We present a feasibility study exploring the use deep learning (DL) methods to enhance the image quality of isotropic 3D-multi-spectral metal artifact suppressed images. Conventional high resolution 2D fast/turbo spin echo images are used as network training labels by masking regions corrupted by metal artifacts. A pilot set of 3 cases deploying the presented concept to T2-weighted imaging of the instrumented spine are analyzed. The quantitative results of this analysis demonstrate improved spinal cord contrast, image resolution, and general agreement with 2D-FSE images when the DL enhancement model is inferred on the complete 3D-MSI datasets.
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Koch et al. (2024) studied this question.
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