A paired dataset of multi-modal MRI at 3 Tesla and 7 Tesla with manual hippocampal subfield segmentations

Lei Chu, Baoqiang Ma, Xiaoxi Dong, Yirong He, Tongtong Che, Debin Zeng, Zihao Zhang, Shuyu Li

Research output: Contribution to journalArticleAcademicpeer-review

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+.

Original languageEnglish
Article number260
JournalScientific data
Volume12
Issue number1
DOIs
Publication statusPublished - 13 Feb 2025
Externally publishedYes

Keywords

  • Adult
  • Algorithms
  • Female
  • Healthy Volunteers
  • Hippocampus/diagnostic imaging
  • Humans
  • Image Processing, Computer-Assisted
  • Magnetic Resonance Imaging
  • Male

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