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7 April 2023 An edge-preserving mean curvature regularization for CT reconstruction
Le Shen, Yuxiang Xing, Li Zhang, Sen Wang
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Proceedings Volume 12463, Medical Imaging 2023: Physics of Medical Imaging; 1246346 (2023) https://doi.org/10.1117/12.2655207
Event: SPIE Medical Imaging, 2023, San Diego, California, United States
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Abstract
Regularization is an essential term to suppress noise and artifacts in iterative reconstruction algorithms. The total variation (TV) is one of the most successful regularizations which can eliminate noise and streak artifacts while preserving edges. However, strong TV regularization usually produces staircase artifacts if the image contains non-constant regions. Recently, the mean curvature (MC) regularization based on the geometry-driven diffusion model has been proposed to solve image processing problems. In an aspect of geometry, minimizing the mean curvature to zero derives a linear surface. In this paper, we develop a linear convolution approximated mean curvature based on the local geometric properties of surfaces embedded in 3D space. We adopted the half window kernel technique and formulated a novel edge-preserving mean curvature (EPMC) regularization. Compared with the traditional curvature diffusion model that employs secondorder partial derivatives, the proposed method is efficient and concise. The ADMM algorithm can solve the optimization problem. We conducted a simulation study and compared the proposed EPMC regularization with TV. The results demonstrate that the proposed method is superior in noise suppression and edge preservation in linear regions while avoiding staircase artifacts.
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Le Shen, Yuxiang Xing, Li Zhang, and Sen Wang "An edge-preserving mean curvature regularization for CT reconstruction", Proc. SPIE 12463, Medical Imaging 2023: Physics of Medical Imaging, 1246346 (7 April 2023); https://doi.org/10.1117/12.2655207
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KEYWORDS
CT reconstruction
Reconstruction algorithms
Diffusion
Image quality
Computed tomography
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