Min Li1,2, Sarah Joy Castillo3,4, Richard Castillo3,4, Edward Castillo3,5,6, Thomas Guerrero3,5, Liang Xiao7, Xiaolin Zheng8. 1. School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing, 210094, China. min.lee023@gmail.com. 2. Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, 77030, USA. min.lee023@gmail.com. 3. Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, 77030, USA. 4. Department of Radiation Oncology, The University of Texas Medical Branch, Galveston, TX, 77555, USA. 5. Department of Radiation Oncology, Beaumont Health System, Royal Oak, Mi, 48073, USA. 6. Department of Computational and Applied Mathematics, Rice University, Houston, TX, 77005, USA. 7. School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing, 210094, China. 8. Bioengineering College, Chongqing University, Chongqing, 400030, China.
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.
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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