update readme

README.md

@@ -36,47 +36,9 @@ The Berkeley Advanced Reconstruction Toolbox ([BART](https://mrirecon.github.io/
   author={xxx},
   year={2023}
 }
+```
+[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
 
-@Article{Luo_Magn.Reson.Med._2023,
-  author    = {Guanxiong Luo and Moritz Blumenthal and Martin Heide and Martin Uecker},
-  title     = {Bayesian {MRI} reconstruction with joint uncertainty
-estimation using diffusion models},
-  doi       = {10.1002/mrm.29624},
-  number    = {4},
-  pages     = {1--17},
-  volume    = {84},
-  journal   = {Magn. Reson. Med.},
-  month     = {apr},
-  publisher = {Wiley},
-  year      = {2023},
-}
-
-@article{Blumenthal_Magn.Reson.Med._2023,
-author       = {Blumenthal, Moritz and Luo, Guanxiong and Schilling, Martin and Holme, H. Christian M. and Uecker, Martin},
-title        = {Deep, deep learning with BART},
-journal      = {Magnetic Resonance in Medicine},
-volume       = {89},
-number       = {2},
-pages        = {678-693},
-keywords     = {automatic differentiation, deep learning, image reconstruction, inverse problems, MRI, parallel imaging},
-doi          = {https://doi.org/10.1002/mrm.29485},
-url          = {https://onlinelibrary.wiley.com/doi/abs/10.1002/mrm.29485},
-eprint       = {https://onlinelibrary.wiley.com/doi/pdf/10.1002/mrm.29485},
-abstract     = {Purpose To develop a deep-learning-based image reconstruction framework for reproducible research in MRI. Methods The BART toolbox offers a rich set of implementations of calibration and reconstruction algorithms for parallel imaging and compressed sensing. In this work, BART was extended by a nonlinear operator framework that provides automatic differentiation to allow computation of gradients. Existing MRI-specific operators of BART, such as the nonuniform fast Fourier transform, are directly integrated into this framework and are complemented by common building blocks used in neural networks. To evaluate the use of the framework for advanced deep-learning-based reconstruction, two state-of-the-art unrolled reconstruction networks, namely the Variational Network and MoDL, were implemented. Results State-of-the-art deep image-reconstruction networks can be constructed and trained using BART's gradient-based optimization algorithms. The BART implementation achieves a similar performance in terms of training time and reconstruction quality compared to the original implementations based on TensorFlow. Conclusion By integrating nonlinear operators and neural networks into BART, we provide a general framework for deep-learning-based reconstruction in MRI.},
-year         = {2023}
-}
-
-@Article{Luo_Magn.Reson.Med._2020,
-  author    = {Guanxiong Luo and Na Zhao and Wenhao Jiang and Edward S. Hui and Peng Cao},
-  title     = {{MRI} reconstruction using deep Bayesian estimation},
-  doi       = {https://doi.org/10.1002/mrm.28274},
-  number    = {4},
-  pages     = {2246--2261},
-  volume    = {84},
-  journal   = {Magn. Reson. Med.},
-  month     = {apr},
-  publisher = {Wiley},
-  year      = {2020},
-}
+[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.