Literature DB >> 26233215

A level set method for cupping artifact correction in cone-beam CT.

Shipeng Xie1, Chunming Li2, Haibo Li1, Qi Ge1.   

Abstract

PURPOSE: To reduce cupping artifacts and improve the contrast-to-noise ratio in cone-beam computed tomography (CBCT).
METHODS: A level set method is proposed to reduce cupping artifacts in the reconstructed image of CBCT. The authors derive a local intensity clustering property of the CBCT image and define a local clustering criterion function of the image intensities in a neighborhood of each point. This criterion function defines an energy in terms of the level set functions, which represent a segmentation result and the cupping artifacts. The cupping artifacts are estimated as a result of minimizing this energy.
RESULTS: The cupping artifacts in CBCT are reduced by an average of 90%. The results indicate that the level set-based algorithm is practical and effective for reducing the cupping artifacts and preserving the quality of the reconstructed image.
CONCLUSIONS: The proposed method focuses on the reconstructed image without requiring any additional physical equipment, is easily implemented, and provides cupping correction through a single-scan acquisition. The experimental results demonstrate that the proposed method successfully reduces the cupping artifacts.

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Year:  2015        PMID: 26233215     DOI: 10.1118/1.4926851

Source DB:  PubMed          Journal:  Med Phys        ISSN: 0094-2405            Impact factor:   4.071


  4 in total

1.  A model-based scatter artifacts correction for cone beam CT.

Authors:  Wei Zhao; Don Vernekohl; Jun Zhu; Luyao Wang; Lei Xing
Journal:  Med Phys       Date:  2016-04       Impact factor: 4.071

2.  An energy minimization method for the correction of cupping artifacts in cone-beam CT.

Authors:  Shipeng Xie; Wenqin Zhuang; Haibo Li
Journal:  J Appl Clin Med Phys       Date:  2016-07-08       Impact factor: 2.102

3.  Contextual loss based artifact removal method on CBCT image.

Authors:  Shipeng Xie; Yingjuan Liang; Tao Yang; Zhenrong Song
Journal:  J Appl Clin Med Phys       Date:  2020-11-02       Impact factor: 2.102

4.  Shading artifact correction in breast CT using an interleaved deep learning segmentation and maximum-likelihood polynomial fitting approach.

Authors:  Peymon Ghazi; Andrew M Hernandez; Craig Abbey; Kai Yang; John M Boone
Journal:  Med Phys       Date:  2019-06-23       Impact factor: 4.071

  4 in total

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