Keywords: Metal artifact reduction, CT preprocessing, projection domain, convolutional neural network, partial convolution
TL;DR: A neural network with specific layer type reduces CT metal artifacts via inpainting in the projection domain.
Abstract: Metal artifacts impair the diagnostic value of medical CT images. These artifacts occur from the projection values associated with the metal objects inside the scanned anatomy. In this work, we replace the corrupted projection values by using a deep convolutional neural network consisting of so-called partial convolution layers. We show that the network trained on simulated data enhances newly presented projection data and therefore the corresponding reconstructed image.
Paper Type: validation/application paper
Primary Subject Area: Application: Radiology
Secondary Subject Area: Validation Study
Paper Status: original work, not submitted yet
Source Code Url: https://gitlab.com/maik.stille/partconvmar
Data Set Url: Data can not be provided since we are not licensed for open distribution.
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