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README.md
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## Generative pretrained models on MRI images.
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The prior distribution of MRI images learned with generative
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| Prior | Model | Phase | Size | Contrast | Subscript |
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| \\(\texttt{D}_\mathrm{PC}\\) | Diffusion | generated | ~80000 | MPRAGE | PC - DDPM, complex |
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## How to use
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The Berkeley Advanced Reconstruction Toolbox ([BART](https://mrirecon.github.io/bart/)) toolbox provides many functionalities for MRI image reconstruction. It introduced the application of TensorFlow graph as regularization in [Deep learning with BART](https://doi.org/10.1002/mrm.29485) and there is a [colab notebook](https://colab.research.google.com/github/mrirecon/bart-workshop/blob/master/ismrm2021/bart_tensorflow/bart_tf.ipynb) where you can give a quickstart with it. For the codes to evaluate above priors, please find them in this [repository](https://github.com/ggluo/image-priors).
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## Citation
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1. Luo, G, Blumenthal, M, Heide, M, Uecker, M. Bayesian MRI reconstruction with joint uncertainty estimation using diffusion models. Magn Reson Med. 2023; 1-17
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## Generative pretrained models on MRI images.
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The prior distribution of MRI images learned with generative
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models has proven to be effective in MRI image reconstruction.
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Here, we include four PixelCNN models and two diffusion models,
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one is SMLD and the another one is DDPM. These models are trained
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with [spreco](https://github.com/mrirecon/spreco).
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For more details on how these models were trained, please find them in our [paper](https://)
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and the related [codes](https://github.com/mrirecon/image-priors).
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## How to use
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The Berkeley Advanced Reconstruction Toolbox, ([BART](https://mrirecon.github.io/bart/)),
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provides many functionalities for MRI image reconstruction.
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It introduced the application of the TensorFlow graph as regularization
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in this [paper](https://doi.org/10.1002/mrm.29485). You can try it on colab.
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[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/ggluo/image-priors/blob/release/misc/demo_sampler_colab.ipynb)
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| Prior | Model | Phase | Size | Contrast | Subscript |
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|-------------------------------|-----------|-----------|----------------------|------------------------------------------------------------------------------------|-----------------------|
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| \\(\texttt{D}_\mathrm{PC}\\) | Diffusion | generated | ~80000 | MPRAGE | PC - DDPM, complex |
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## Citation
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1. Luo, G, Blumenthal, M, Heide, M, Uecker, M. Bayesian MRI reconstruction with joint uncertainty estimation using diffusion models. Magn Reson Med. 2023; 1-17
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