Prog Med Phys.  2018 Dec;29(4):150-156. 10.14316/pmp.2018.29.4.150.

Anisotropic Total Variation Denoising Technique for Low-Dose Cone-Beam Computed Tomography Imaging

• Lee H
• Yoon J
• Lee E

• KMID: 2434649
• DOI: http://doi.org/10.14316/pmp.2018.29.4.150

Abstract

This study aims to develop an improved Feldkamp-Davis-Kress (FDK) reconstruction algorithm using anisotropic total variation (ATV) minimization to enhance the image quality of low-dose cone-beam computed tomography (CBCT). The algorithm first applies a filter that integrates the Shepp-Logan filter into a cosine window function on all projections for impulse noise removal. A total variation objective function with anisotropic penalty is then minimized to enhance the difference between the real structure and noise using the steepest gradient descent optimization with adaptive step sizes. The preserving parameter to adjust the separation between the noise-free and noisy areas is determined by calculating the cumulative distribution function of the gradient magnitude of the filtered image obtained by the application of the filtering operation on each projection. With these minimized ATV projections, voxel-driven backprojection is finally performed to generate the reconstructed images. The performance of the proposed algorithm was evaluated with the catphan503 phantom dataset acquired with the use of a low-dose protocol. Qualitative and quantitative analyses showed that the proposed ATV minimization provides enhanced CBCT reconstruction images compared with those generated by the conventional FDK algorithm, with a higher contrast-to-noise ratio (CNR), lower root-mean-square-error, and higher correlation. The proposed algorithm not only leads to a potential imaging dose reduction in repeated CBCT scans via lower mA levels, but also elicits high CNR values by removing noisy corrupted areas and by avoiding the heavy penalization of striking features.

Keyword

Low-dose CBCT; FDK; Anisotropic total variation; Low mAs

MeSH Terms

Cone-Beam Computed Tomography*
Dataset
Noise
Strikes, Employee

Figure

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  Fig. 1 1–D ramp filter.

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  Fig. 2 Example of the CDF plot generated with the gradient magnitude calculated at all the pixels of the filtered projection data.

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  Fig. 3 Comparisons of the same views of the reconstructed image generated by applying (a) FDK with the Shepp–Logan filter, (b) FDK with a modified filter, and (c) FDK with ATV. The top row contains the CTP404 module and the bottom row contains the CTP486 module. All the images are displayed using W=1600 and L=200 HU.

• 

  Fig. 4 Comparison of reconstructed images for a representative slice generated by applying (a) conventional FDK using high-dose projections and (b) FDK with ATV using low-dose projections. These images, including the CTP528 module, are displayed using W=2400 and L=200 HU.

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