在多中心数据集中开发和验证周脉体空间分割方法
Peiyu Huang1, Lingyun Liu1, Yao Zhang2
1Department of Radiology, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China; Department of Radiology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China.
NeuroImage
|August 24, 2024
概括
一个新的神经网络,mcPVS-Net,在多中心MRI扫描中准确量化周血管空间 (PVS). 这种工具在各种神经学研究中显示出一致的PVS分析的前景.
科学领域:
- 神经成像是一种神经成像.
- 医疗图像分析 医学图像分析
- 量化MRI是指数量化的MRI.
背景情况:
- 在MRI上周血管空间 (PVS) 是神经疾病的关键指标.
- 定量PVS分析提供了更好的灵敏度和一致性.
- 目前缺乏用于PVS分析的经过验证的多中心方法.
研究的目的:
- 开发和验证一个多中心PVS细分网络 (mcPVS-Net).
- 评估mcPVS-Net在不同MRI扫描仪供应商的准确性和一致性.
- 探索PVS体积与临床因素之间的相关性.
主要方法:
- mcPVS-Net在来自西门子,通用电气和飞利浦扫描仪的多中心3D T1加权 (T1w) MRI数据上进行了训练和测试.
- 对细分精度的评估与地面真相面具和视觉分数进行了对比.
- 在1020名受试者中,分析了PVS体积与临床因素 (年龄,高血压,WMH) 之间的相关性.
- mcPVS-Net还应用于来自联合成像扫描仪的T1w和T2w图像.
主要成果:
- mcPVS-Net实现了0.80的平均DICE系数,证明了良好的细分精度.
- 在不同扫描仪供应商中,细分性能是一致的.
- PVS体积与视觉评分,年龄,高血压和白质超强度 (WMH) 有显著的相关性.
- 在PVS细分精度方面,mcPVS-Net在PVS细分精度方面表现优于以前的方法,特别是在基底性中.
结论:
- mcPVS-Net从3D T1w MRI图像提供了准确的PVS细分.
- 该网络在多个扫描仪供应商之间展示了一致的性能.
- mcPVS-Net是未来多中心PVS研究的一个有前途的工具.
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