A Novel Method for Segmentation-Based Semiautomatic Quantitative Evaluation of Metal Artifact Reduction Algorithms

Objectives The aim of this study was to establish an objective segmentation-based evaluation of metal artifact reduction algorithms in the context of percutaneous microwave ablation in a porcine model. Materials and Methods Five computed tomography acquisitions from a previous animal study on computed tomography–guided percutaneous applicator positioning for microwave antenna were reconstructed with 6 different algorithms (30 image series total): standard filtered backprojection (B30f) and iterative reconstruction (ADMIRE-I30–1, ADMIRE-I30–3), all with and without metal artifact reduction. For artifact quantification, 3-dimensional segmentation of liver parenchyma without visible artifacts (VLiverReference) and liver volume surrounding the antenna (VLiverVOI) was performed, determining thresholds for artifact segmentation and calculating volume of voxels influenced by artifacts. Objective image analysis was based on relative volume of artifacts, and subjective image quality (ie, metal artifact extent) was evaluated by 2 independent observers. Correlation between objective and subjective evaluation was calculated. Results Both objective and subjective evaluations showed a significant reduction in metal artifacts when using dedicated metal artifact reduction algorithms (both P < 0.05). No significant reduction in metal artifacts was found when using iterative reconstruction (both P > 0.05). A good correlation between subjective and objective image quality was found (Spearman rank correlation coefficient rs = 0.65; P < 0.05). Interreader agreement was substantial (&kgr; = 0.67). Conclusions Segmentation-based objective evaluation of metal artifacts shows good agreement with conventional subjective evaluations and offers a promising quantitative and precise approach with limited time expenditure.

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