---
language: en
license: other
tags:
- mri
- quantitative mri
- reconstruction
- segmentation
- detection
---
# SKM-TEA Sample Data
This dataset consists of a subset of scans from the [SKM-TEA dataset](https://arxiv.org/abs/2203.06823). It can be used to build tutorials / demos with the SKM-TEA dataset.

To access to the full dataset, please follow instructions on [Github](https://github.com/StanfordMIMI/skm-tea/blob/main/DATASET.md).

**NOTE**: This dataset subset *should not* be used for reporting/publishing metrics. All metrics should be computed on the full SKM-TEA test split.

## Details
This mini dataset (~30GB) consists of 2 training scans, 1 validation scan, and 1 test scan from the SKM-TEA dataset. HDF5 files for the Raw Data Track are [lzf-compressed](http://www.h5py.org/lzf/) to reduce size while maximizing speed for decompression.

## License
By using this dataset, you agree to the [Stanford University Dataset Research Use Agreement](https://stanfordaimi.azurewebsites.net/datasets/4aaeafb9-c6e6-4e3c-9188-3aaaf0e0a9e7).

## Reference
If you use this dataset, please reference the SKM-TEA paper:
```
@inproceedings{
desai2021skmtea,
title={{SKM}-{TEA}: A Dataset for Accelerated {MRI} Reconstruction with Dense Image Labels for Quantitative Clinical Evaluation},
author={Arjun D Desai and Andrew M Schmidt and Elka B Rubin and Christopher Michael Sandino and Marianne Susan Black and Valentina Mazzoli and Kathryn J Stevens and Robert Boutin and Christopher Re and Garry E Gold and Brian Hargreaves and Akshay Chaudhari},
booktitle={Thirty-fifth Conference on Neural Information Processing Systems Datasets and Benchmarks Track (Round 2)},
year={2021},
url={https://openreview.net/forum?id=YDMFgD_qJuA}
}
```