scholarly journals Computed tomography noise reduction based on total variation minimization and morphological component analysis

2021 ◽  
Author(s):  
Aryan Khodabandeh
Keyword(s):  
Computed Tomography ◽  
Total Variation ◽  
Low Dose ◽  
Denoising Method ◽  
Decomposition Approach ◽  
Total Variation Minimization ◽  
Morphological Component Analysis ◽  
Image Separation ◽  
X Ray Computed ◽  
Harmful Radiation

X-ray Computed Tomography (CT) scans, while useful, emit harmful radiation which is why low-dose image acquisition is desired. However, noise corruption in these cases is a difficult obstacle. CT image denoising is a challenging topic because of the difficulty in modeling noise. In this study, we propose taking an image decomposition approach to removing noise from low-dose CT images. We model the image as the superposition of a structure layer and a noise layer. Total Variation (TV) minimization is used to learn two dictionaries to represent each layer independently, and sparse coding is used to separate them. Finally, an iterative post-processing stage is introduced that uses image-adapted curvelet dictionaries to recover blurred edges. Our results demonstrate that image separation is a viable alternative to the classic K-SVD denoising method.

scholarly journals Computed tomography noise reduction based on total variation minimization and morphological component analysis

2021 ◽  
Author(s):  
Aryan Khodabandeh
Keyword(s):  
Computed Tomography ◽  
Total Variation ◽  
Low Dose ◽  
Denoising Method ◽  
Decomposition Approach ◽  
Total Variation Minimization ◽  
Morphological Component Analysis ◽  
Image Separation ◽  
X Ray Computed ◽  
Harmful Radiation

X-ray Computed Tomography (CT) scans, while useful, emit harmful radiation which is why low-dose image acquisition is desired. However, noise corruption in these cases is a difficult obstacle. CT image denoising is a challenging topic because of the difficulty in modeling noise. In this study, we propose taking an image decomposition approach to removing noise from low-dose CT images. We model the image as the superposition of a structure layer and a noise layer. Total Variation (TV) minimization is used to learn two dictionaries to represent each layer independently, and sparse coding is used to separate them. Finally, an iterative post-processing stage is introduced that uses image-adapted curvelet dictionaries to recover blurred edges. Our results demonstrate that image separation is a viable alternative to the classic K-SVD denoising method.

scholarly journals Ant Colony-Based Hyperparameter Optimisation in Total Variation Reconstruction in X-ray Computed Tomography

Sensors ◽  
2021 ◽  
Vol 21 (2) ◽  
pp. 591
Author(s):  
Manasavee Lohvithee ◽  
Wenjuan Sun ◽  
Stephane Chretien ◽  
Manuchehr Soleimani
Keyword(s):  
Computed Tomography ◽  
Total Variation ◽  
Cross Validation ◽  
Ant Colony ◽  
Reference Image ◽  
X Ray ◽  
X Ray Computed ◽  
Hyperparameter Selection ◽  
The Cross ◽  
Optimal Set

In this paper, a computer-aided training method for hyperparameter selection of limited data X-ray computed tomography (XCT) reconstruction was proposed. The proposed method employed the ant colony optimisation (ACO) approach to assist in hyperparameter selection for the adaptive-weighted projection-controlled steepest descent (AwPCSD) algorithm, which is a total-variation (TV) based regularisation algorithm. During the implementation, there was a colony of artificial ants that swarm through the AwPCSD algorithm. Each ant chose a set of hyperparameters required for its iterative CT reconstruction and the correlation coefficient (CC) score was given for reconstructed images compared to the reference image. A colony of ants in one generation left a pheromone through its chosen path representing a choice of hyperparameters. Higher score means stronger pheromones/probabilities to attract more ants in the next generations. At the end of the implementation, the hyperparameter configuration with the highest score was chosen as an optimal set of hyperparameters. In the experimental results section, the reconstruction using hyperparameters from the proposed method was compared with results from three other cases: the conjugate gradient least square (CGLS), the AwPCSD algorithm using the set of arbitrary hyperparameters and the cross-validation method.The experiments showed that the results from the proposed method were superior to those of the CGLS algorithm and the AwPCSD algorithm using the set of arbitrary hyperparameters. Although the results of the ACO algorithm were slightly inferior to those of the cross-validation method as measured by the quantitative metrics, the ACO algorithm was over 10 times faster than cross—Validation. The optimal set of hyperparameters from the proposed method was also robust against an increase of noise in the data and can be applicable to different imaging samples with similar context. The ACO approach in the proposed method was able to identify optimal values of hyperparameters for a dataset and, as a result, produced a good quality reconstructed image from limited number of projection data. The proposed method in this work successfully solves a problem of hyperparameters selection, which is a major challenge in an implementation of TV based reconstruction algorithms.

scholarly journals Low-Dosed X-Ray Computed Tomography Imaging by Regularized Fully Spatial Fractional-Order Perona-Malik Diffusion

2013 ◽  
Vol 2013 ◽  
pp. 1-9 ◽  
Author(s):  
Zhiwu Liao
Keyword(s):  
Computed Tomography ◽  
Fractional Order ◽  
Low Dose ◽  
Computed Tomography Imaging ◽  
Edge Preserving ◽  
X Ray ◽  
Good Ability ◽  
X Ray Computed ◽  
The Right ◽  
Low Dose Computed Tomography

Existing fractional-order Perona-Malik Diffusion (FOPMD) algorithms used in noise suppressing suffer from undesired artifacts and speckle effect, which hamper FOPMD used in low-dosed X-ray computed tomography (LDCT) imaging. In this paper, we propose a new FOPMD method for low-dose computed tomography (LDCT) imaging, which is called regularized fully spatial FOPMD (RFS-FOPMD), whose numerical scheme is also given based on Grünwald-Letnikov derivative (G-L derivative). Here, fully spatial FOPMD represents all the integer-order derivatives (IODs) in the right hand of Perona-Malik Diffusion (PMD) which are replaced by fractional-order derivatives (FODs). Since the new scheme has advantages of both regularization and FOPMD, it has good abilities in singularities preserving while suppressing noise. Some real sinogram of LDCT are used to compare the different performances not only for some classical but also for some state-of-art diffusion schemes. These schemes include PMD, regularized PMD (RPMD), and FOPMD in (Hu et al. 2012). Experimental results show that besides good ability in edge preserving, the new scheme also has good stability for iteration number and can avoid artifacts and speckle effect with suitable parameters.

Low-dose low-scattering X-ray computed tomography with high-spatial-energy resolutions using a cooled cadmium telluride detector

2019 ◽  
Vol 199 ◽  
pp. 62-69 ◽  
Author(s):  
Hodaka Moriyama ◽  
Manabu Watanabe ◽  
Shinya Kusachi ◽  
Yasuyuki Oda ◽  
Eiichi Sato
Keyword(s):  
Computed Tomography ◽  
Cadmium Telluride ◽  
Low Dose ◽  
X Ray ◽  
X Ray Computed
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