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SU‐C‐206‐03: Metal Artifact Reduction in X‐Ray Computed Tomography Based On Local Anatomical Similarity
Author(s) -
Dong X,
Yang X,
Rosenfield J,
Elder E,
Dhabaan A
Publication year - 2016
Publication title -
medical physics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.473
H-Index - 180
eISSN - 2473-4209
pISSN - 0094-2405
DOI - 10.1118/1.4955585
Subject(s) - streaking , pixel , artificial intelligence , image guided radiation therapy , similarity (geometry) , artifact (error) , computer vision , nuclear medicine , computer science , medicine , medical imaging , image (mathematics) , pathology
Purpose: Metal implants such as orthopedic hardware and dental fillings cause severe bright and dark streaking in reconstructed CT images. These artifacts decrease image contrast and degrade HU accuracy, leading to inaccuracies in target delineation and dose calculation. Additionally, such artifacts negatively impact patient set‐up in image guided radiation therapy (IGRT). In this work, we propose a novel method for metal artifact reduction which utilizes the anatomical similarity between neighboring CT slices. Methods: Neighboring CT slices show similar anatomy. Based on this anatomical similarity, the proposed method replaces corrupted CT pixels with pixels from adjacent, artifact‐free slices. A gamma map, which is the weighted summation of relative HU error and distance error, is calculated for each pixel in the artifact‐corrupted CT image. The minimum value in each pixel's gamma map is used to identify a pixel from the adjacent CT slice to replace the corresponding artifact‐corrupted pixel. This replacement only occurs if the minimum value in a particular pixel's gamma map is larger than a threshold. The proposed method was evaluated with clinical images. Results: Highly attenuating dental fillings and hip implants cause severe streaking artifacts on CT images. The proposed method eliminates the dark and bright streaking and improves the implant delineation and visibility. In particular, the image non‐uniformity in the central region of interest was reduced from 1.88 and 1.01 to 0.28 and 0.35, respectively. Further, the mean CT HU error was reduced from 328 HU and 460 HU to 60 HU and 36 HU, respectively. Conclusions: The proposed metal artifact reduction method replaces corrupted image pixels with pixels from neighboring slices that are free of metal artifacts. This method proved capable of suppressing streaking artifacts, improving HU accuracy and image detectability.