An improved statistical iterative algorithm for sparse-view and limited-angle CT image reconstruction

Zhanli Hu

doi:10.1038/s41598-017-11222-z

2017-09-06

DOI：10.1038/s41598-017-11222-z

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摘要

En 中文

Because radiation is harmful to patients, it is important to reduce X-ray exposure in the clinic. For CT, reconstructions from sparse views or limited angle tomography are being used more frequently for low dose imaging. However, insufficient sampling data causes severe streak artifacts in images reconstructed using conventional methods. To solve this issue, various methods have recently been developed. In this paper, we improve a statistical iterative algorithm based on the minimization of the image total variation (TV) for sparse or limited projection views during CT image reconstruction. Considering the statistical nature of the projection data, the TV is performed under a penalized weighted least-squares (PWLS-TV) criterion. During implementation of the proposed method, the image reconstructed using the filtered back-projection (FBP) method is used as the initial value of the first iteration. Next, the feature refinement (FR) step is performed after each PWLS-TV iteration to extract the fine features lost in the TV minimization, which we refer to as 'PWLS-TV-FR'.

Keyword:

REDUCTION

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对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。

期刊

Scientific Reports

IF:

3.9

论文数:

28.0W

被引数:

83.5W

机构

chinese academy of sciences

学者数:

56.7W

论文数: 45.0W

被引数: 704

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