updated readme.md

README.md

@@ -11,19 +11,22 @@ tags:
 - PixelCNN
 ---
 
-# Generative pretrained models on MRI images.
+## Generative pretrained models on MRI images.
 
-It was introduced in [this paper](https://) and first released at [this page](https://github.com/mrirecon/image-priors). The prior distribution of MRI images learned with generative models has proven to be effective in MRI image reconstruction. Here, we host several generative pretrained models.
+It was introduced in [this paper](https://) and first released at [this page](https://github.com/mrirecon/image-priors). 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. For more details on how these models were trained, please find them in the paper.
 
+| Prior                         | Model     | Phase     | Size                 | Contrast                                                                           | Subscript             |
+|-------------------------------|-----------|-----------|----------------------|------------------------------------------------------------------------------------|-----------------------|
+| $\texttt{P}_\mathrm{SC}$  | PixelCNN  | preserved | 1000                 | T1, T2, T2-FLAIR, T$^*_\mathrm{2}$ | SC - Small, complex   |
+| $\texttt{P}_\mathrm{SM}$  | PixelCNN  | unknown   | 1000                 | T1, T2, T2-FLAIR, 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    |
 
-## Model Description
 
 ## How to use
-
-## Training data
-
-## Limitations
-
+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).
 
 ## BibTex entry and citation info