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

IF 6.9 2区 综合性期刊 Q1 MULTIDISCIPLINARY SCIENCES Scientific Data Pub Date : 2025-02-13 DOI:10.1038/s41597-025-04586-9
Lei Chu, Baoqiang Ma, Xiaoxi Dong, Yirong He, Tongtong Che, Debin Zeng, Zihao Zhang, Shuyu Li
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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+.

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3特斯拉和7特斯拉的多模态MRI配对数据集,手工海马子区分割。
海马体在记忆中起着至关重要的作用,容易发生神经退行性疾病。其复杂的结构和不同的子场,由于分辨率和对比度的限制,给3t MRI的自动分割带来了挑战。虽然7t MRI提供了优越的解剖细节和更好的灰质对比,有助于更清晰地区分海马结构,但其使用受到高成本的限制。为了弥补这一差距,正在开发从3t扫描中合成7t类图像的算法,需要成对的数据集进行训练。然而,这种高质量配对数据集的稀缺性,特别是那些人工海马子区分割作为基础事实的数据集,阻碍了进展。在此,我们引入了一个数据集,其中包括来自20名健康志愿者的配对3t和7t MRI扫描,并对7t t2加权图像进行了人工海马子区注释。该数据集旨在支持3t至7t MR图像合成模型的开发和评估,以及3t图像上的自动海马分割算法。我们使用MRIQC评估图像质量。该数据集可以在Figshare+上免费访问。
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来源期刊
Scientific Data
Scientific Data Social Sciences-Education
CiteScore
11.20
自引率
4.10%
发文量
689
审稿时长
16 weeks
期刊介绍: Scientific Data is an open-access journal focused on data, publishing descriptions of research datasets and articles on data sharing across natural sciences, medicine, engineering, and social sciences. Its goal is to enhance the sharing and reuse of scientific data, encourage broader data sharing, and acknowledge those who share their data. The journal primarily publishes Data Descriptors, which offer detailed descriptions of research datasets, including data collection methods and technical analyses validating data quality. These descriptors aim to facilitate data reuse rather than testing hypotheses or presenting new interpretations, methods, or in-depth analyses.
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