Department of Radiology, University of North Carolina, Chapel Hill, NC, 27599, USA.
Biomedical Research Imaging Center (BRIC), University of North Carolina, Chapel Hill, NC, 27599, USA.
Sci Data. 2023 Jul 27;10(1):489. doi: 10.1038/s41597-023-02400-y.
Brain magnetic resonance imaging (MRI) provides detailed soft tissue contrasts that are critical for disease diagnosis and neuroscience research. Higher MRI resolution typically comes at the cost of signal-to-noise ratio (SNR) and tissue contrast, particularly for more common 3 Tesla (3T) MRI scanners. At ultra-high magnetic field strength, 7 Tesla (7T) MRI allows for higher resolution with greater tissue contrast and SNR. However, the prohibitively high costs of 7T MRI scanners deter their widespread adoption in clinical and research centers. To obtain higher-quality images without 7T MRI scanners, algorithms that can synthesize 7T MR images from 3T MR images are under active development. Here, we make available a dataset of paired T1-weighted and T2-weighted MR images at 3T and 7T of 10 healthy subjects to facilitate the development and evaluation of 3T-to-7T MR image synthesis models. The quality of the dataset is assessed using image quality metrics implemented in MRIQC.
脑磁共振成像（MRI）提供了对疾病诊断和神经科学研究至关重要的详细软组织对比。更高的 MRI 分辨率通常是以信噪比（SNR）和组织对比度为代价的，特别是对于更常见的 3 特斯拉（3T）MRI 扫描仪。在超高磁场强度下，7 特斯拉（7T）MRI 允许更高的分辨率，具有更大的组织对比度和 SNR。然而，7T MRI 扫描仪过高的成本阻碍了它们在临床和研究中心的广泛采用。为了在没有 7T MRI 扫描仪的情况下获得更高质量的图像，正在积极开发可以从 3T MRI 图像合成 7T MRI 图像的算法。在这里，我们提供了一个由 10 名健康受试者的 3T 和 7T 的 T1 加权和 T2 加权 MRI 图像对组成的数据集，以促进 3T 到 7T MRI 图像合成模型的开发和评估。该数据集的质量使用 MRIQC 中实现的图像质量指标进行评估。
