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README.md

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 ## 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 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 the paper.
+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             |
 |-------------------------------|-----------|-----------|----------------------|------------------------------------------------------------------------------------|-----------------------|
@@ -38,7 +38,5 @@ The Berkeley Advanced Reconstruction Toolbox ([BART](https://mrirecon.github.io/
 }
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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.