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

Zhanli Hu,Xin Liu,Dong Liang,Yongfeng Yang,Juan Gao,Na Zhang,Hairong Zheng

doi:10.1038/s41598-017-11222-z

Zhanli Hu, Xin Liu + Show 5 more

Open Access

https://doi.org/10.1038/s41598-017-11222-z

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Journal: Scientific Reports	Publication Date: Sep 6, 2017
Citations: 26	License type: open-access

Affiliation: Shenzhen Institutes of Advanced Technology

Abstract

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’.

Highlights

The clinical value of CT is unquestionable, radiation exposure is harmful to patients[1, 2]
One of the main strategies for low-dose CT image reconstruction restores the line integrals from acquired low-dose projection data, and a transform-based method has been presented to address the noise properties of low-mAs CT sinogram data
To address the global use of high-diagnostic CT images obtained from sparse-view or limited-angle projection data, we improved on the statistical iterative algorithm by including an feature refinement (FR) step

Summary

Introduction

The clinical value of CT is unquestionable, radiation exposure is harmful to patients[1, 2]. In the CT field, the radiation dose is usually reduced by lowering the X-ray tube current, lowering the X-ray tube voltage, or sampling fewer views or using limited-angle projection. One of the main strategies for low-dose CT image reconstruction restores the line integrals from acquired low-dose projection data, and a transform-based method has been presented to address the noise properties of low-mAs CT sinogram data. Li et al determined a nonlinear relationship between the variance and the mean of acquired low-mAs sinogram data[9], which facilitates low-dose CT image reconstruction Based on this relationship, a framework for image reconstruction using low-mAs sinogram data with penalized re-weighted least squares was investigated by Wang et al.[10]. Statistical iterative reconstruction (SIR), which is used to model measurement statistics and imaging geometry, can significantly maintain the image quality in cases of insufficient sampling data, especially compared with the filtered back-projection (FBP) reconstruction algorithm[13]. The algorithm was applied to a scanning configuration which is not practically used (namely the interior problem)

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