fastMRI:原始MRI测量值和临床MRI图像的大规模数据集

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快速核磁共振 | | 通过获取更少的测量值来加快磁共振成像(MRI)的潜力,可以降低医疗成本,将对患者的压力降到最低,并使MR成像在目前速度缓慢或昂贵的应用中成为可能。 是Facebook AI Research(FAIR)和NYU Langone Health的一项合作研究项目,旨在研究使用AI来加快MRI扫描的速度。 纽约大学朗格健康中心已经发布了完全匿名的膝盖和大脑MRI数据集,可以从下载。 可以找到与fastMRI项目相关的出版物。 该存储库包含方便的PyTorch数据加载器,子采样功能,评估指标以及简单基准方法的参考实现。 它还包含fastMRI项目的某些出版物中方法的实现
fastMRI-master.zip
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# fastMRI [![LICENSE](https://img.shields.io/badge/license-MIT-blue.svg)](https://github.com/facebookresearch/fastMRI/blob/master/LICENSE.md) [![CircleCI](https://circleci.com/gh/facebookresearch/fastMRI.svg?style=svg)](https://app.circleci.com/pipelines/github/facebookresearch/fastMRI) [Website and Leaderboards](https://fastMRI.org) | [Dataset](https://fastmri.med.nyu.edu/) | [GitHub](https://github.com/facebookresearch/fastMRI) | [Publications](#list-of-papers) Accelerating Magnetic Resonance Imaging (MRI) by acquiring fewer measurements has the potential to reduce medical costs, minimize stress to patients and make MR imaging possible in applications where it is currently prohibitively slow or expensive. [fastMRI](https://fastMRI.org) is a collaborative research project from Facebook AI Research (FAIR) and NYU Langone Health to investigate the use of AI to make MRI scans faster. NYU Langone Health has released fully anonymized knee and brain MRI datasets that can be downloaded from [the fastMRI dataset page](https://fastmri.med.nyu.edu/). Publications associated with the fastMRI project can be found [at the end of this README](#list-of-papers). This repository contains convenient PyTorch data loaders, subsampling functions, evaluation metrics, and reference implementations of simple baseline methods. It also contains implementations for methods in some of the publications of the fastMRI project. ## Documentation Documentation for the fastMRI dataset and baseline reconstruction performance can be found in [our paper on arXiv](https://arxiv.org/abs/1811.08839). The paper is updated on an ongoing basis for dataset additions and new baselines. For code documentation, most functions and classes have accompanying docstrings that you can access via the `help` function in IPython. For example: ```python from fastmri.data import SliceDataset help(SliceDataset) ``` ## Dependencies and Installation We have tested this code using: * Ubuntu 18.04 * Python 3.8 * CUDA 10.1 * CUDNN 7.6.5 First install PyTorch according to the directions at the [PyTorch Website](https://pytorch.org/get-started/) for your operating system and CUDA setup. Then, navigate to the `fastmri` root directory and run ```bash pip install -e . ``` `pip` will handle all package dependencies. After this you should be able to run most of the code in the repository. ## Package Structure & Usage The repository is centered around the `fastmri` module. The following breaks down the basic structure: `fastmri`: Contains a number of basic tools for complex number math, coil combinations, etc. * `fastmri.data`: Contains data utility functions from original `data` folder that can be used to create sampling masks and submission files. * `fastmri.models`: Contains reconstruction models, such as the U-Net and VarNet. * `fastmri.pl_modules`: PyTorch Lightning modules for data loading, training, and logging. ## Examples and Reproducibility The `fastmri_examples` and `banding_removal` folders include code for reproducibility. The baseline models were used in the arXiv paper: [fastMRI: An Open Dataset and Benchmarks for Accelerated MRI ({J. Zbontar*, F. Knoll*, A. Sriram*} et al., 2018)](https://arxiv.org/abs/1811.08839) A brief summary of implementions based on papers with links to code follows. For completeness we also mention work on active acquisition, which is hosted in another repository. * **Baseline Models** * [Zero-filled examples for saving images for leaderboard submission](fastmri_examples/zero_filled/) * [ESPIRiT—an eigenvalue approach to autocalibrating parallel MRI: where SENSE meets GRAPPA (M. Uecker et al., 2013)](fastmri_examples/cs/) * [U-Net: Convolutional networks for biomedical image segmentation (O. Ronneberger et al., 2015)](fastmri_examples/unet/) * **Sampling, Reconstruction and Artifact Correction** * [Offset Sampling Improves Deep Learning based Accelerated MRI Reconstructions by Exploiting Symmetry (A. Defazio, 2019)](banding_removal/fastmri/common/subsample.py#L126-L198) * [End-to-End Variational Networks for Accelerated MRI Reconstruction ({A. Sriram*, J. Zbontar*} et al., 2020)](fastmri_examples/varnet/) * [MRI Banding Removal via Adversarial Training (A. Defazio, et al., 2020)](banding_removal) * **Active Acquisition** (external repository) * [Reducing uncertainty in undersampled MRI reconstruction with active acquisition (Z. Zhang et al., 2019)](https://github.com/facebookresearch/active-mri-acquisition/tree/master/activemri/experimental/cvpr19_models) * [Active MR k-space Sampling with Reinforcement Learning (L. Pineda et al., 2020)](https://github.com/facebookresearch/active-mri-acquisition) ## Testing Run `pytest tests`. By default integration tests that use the fastMRI data are skipped. If you would like to run these tests, set `SKIP_INTEGRATIONS` to `False` in the [conftest](tests/conftest.py). ## Training a model The [data README](fastmri/data/README.md) has a bare-bones example for how to load data and incorporate data transforms. This [jupyter notebook](fastMRI_tutorial.ipynb) contains a simple tutorial explaining how to get started working with the data. Please look at [this U-Net demo script](fastmri_examples/unet/train_unet_demo.py) for an example of how to train a model using the PyTorch Lightning framework. ## Submitting to the Leaderboard Run your model on the provided test data and create a zip file containing your predictions. `fastmri` has a `save_reconstructions` function that saves the data in the correct format. Upload the zip file to any publicly accessible cloud storage (e.g. Amazon S3, Dropbox etc). Submit a link to the zip file on the [challenge website](https://fastmri.org/submit). You will need to create an account before submitting. ## License fastMRI is MIT licensed, as found in the [LICENSE file](LICENSE.md). ## Cite If you use the fastMRI data or code in your project, please cite the arXiv paper: ```BibTeX @inproceedings{zbontar2018fastMRI, title={{fastMRI}: An Open Dataset and Benchmarks for Accelerated {MRI}}, author={Jure Zbontar and Florian Knoll and Anuroop Sriram and Tullie Murrell and Zhengnan Huang and Matthew J. Muckley and Aaron Defazio and Ruben Stern and Patricia Johnson and Mary Bruno and Marc Parente and Krzysztof J. Geras and Joe Katsnelson and Hersh Chandarana and Zizhao Zhang and Michal Drozdzal and Adriana Romero and Michael Rabbat and Pascal Vincent and Nafissa Yakubova and James Pinkerton and Duo Wang and Erich Owens and C. Lawrence Zitnick and Michael P. Recht and Daniel K. Sodickson and Yvonne W. Lui}, journal = {ArXiv e-prints}, archivePrefix = "arXiv", eprint = {1811.08839}, year={2018} } ``` ## List of Papers The following lists titles of papers from the fastMRI project. The corresponding abstracts, as well as links to preprints and code can be found [here](LIST_OF_PAPERS.md). 1. Zbontar, J., Knoll, F., Sriram, A., Murrell, T., Huang, Z., Muckley, M. J., ... & Lui, Y. W. (2018). [fastMRI: An open dataset and benchmarks for accelerated MRI](https://arxiv.org/abs/1811.08839). *arXiv preprint arXiv:1811.08839*. 2. Zhang, Z., Romero, A., Muckley, M. J., Vincent, P., Yang, L., & Drozdzal, M. (2019). [Reducing uncertainty in undersampled MRI reconstruction with active acquisition](https://openaccess.thecvf.com/content_CVPR_2019/html/Zhang_Reducing_Uncertainty_in_Undersampled_MRI_Reconstruction_With_Active_Acquisition_CVPR_2019_paper.html). In *Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition*, pages 2049-2058. 3. Defazio, A. (2019). [Offset Sampling Improves Deep Learning based Accelerated MRI Reconstructions by Exploiting Symmetry](https://arxiv.org/abs/1912.01101). *arXiv preprint, arXiv:1912.01101*. 4. Knoll, F., Zbontar, J., Sriram, A., Muckley, M. J., Bruno, M., Defazio, A., ... & Lui, Y. W. (2020). [fastMRI: A Publicly Available Raw k-Space and DICOM Dataset of Knee Images for Accelerated MR Image Recons
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