Keywords: Cardiac CT, Metal Artifact Reduction, Convolutional Neural Networks
Abstract: Metal objects in the human heart like implanted pacemakers frequently occur in elderly patients. Due to cardiac motion, they are not static during the CT acquisition and lead to heavy artifacts in reconstructed CT image volumes. Furthermore, cardiac motion precludes the application of standard metal artifact reduction methods which assume that the object does not move. We propose a deep-learning-based approach for dynamic pacemaker artifact removal which deals with metal shadow segmentation directly in the projection domain. The data required for supervised learning is generated by introducing synthetic pacemaker leads into 14 clinical data sets without pacemakers. CNNs achieve a Dice coefficient of 0.913 on test data with synthetic metal leads. Application of the trained CNNs on eight data sets with real pacemakers and subsequent inpainting of the post-processed segmentation masks leads to significantly reduced metal artifacts in the reconstructed CT image volumes.
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