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  ## Generative pretrained models on MRI images.
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- The prior distribution of MRI images learned with generative models has proven to be effective in MRI image reconstruction. Here, we include four PixelCNN models and two diffusion models, one is SMLD and the another one is DDPM. These models are trained with [spreco](https://github.com/mrirecon/spreco). For more details on how these models were trained, please find them in our [paper](https://) and the related [codes](https://github.com/mrirecon/image-priors).
 
 
 
 
 
 
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- Have a quick-try with [![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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- ## 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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-
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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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+
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+ ## How to use
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+
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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