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Automated identification and reduction of artifacts in cine four-dimensional computed tomography (4DCT) images using respiratory motion model

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International Journal of Computer Assisted Radiology and Surgery Aims and scope Submit manuscript

Abstract

Purpose

Four-dimensional computed tomography (4DCT) images are often marred by artifacts that substantially degrade image quality and confound image interpretation. Human observation remains the standard method of 4DCT artifact evaluation, which is time-consuming and subjective. We develop a method to automatically identify and reduce artifacts in cine 4DCT images.

Methods

We proposed an algorithm that consisted of two main stages: deformable image registration and respiratory motion simulation. Specifically, each 4DCT phase image was registered to the breath-holding CT image using the block-matching method, with erroneous spatial matches removed by the least median of squares filter and the full displacement vector field generated by the moving least squares interpolation. The lung’s respiratory motion trajectory was then recovered from the displacement vector field using the parameterized polynomial function, with fitting parameters estimated by combinatorial optimization. In this way, artifacts were located according to deviations between image points and their motion trajectories and further corrected based on position prediction.

Results

The mean spatial error (standard deviation) was 1.00 (0.85) mm after registration as opposed to 6.96 (4.61) mm before registration. In addition, we took human observation conducted by medical experts as the gold standard. The average sensitivity, specificity, and accuracy of the proposed method in artifact identification were 0.97, 0.84, and 0.89, respectively.

Conclusions

The proposed method identified and reduced artifacts accurately and automatically, providing an alternative way to analyze 4DCT image quality and to correct problematic images for radiation therapy.

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Acknowledgements

This work was supported by the National Natural Science Foundation of China (Nos. 61501241, 61571230), the Natural Science Foundation of Jiangsu Province (No. BK20150792), and the project funded by China Postdoctoral Science Foundation (No. 2015M570450).This work was also partially funded by The University of Texas MD Anderson Cancer Center’s Support Grant (CA016672).

Author information

Authors and Affiliations

  1. School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing, 210094, China

    Min Li & Liang Xiao

  2. Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, 77030, USA

    Min Li, Sarah Joy Castillo, Richard Castillo, Edward Castillo & Thomas Guerrero

  3. Department of Radiation Oncology, The University of Texas Medical Branch, Galveston, TX, 77555, USA

    Sarah Joy Castillo & Richard Castillo

  4. Department of Radiation Oncology, Beaumont Health System, Royal Oak, Mi, 48073, USA

    Edward Castillo & Thomas Guerrero

  5. Department of Computational and Applied Mathematics, Rice University, Houston, TX, 77005, USA

    Edward Castillo

  6. Bioengineering College, Chongqing University, Chongqing, 400030, China

    Xiaolin Zheng

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  1. Min Li
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  2. Sarah Joy Castillo
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  3. Richard Castillo
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Correspondence to Min Li.

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Conflict of interest

Min Li, Sarah Joy Castillo, Richard Castillo, Edward Castillo, Thomas Guerrero, Liang Xiao, and Xiaolin Zheng declare that they have no conflict of interest.

Ethical approval

All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki Declaration and its later amendments or comparable ethical standards.

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Informed consent was obtained from all individual participants included in the study.

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Li, M., Castillo, S.J., Castillo, R. et al. Automated identification and reduction of artifacts in cine four-dimensional computed tomography (4DCT) images using respiratory motion model. Int J CARS 12, 1521–1532 (2017). https://doi.org/10.1007/s11548-017-1538-0

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  • DOI: https://doi.org/10.1007/s11548-017-1538-0

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