lu

2019 ◽  
Vol 48 (8) ◽  
pp. 20190235
Author(s):  
Hugo Gaêta-Araujo ◽  
Nicolly Oliveira-Santos ◽  
Danieli Moura Brasil ◽  
Eduarda Helena Leandro do Nascimento ◽  
Daniela Verardi Madlum ◽  
...  

Objectives: To evaluate the influence of the level of three micro-CT reconstruction tools: beam-hardening correction (BHC), smoothing filter (SF), and ring artefact correction (RAC) on the fractal dimension (FD) analysis of trabecular bone. Methods: Five Wistar rats’ maxillae were individually scanned in a SkyScan 1174 micro-CT device, under the following settings: 50 kV, 800 µA, 10.2 µm voxel size, 0.5 mm Al filter, rotation step 0.5°, two frames average, 180° rotation and scan time of 35 min. The raw images were reconstructed under the standard protocol (SP) recommended by the manufacturer, a protocol without any artefact correction tools (P0) and 35 additional protocols with different combinations of SF, RAC and BHC levels. The same volume of interest was established in all reconstructions for each maxilla and the FD was calculated using the Kolmogorov (box counting) method. One-way ANOVA with Dunnet’s post-hoc test was used to compare the FD of each reconstruction protocol (P0–P35) with the SP (α = 5%). Multiple linear regression verified the dependency of reconstruction tools in FD. Results: Overall, FD values are not dependent on RAC (p = 0.965), but increased significantly when the level of BHC and SF increased (p < 0.001). FD values from protocols with BHC at 45% combined with SF of 2, and BHC at 30% combined with SF of 4 or 6 had no statistical difference compared to SP. Conclusions: BHC and SF tools affect the FD values of micro-CT images of the trabecular bone. Therefore, these reconstruction parameters should be standardized when the FD is analyzed.


2022 ◽  
Author(s):  
Yun Chen ◽  
Yao Lu ◽  
Xiangyuan Ma ◽  
Yuesheng Xu

Abstract The goal of this study is to develop a new computed tomography (CT) image reconstruction method, aiming at improving the quality of the reconstructed images of existing methods while reducing computational costs. Existing CT reconstruction is modeled by pixel-based piecewise constant approximations of the integral equation that describes the CT projection data acquisition process. Using these approximations imposes a bottleneck model error and results in a discrete system of a large size. We propose to develop a content-adaptive unstructured grid (CAUG) based regularized CT reconstruction method to address these issues. Specifically, we design a CAUG of the image domain to sparsely represent the underlying image, and introduce a CAUG-based piecewise linear approximation of the integral equation by employing a collocation method. We further apply a regularization defined on the CAUG for the resulting illposed linear system, which may lead to a sparse linear representation for the underlying solution. The regularized CT reconstruction is formulated as a convex optimization problem, whose objective function consists of a weighted least square norm based fidelity term, a regularization term and a constraint term. Here, the corresponding weighted matrix is derived from the simultaneous algebraic reconstruction technique (SART). We then develop a SART-type preconditioned fixed-point proximity algorithm to solve the optimization problem. Convergence analysis is provided for the resulting iterative algorithm. Numerical experiments demonstrate the outperformance of the proposed method over several existing methods in terms of both suppressing noise and reducing computational costs. These methods include the SART without regularization and with quadratic regularization on the CAUG, the traditional total variation (TV) regularized reconstruction method and the TV superiorized conjugate gradient method on the pixel grid.


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