Improved total variation minimization method for few-view computed tomography image reconstruction

BioMedical Engineering OnLine

Zhanli Hu²  Hairong Zheng¹ 

[1] Beijing Center for Mathematics and Information Interdisciplinary Sciences, Beijing, China

[2] Shenzhen Key Lab for Molecular Imaging, Shenzhen, China

关键词: Compressive sampling;    Total variation;    Adaptive prior image;    Few-view;    Computed tomography;   

Others  :  793189
DOI  :  10.1186/1475-925X-13-70

 received in 2014-04-08, accepted in 2014-06-02,  发布年份 2014

【 摘 要 】

Background

Due to the harmful radiation dose effects for patients, minimizing the x-ray exposure risk has been an area of active research in medical computed tomography (CT) imaging. In CT, reducing the number of projection views is an effective means for reducing dose. The use of fewer projection views can also lead to a reduced imaging time and minimizing potential motion artifacts. However, conventional CT image reconstruction methods will appears prominent streak artifacts for few-view data. Inspired by the compressive sampling (CS) theory, iterative CT reconstruction algorithms have been developed and generated impressive results.

Method

In this paper, we propose a few-view adaptive prior image total variation (API-TV) algorithm for CT image reconstruction. The prior image reconstructed by a conventional analytic algorithm such as filtered backprojection (FBP) algorithm from densely angular-sampled projections.

Results

To validate and evaluate the performance of the proposed algorithm, we carried out quantitative evaluation studies in computer simulation and physical experiment.

Conclusion

The results show that the API-TV algorithm can yield images with quality comparable to that obtained with existing algorithms.

【 授权许可】

   
2014 Hu and Zheng; licensee BioMed Central Ltd.

【 图 表 】

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