spreco


Namespreco JSON
Version 0.0.4 PyPI version JSON
download
home_page
SummaryGenerative image priors for MRI image reconstruction
upload_time2023-08-17 13:58:43
maintainer
docs_urlNone
author
requires_python>=3.7
license
keywords mri reconstruction generative model image priors sampling
VCS
bugtrack_url
requirements No requirements were recorded.
Travis-CI No Travis.
coveralls test coverage No coveralls.
            ## 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](https://github.com/mrirecon/bart)

**Installation:** Clone this repository and use [conda](https://www.anaconda.com/products/individual) to set up the environment.

```shell
$ 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.


            

Raw data

            {
    "_id": null,
    "home_page": "",
    "name": "spreco",
    "maintainer": "",
    "docs_url": null,
    "requires_python": ">=3.7",
    "maintainer_email": "",
    "keywords": "MRI reconstruction,generative model,image priors,sampling",
    "author": "",
    "author_email": "Guanxiong Luo <guanxiong.luo@med.uni-goettingen.de>",
    "download_url": "https://files.pythonhosted.org/packages/3f/77/7bf234f6ab11d00cd1f08ac87746297127b9be68c0b2b51102e3e4ec9b48/spreco-0.0.4.tar.gz",
    "platform": null,
    "description": "## Speed up MR scanner with generative priors for image reconstruction (SPRECO)\n\n<img src=\"./misc/overview.png\" alt=\"workflow\" width=\"350\" align=\"right\"/>\nThis package is to help you train generative image priors of MRI images and then use them in image reconstruction. It has the following features:\n\n1. Distributed training\n2. Interruptible training\n3. Efficient dataloader for medical images\n4. Customizable with a configuration file\n5. Seamless deployment with [BART](https://github.com/mrirecon/bart)\n\n**Installation:** Clone this repository and use [conda](https://www.anaconda.com/products/individual) to set up the environment.\n\n```shell\n$ git clone https://github.com/mrirecon/spreco.git\n$ cd spreco\n$ pip install .\n```\n<!-- \n## Quickstart with colab\n\n1. Sample the posterior \n   - [Jupyter Notebook](https://github.com/mrirecon/spreco/blob/main/examples/scripts/demo_recon.ipynb)\n   - [![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)\n2. Train an image prior\n   - [Jupyter Notebook](https://github.com/mrirecon/spreco/blob/main/examples/scripts/demo_train.ipynb)\n   - [![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)\n3. Using Prior with BART\n   - [Jupyter Notebook](https://github.com/mrirecon/bart-workshop/blob/master/ismrm2021/bart_tensorflow/bart_tf.ipynb)\n   - [![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)-->\n\n## Reference \nWe would appreciate it if you tried our codes and cited our work.\n\n[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)\n\n[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\n\n[3] M. Blumenthal, G. Luo, M. Schilling, HCM. Holme, M. Uecker. Deep, deep learning with BART. Magn Reson Med. 2023; 89: 678- 693.\n\n[4] G. Luo, N. Zhao, W. Jiang, ES. Hui, P. Cao. MRI reconstruction using deep Bayesian estimation. Magn Reson Med. 2020; 84: 2246-2261.\n\n",
    "bugtrack_url": null,
    "license": "",
    "summary": "Generative image priors for MRI image reconstruction",
    "version": "0.0.4",
    "project_urls": {
        "Bug Tracker": "https://github.com/mrirecon/spreco/issues",
        "Homepage": "https://github.com/mrirecon/spreco"
    },
    "split_keywords": [
        "mri reconstruction",
        "generative model",
        "image priors",
        "sampling"
    ],
    "urls": [
        {
            "comment_text": "",
            "digests": {
                "blake2b_256": "ac6b84890433a33fa42d8b007852572a7de64f01ab2f40c12cea274a06f6a85b",
                "md5": "cdfb4b47b3253db54233aecb4505891f",
                "sha256": "d75bbdc1f7bc89a783baac4b3f420875ead6c4aea890e3877ff81fbd0bab00d1"
            },
            "downloads": -1,
            "filename": "spreco-0.0.4-py3-none-any.whl",
            "has_sig": false,
            "md5_digest": "cdfb4b47b3253db54233aecb4505891f",
            "packagetype": "bdist_wheel",
            "python_version": "py3",
            "requires_python": ">=3.7",
            "size": 69842,
            "upload_time": "2023-08-17T13:58:42",
            "upload_time_iso_8601": "2023-08-17T13:58:42.108035Z",
            "url": "https://files.pythonhosted.org/packages/ac/6b/84890433a33fa42d8b007852572a7de64f01ab2f40c12cea274a06f6a85b/spreco-0.0.4-py3-none-any.whl",
            "yanked": false,
            "yanked_reason": null
        },
        {
            "comment_text": "",
            "digests": {
                "blake2b_256": "3f777bf234f6ab11d00cd1f08ac87746297127b9be68c0b2b51102e3e4ec9b48",
                "md5": "d7d7a0198e93293ddc4411db6fc7a688",
                "sha256": "88746ff890cd10f6a3aac048fbbd62ae79a5cfdd118ca135785336c9d6500f05"
            },
            "downloads": -1,
            "filename": "spreco-0.0.4.tar.gz",
            "has_sig": false,
            "md5_digest": "d7d7a0198e93293ddc4411db6fc7a688",
            "packagetype": "sdist",
            "python_version": "source",
            "requires_python": ">=3.7",
            "size": 57810,
            "upload_time": "2023-08-17T13:58:43",
            "upload_time_iso_8601": "2023-08-17T13:58:43.449128Z",
            "url": "https://files.pythonhosted.org/packages/3f/77/7bf234f6ab11d00cd1f08ac87746297127b9be68c0b2b51102e3e4ec9b48/spreco-0.0.4.tar.gz",
            "yanked": false,
            "yanked_reason": null
        }
    ],
    "upload_time": "2023-08-17 13:58:43",
    "github": true,
    "gitlab": false,
    "bitbucket": false,
    "codeberg": false,
    "github_user": "mrirecon",
    "github_project": "spreco",
    "travis_ci": false,
    "coveralls": false,
    "github_actions": false,
    "lcname": "spreco"
}
        
Elapsed time: 0.28604s