CT Image Reconstruction Algorithm Based on Anisotropic Total Variation Minimization
关键词: 计算机断层成像； 不完全角度重建； 总变分最小化； 交替方向法； 各向异性总变分最小化； CT Image Reconstruction； Few-View Reconstruction； Total Variation Minimization； Alternating Direction Method； Anisotropic Total Variation Minimization摘要:
Abstract: In many practical applications, due to the data acquisition time, dose, and geometric constraint scanning, only in a limited Angle range, various data is available to acquire. It is the so-called few-view problem. In recent years, the Total Variation (TV) minimization model, using alternating direction method (ADM) in sparse optimization algorithm shows better reconstruction results among these TV-based algorithms. However, Isotropic TV minimization based algorithms for few- view reconstruction has not so good accuracy and there is further improvement to achieve. Aiming at this problem, Anisotropic TV minimization algorithm is proposed in this paper. The algorithm is based on ADM and uses sparse optimization theory. Experimental results demonstrate that the proposed method compared with Isotropic TV minimization algorithm, has higher reconstruction accuracy and a more excellent comprehensive performance.
文章引用: 张 莹 , 王 丹 (2014) 一种基于各向异性总变分最小化的CT图像重建算法。 计算机科学与应用， 4， 240-247. doi: 10.12677/CSA.2014.410033
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