Update README.md

README.md

@@ -28,15 +28,9 @@ The prior distribution of MRI images learned with generative models has proven t
 ## 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).
 
-## BibTex entry and citation info
-
-```bibtex
-@article{Luo2023,
-  title={Generative Pretrained Image Priors for MRI Reconstruction},
-  author={xxx},
-  year={2023}
-}
-```
+## 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.