Adapting image editing foundation models via LoRA with multi-reference conditioning achieves state-of-the-art CT metal artifact reduction using two orders of magnitude less paired training data than prior methods.
An iterative maximum-likelihood polychromatic algo- rithm for ct.IEEE Transactions on Medical Imaging, 20(10):999–1008
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Leveraging Image Editing Foundation Models for Data-Efficient CT Metal Artifact Reduction
Adapting image editing foundation models via LoRA with multi-reference conditioning achieves state-of-the-art CT metal artifact reduction using two orders of magnitude less paired training data than prior methods.