Quantification of metal artifacts in computed tomography: methodological considerations

Quant Imaging Med Surg. 2020 May;10(5):1033-1044. doi: 10.21037/qims.2020.04.03.

Abstract

Numerous methods for artifact quantification in computed tomography (CT) imaging have been suggested. This study evaluated their utility with regards to correspondence with visual artifact perception and reproducibility. Two titanium rods (5 and 10 mm) were examined with 25 different scanning- and image-reconstruction parameters resulting in different types and extents of artifacts. Four radiologists evaluated every image against each other using an in-house developed software. Rating was repeated two times (2,400 comparisons = 2 times × 4 readers × 300 comparisons). Rankings were combined to obtain a reference ranking. Proposed approaches for artifact quantification include manual measurement of attenuation, standard deviation and noise and sophisticated algorithm-based approaches within the image- and frequency-domain. Two radiologists conducted manual measurements twice while the aforementioned algorithms were implemented within the Matlab-Environment allowing for automated image analysis. The reference ranking was compared to all aforementioned methods for artifact quantification to identify suited approaches. Besides visual analysis, Kappa-statistics and intraclass correlation coefficients (ICC) were used. Intra- and Inter-reader agreements of visual artifact perception were excellent (ICC 0.85-0.92). No quantitative method was able to represent the exact ranking of visually perceived artifacts; however, ICC for manual measurements were low (ICC 0.25-0.97). The method that showed best correspondence and reproducibility used a Fourier-transformed linear ROI and lower-end frequency bins. Automated measurements of artifact extent should be preferred over manual measurements as the latter show a limited reproducibility. One method that allows for automated quantification of such artefacts is made available as an electronic supplement.

Keywords: Computed tomography (CT); artifact; image reconstruction; quantitative image analysis.