Keywords: Machine Learning/Artificial Intelligence, Artifacts, Metal implantsIn MRI, presence of metal can cause blooming artifacts on MRI images. Automatically detecting the presence of metal and localizing it on the scout images itself can help streamline the MRI imaging workflow decisions downstream. In this work, we demonstrate a DL framework for hot spotting of metal/metal affected regions based on 2D three-plane MRI scout images of spine. The results indicate that the solution not only detects metal regions well on spine images (on which it was trained), but also is generalizable enough to work on other anatomies such as knee which was not part of the training data.
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