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--- |
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language: en |
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tags: |
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- MRI image priors |
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- Generative models |
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- Diffusion models |
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- TensorFlow |
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- PixelCNN |
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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 |
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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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[](https://colab.research.google.com/github/ggluo/image-priors/blob/main/misc/demo_image_priors_colab.ipynb) |
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| Prior | Model | Phase | Size | Contrast | Subscript | |
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|-------------------------------|-----------|-----------|----------------------|------------------------------------------------------------------------------------|-----------------------| |
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| \\(\texttt{P}_\mathrm{SC}\\) | PixelCNN | preserved | 1000 | T1, T2, T2-FLAIR, \\(\texttt{T}^*_\mathrm{2}\\) | SC - Small, complex | |
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| \\(\texttt{P}_\mathrm{SM}\\) | PixelCNN | unknown | 1000 | T1, T2, T2-FLAIR, \\(\texttt{T}^*_\mathrm{2}\\) | SM - Small, magnitude | |
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| \\(\texttt{P}_\mathrm{LM}\\) | PixelCNN | unknown | ~20000 | MPRAGE | LM - Large, magnitude | |
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| \\(\texttt{P}_\mathrm{LC}\\) | PixelCNN | generated | ~20000 | MPRAGE | LC - Large, complex | |
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| \\(\texttt{D}_\mathrm{SC}\\) | Diffusion | generated | ~80000 | MPRAGE | SC - SMLD, complex | |
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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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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. |
