ct image reconstruction from sparse projections using adaptive tpv regularization

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ID: 185866
2015
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Abstract
Radiation dose reduction without losing CT image quality has been an increasing concern. Reducing the number of X-ray projections to reconstruct CT images, which is also called sparse-projection reconstruction, can potentially avoid excessive dose delivered to patients in CT examination. To overcome the disadvantages of total variation (TV) minimization method, in this work we introduce a novel adaptive TpV regularization into sparse-projection image reconstruction and use FISTA technique to accelerate iterative convergence. The numerical experiments demonstrate that the proposed method suppresses noise and artifacts more efficiently, and preserves structure information better than other existing reconstruction methods.
Reference Key
qi2015computationalct Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors ;Hongliang Qi;Zijia Chen;Linghong Zhou
Journal advanced functional materials
Year 2015
DOI
10.1155/2015/354869
URL
http://dx.doi.org/10.1155/2015/354869
Keywords
medical informatics computer applications to medicine

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