A paired dataset of multi-modal MRI at 3 Tesla and 7 Tesla with manual hippocampal subfield segmentations
The hippocampus plays a critical role in memory and is prone to neural degenerative diseases. Its complex structure and distinct subfields pose challenges for automatic segmentation in 3 T MRI because of its limited resolution and contrast. While 7 T MRI offers superior anatomical details and better gray-white matter contrast, aiding in clearer differentiation of hippocampal structures, its use is restricted by high costs. To bridge this gap, algorithms synthesizing 7T-like images from 3 T scans are being developed, requiring paired datasets for training. However, the scarcity of such high-quality paired datasets, particularly those with manual hippocampal subfield segmentations as ground truth, hinders progress. Herein, we introduce a dataset comprising paired 3 T and 7 T MRI scans from 20 healthy volunteers, with manual hippocampal subfield annotations on 7 T T2-weighted images. This dataset is designed to support the development and evaluation of both 3T-to-7T MR image synthesis models and automated hippocampal segmentation algorithms on 3 T images. We assessed the image quality using MRIQC. The dataset is freely accessible on the Figshare+.
Authors
- Debin Zeng (ORCID: https://orcid.org/0000-0002-0138-1314)
- Baoqiang Ma (ORCID: https://orcid.org/0000-0002-6461-1244)
- Lei Chu (ORCID: https://orcid.org/0000-0001-6265-6505)
- Tongtong Che (ORCID: https://orcid.org/0000-0003-4993-0586)
- Xiaoxi Dong (ORCID: https://orcid.org/0000-0003-3852-331X)
- Zihao Zhang (ORCID: https://orcid.org/0000-0003-0808-220X)
- Shuyu Li (ORCID: https://orcid.org/0000-0002-3459-6821)
- Yirong He
Institutions
- University Medical Center Groningen (NL)
- University of Groningen (NL)
- Chinese Academy of Sciences (CN)
- Beijing Normal University (CN)
- Institute of Biophysics (CN)
- University of Chinese Academy of Sciences (CN)
- Beihang University (CN)
Publication Details
- Journal
- Scientific Data
- Published
- 2025-02-13
- DOI
- https://doi.org/10.1038/s41597-025-04586-9
- Citations
- 5
- Primary Topic
- Advanced MRI Techniques and Applications
- Type
- article
- Field-Weighted Citation Impact
- 6.80
Funders
- National Natural Science Foundation of China
- Beijing Normal University
- Youth Innovation Promotion Association of the Chinese Academy of Sciences
- Youth Innovation Promotion Association