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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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| Prior | Model | Phase | Size | Contrast | Subscript |
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|-------------------------------|-----------|-----------|----------------------|------------------------------------------------------------------------------------|-----------------------|
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year={2023}
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```
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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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year={2023}
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}
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```
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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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2. Blumenthal, M, Luo, G, Schilling, M, Holme, HCM, Uecker, M. Deep, deep learning with BART. Magn Reson Med. 2023; 89: 678- 693.
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3. Luo, G, Zhao, N, Jiang, W, Hui, ES, Cao, P. MRI reconstruction using deep Bayesian estimation. Magn Reson Med. 2020; 84: 2246-2261.
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