---
language: en

license: bsd-3-clause

tags:
- MRI image priors
- Generative models
- Diffusion models
- TensorFlow
- PixelCNN
---

## Generative pretrained models on MRI images.

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).

| Prior                         | Model     | Phase     | Size                 | Contrast                                                                           | Subscript             |
|-------------------------------|-----------|-----------|----------------------|------------------------------------------------------------------------------------|-----------------------|
| \\(\texttt{P}_\mathrm{SC}\\)  | PixelCNN  | preserved | 1000                 | T1, T2, T2-FLAIR, \\(\texttt{T}^*_\mathrm{2}\\) | SC - Small, complex   |
| \\(\texttt{P}_\mathrm{SM}\\)  | PixelCNN  | unknown   | 1000                 | T1, T2, T2-FLAIR, \\(\texttt{T}^*_\mathrm{2}\\) | SM - Small, magnitude |
| \\(\texttt{P}_\mathrm{LM}\\)  | PixelCNN  | unknown   | ~20000 | MPRAGE                                                                             | LM - Large, magnitude |
| \\(\texttt{P}_\mathrm{LC}\\)  | PixelCNN  | generated | ~20000 | MPRAGE                                                                             | LC - Large, complex   |
| \\(\texttt{D}_\mathrm{SC}\\)  | Diffusion | generated | ~80000 | MPRAGE                                                                             | SC - SMLD, complex    |
| \\(\texttt{D}_\mathrm{PC}\\)  | Diffusion | generated | ~80000 | MPRAGE                                                                             | PC - DDPM, complex    |


## How to use
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 quickstart with it. For the codes to evaluate above priors, please find them in this [repository](https://github.com/mrirecon/image-priors).

## Citation
[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

[2] Blumenthal, M, Luo, G, Schilling, M, Holme, HCM, Uecker, M. Deep, deep learning with BART. Magn Reson Med. 2023; 89: 678- 693.

[3] Luo, G, Zhao, N, Jiang, W, Hui, ES, Cao, P. MRI reconstruction using deep Bayesian estimation. Magn Reson Med. 2020; 84: 2246-2261.