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Speed up MR scanner with generative priors for image reconstruction (SPRECO)

<img src="./misc/overview.png" alt="workflow" width="350" align="right"/> This package is to help you train generative image priors of MRI images and then use them in image reconstruction. It has the following features:
  1. Distributed training
  2. Interruptible training
  3. Efficient dataloader for medical images
  4. Customizable with a configuration file
  5. Seamless deployment with BART

Installation: Clone this repository and use conda to set up the environment.

$ git clone https://github.com/mrirecon/spreco.git
$ cd spreco
$ pip install .
<!-- ## Quickstart with colab 1. Sample the posterior - [Jupyter Notebook](https://github.com/mrirecon/spreco/blob/main/examples/scripts/demo_recon.ipynb) - [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/mrirecon/spreco/blob/main/examples/scripts/demo_recon.ipynb) 2. Train an image prior - [Jupyter Notebook](https://github.com/mrirecon/spreco/blob/main/examples/scripts/demo_train.ipynb) - [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/mrirecon/spreco/blob/main/examples/scripts/demo_train.ipynb) 3. Using Prior with BART - [Jupyter Notebook](https://github.com/mrirecon/bart-workshop/blob/master/ismrm2021/bart_tensorflow/bart_tf.ipynb) - [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/mrirecon/bart-workshop/blob/master/ismrm2021/bart_tensorflow/bart_tf.ipynb)-->

Reference

We would appreciate it if you tried our codes and cited our work.

[1] G. Luo, X. Wang, M. Blumenthal, M. Schilling, EHU. Rauf, R. Kotikalapudi, NK. Focke, M. Uecker. Generative image priors for MRI reconstruction trained from magnitude-only images. arXiv preprint arXiv:2308.02340 (2023)

[2] G. Luo, M. Blumenthal, M. Heide, M. Uecker. Bayesian MRI reconstruction with joint uncertainty estimation using diffusion models. Magn Reson Med. 2023; 1-17

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

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