Journal Article Dataset Epublish
PubMed: 39948093 DOI: 10.1038/s41597-025-04586-9
A paired dataset of multi-modal MRI at 3 Tesla and 7 Tesla with manual hippocampal subfield segmentations.
Sci Data 12(1):260
Authors: Lei Chu, Baoqiang Ma, Xiaoxi Dong, Yirong He, Tongtong Che, Debin Zeng, Zihao Zhang, Shuyu Li
Affiliations
- Beijing Advanced Innovation Center for Biomedical Engineering, School of Biological Science & Medical Engineering, Beihang University, Beijing, 100083, China.
- Department of Radiation Oncology, University Medical Center Groningen, University of Groningen, Groningen, the Netherlands.
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, 100875, China.
- State Key Laboratory of Brain and Cognitive Science, Institute of Biophysics, Chinese Academy of Sciences, Beijing, 100101, China. Anhui Province Key Laboratory of Biomedical Imaging and Intelligent Processing, Institute of Artificial Intelligence, Hefei Comprehensive National Science Center, Hefei, 230088, China. University of the Chinese Academy of Sciences, Beijing, 100049, China.
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, 100875, China.
Abstract
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+.
MeSH
Multiple Chronic Conditions, Algorithms, Information Storage And Retrieval