| Abstract: |
| This report will introduce two types of CT metal artifact reduction (MAR) methods based on the fusion of physical priors and deep learning. The first is a deep unrolling framework with soft metal trace, which adopts an adaptive projection-domain weighting mechanism to preserve effective tissue information and integrates a physics-informed primal dual hybrid gradient optimization scheme into the network to enhance model interpretability. The second is a self-supervised framework based on implicit neural representation, which does not require paired data, combines multi-resolution hash encoding to capture high-frequency anatomical information, and incorporates physical correction operators and an adaptive bone region weighting strategy to achieve simultaneous correction of metal and bone regions. |
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