Metal artifact reduction for CT-guided interventional oncology procedures (MARIO): demonstration of a deep learning-based approach for metal artifact reduction for cryoablation procedures

Computed tomography (CT) is routinely used to guide cryoablation procedures. Notably, CT-guidance provides 3D localization of cryoprobes and can be used to delineate frozen tissue during ablation. However, metal-induced artifacts from ablation probes can make accurate probe placement challenging and degrade the ice ball conspicuity, which in combination could lead to undertreatment of potentially curable lesions. An image domain metal artifact simulation framework was developed and validated for deep-learning-based metal artifact reduction for interventional oncology (MARIO). Metal probes and resulting artifacts were segmented from 19 phantom image sets and inserted into 19 different sets of patient CT images to simulate artifacts. This dataset was used to optimize a U-Net type model. Due to unique traits of probe artifacts, we employed custom augmentation techniques and loss functions for model optimization. An ablation study compared performance with and without these additional factors. The combined strategies improved quantitative metrics by 40.95% over baseline training. Augmentations also increased generalizability. Patient cases showed MARIO substantially reduced artifacts while preserving anatomical details. In a reader study, scores from three board-certified radiologists were significantly higher for MARIO processed images compared to the original images across all metrics (all p<0.0001).