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Proceedings Paper

Image-based metal artifact reduction in x-ray computed tomography utilizing local anatomical similarity
Author(s): Xue Dong; Xiaofeng Yang; Jonathan Rosenfield; Eric Elder; Anees Dhabaan
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Paper Abstract

X-ray computed tomography (CT) is widely used in radiation therapy treatment planning in recent years. However, metal implants such as dental fillings and hip prostheses can cause severe bright and dark streaking artifacts in reconstructed CT images. These artifacts decrease image contrast and degrade HU accuracy, leading to inaccuracies in target delineation and dose calculation. In this work, a metal artifact reduction method is proposed based on the intrinsic anatomical similarity between neighboring CT slices. Neighboring CT slices from the same patient exhibit similar anatomical features. Exploiting this anatomical similarity, a gamma map is calculated as a weighted summation of relative HU error and distance error for each pixel in an artifact-corrupted CT image relative to a neighboring, artifactfree image. The minimum value in the gamma map for each pixel is used to identify an appropriate pixel from the artifact-free CT slice to replace the corresponding artifact-corrupted pixel. With the proposed method, the mean CT HU error was reduced from 360 HU and 460 HU to 24 HU and 34 HU on head and pelvis CT images, respectively. Dose calculation accuracy also improved, as the dose difference was reduced from greater than 20% to less than 4%. Using 3%/3mm criteria, the gamma analysis failure rate was reduced from 23.25% to 0.02%. An image-based metal artifact reduction method is proposed that replaces corrupted image pixels with pixels from neighboring CT slices free of metal artifacts. This method is shown to be capable of suppressing streaking artifacts, thereby improving HU and dose calculation accuracy.

Paper Details

Date Published: 9 March 2017
PDF: 6 pages
Proc. SPIE 10132, Medical Imaging 2017: Physics of Medical Imaging, 1013232 (9 March 2017); doi: 10.1117/12.2255083
Show Author Affiliations
Xue Dong, The Winship Cancer Institute of Emory Univ. (United States)
Xiaofeng Yang, The Winship Cancer Institute of Emory Univ. (United States)
Jonathan Rosenfield, The Winship Cancer Institute of Emory Univ. (United States)
Eric Elder, The Winship Cancer Institute of Emory Univ. (United States)
Anees Dhabaan, The Winship Cancer Institute of Emory Univ. (United States)


Published in SPIE Proceedings Vol. 10132:
Medical Imaging 2017: Physics of Medical Imaging
Thomas G. Flohr; Joseph Y. Lo; Taly Gilat Schmidt, Editor(s)

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