Comments (1)
Yes. DDM^2 is a self-supervised optimization-based denoising algorithm. That means the training data needs to be the testing data itself. With this being said, we require one single 4D MRI acquisition (more volumes are better) as the input. After training, the trained model is able to (and is only able to) denoise the same 4D MRI acquisition.
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Related Issues (20)
- Experimenting on DCE-MRI simulated phantom with added noise HOT 1
- Need for Stage 1 Model in Stage 3 training HOT 1
- Clarifications on dimensions HOT 7
- Environment conflicts with GPU HOT 3
- Configurations for executing code with the PPMI dataset HOT 2
- Evaluation HOT 5
- Question regarding dataset HOT 2
- Question about the second stage HOT 1
- Question about training data valid_mask[10,160]
- sampling process HOT 5
- Question about data['condition'] HOT 2
- Question about matched state? HOT 1
- Question about the diffusion training process. HOT 1
- voxel size change in denoised image HOT 1
- Questions about the third stage HOT 1
- Question about the beta schedule HOT 4
- Questions about datasets HOT 4
- Provide benchmark dataset and trained model for unit-test HOT 1
- Reproducing the FA map
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