使用自适应分数阶正则化的统计迭代重建
Statistical iterative reconstruction using adaptive fractional order regularization.
作者信息
Zhang Yi, Wang Yan, Zhang Weihua, Lin Feng, Pu Yifei, Zhou Jiliu
机构信息
College of Computer Science, Sichuan University, Chengdu 610065, China.
出版信息
Biomed Opt Express. 2016 Feb 24;7(3):1015-29. doi: 10.1364/BOE.7.001015. eCollection 2016 Mar 1.
Abstract
In order to reduce the radiation dose of the X-ray computed tomography (CT), low-dose CT has drawn much attention in both clinical and industrial fields. A fractional order model based on statistical iterative reconstruction framework was proposed in this study. To further enhance the performance of the proposed model, an adaptive order selection strategy, determining the fractional order pixel-by-pixel, was given. Experiments, including numerical and clinical cases, illustrated better results than several existing methods, especially, in structure and texture preservation.
摘要
为了降低X射线计算机断层扫描（CT）的辐射剂量，低剂量CT在临床和工业领域都备受关注。本研究提出了一种基于统计迭代重建框架的分数阶模型。为了进一步提高所提模型的性能，给出了一种逐像素确定分数阶的自适应阶数选择策略。包括数值和临床病例在内的实验表明，该方法比几种现有方法具有更好的效果，尤其是在结构和纹理保留方面。
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