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Adaptive‐weighted high order TV algorithm for sparse‐view CT reconstruction by Yarui Xi; Pengwu Zhou; Haijun Yu; Tao Zhang; Lingli Zhang; Zhiwei Qiao; Fenglin Liu is a Medicine article available to read on EtoBox.

What is Adaptive‐weighted high order TV algorithm for sparse‐view CT reconstruction about?

Background: With the development of low-dose computed tomography (CT), incomplete data reconstruction has been widely concerned. The total variation (TV) minimization algorithm can accurately reconstruct images from sparse or noisy data. Purpose: However, the traditional TV algorithm ignores the direction of structures in images, leading to the loss of edge information and block artifacts when the object is not piecewise constant. Since the anisotropic information can facilitate preserving the edge and detail information in images, we aim to improve the TV algorithm in terms of reconstruction accuracy via this approach. Methods: In this paper, we propose an adaptive-weighted high order total variation (awHOTV) algorithm. We construct the second order TV-norm using the second order gradient, adapt the anisotropic edge property between neighboring image pixels, adjust the local image-intensity gradient to keep edge information, and design the corresponding Chambolle-Pock (CP) solving algorithm. Implementing the proposed algorithm, comprehensive studies are conducted in the ideal projection data experiment where the Structural similarity (SSIM), Root Mean Square Error (RMSE), Contrast

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Author

Yarui Xi; Pengwu Zhou; Haijun Yu; Tao Zhang; Lingli Zhang; Zhiwei Qiao; Fenglin Liu

Publisher

Wiley

Published

2023

Language

EN

Field

Medicine (Health Sciences)