DDM2
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[ICLR2023] Official repository of DDM2: Self-Supervised Diffusion MRI Denoising with Generative Diffusion Models
Hi, thank you for your contribution and sharing. I would like to ask, why does sqrt_alphas_cumprod_prev need append 1? And how should we solve the problem when the time step...
Has that dataset be applied to another type of MRI? The MRI I am working on is with dimensions 90,90,3,5 - magnetic field x evolution time? I tried to combine...
Can you given example for one dataset like stanford_hardi what the typical dataroot in config looks like for these datasets ? I have downloaded stanford_hardi in a folder specifying the...
Inference
Great job! Thanks! Will you upload the trained model in the future? It's something we can use to infer directly, without training. I don't know if it's okay to ask,...
Hi @tiangexiang I have a few questions about evaluation and metrics. I run the denoise.py with the save option, but in the code I see: #### #### if args.save: dataset_opt['val_volume_idx']...
Hi, thanks a lot for your interest in those issues, I wanted to ask about your comment on the following issue when I want to train Stage1: ``` 24-01-18 01:06:41.203...
Hi, thank you for this amazing paper. I wanted to ask you very few questions to elaborate in very detail. I have seen it in multiple places (i.e. `mri_dataset.py`) that...
I was experimenting with a simulated 4D phantom of ellipse with 4th dimension being time. I generated a 3D ellipse with smaller ellipsed and changed signal intensity over time and...
Hi, Thank you for this repository. It is great work. I have a few questions and would like to clarify some doubts. I was successfully able to run all three...
Hi, Thank you for your contribution about DDM2. I encountered the following problem when running the first step of the code. Can you help me look at it? ![image](https://github.com/StanfordMIMI/DDM2/assets/68863894/1b7a9ef5-92ba-4df2-b0f0-c1f8a98687b7)