在多扫描仪,多模式MRI中,可变质量的中间叶子区域的自动细分
Yue Li1,2, Long Xie3, Pulkit Khandelwal1
1Penn Image Computing and Science Laboratory, University of Pennsylvania, Philadelphia, USA.
bioRxiv : the preprint server for biology
|June 3, 2024
概括
一个新的多模态MRI细分模型改善了阿尔茨海默病的中间叶子区域分析,即使在图像质量差的情况下也提供了强大的神经退行症跟踪.
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
- 阿尔茨海默病的研究研究.
背景情况:
- 来自MRI的中叶 (MTL) 亚区域体积测量有助于跟踪阿尔茨海默氏症 (AD) 神经退行.
- 低质量的MRI可以影响MTL亚区域细分的准确性.
- 不同的MRI模式为成像特定的MTL结构提供了独特的优势.
研究的目的:
- 开发一种多模式的MRI细分模型,用于强大的MTL亚区域分析.
- 提高神经退行生物标志物在阿尔茨海默病的可靠性,特别是在不同的图像质量.
- 为了利用7特斯拉 (7T) 和3特斯拉 (3T) 的结构性MRI进行增强的细分.
主要方法:
- 开发了一个多模式细分模型,使用197名参与者的7T和3T结构性MRI数据.
- 该模型基于nnU-Net,使用多个注册的MRI模式 (T1w,T2w) 作为输入.
- 在训练期间使用高斯噪声增强模式来增强强度.
- 通过比较Amyloid+ MCI (A+MCI) 与Amyloid-认知不受损 (A-CU) 组的歧视和纵向稳定性来评估性能.
主要成果:
- 多模式模型无论7T-T2w图像质量如何,都取得了良好的性能.
- 单模模式模型显示,在质量差的图像中,细分不足.
- 多模式模型显示了A+MCI和A-CU组之间的优异歧视.
- 与单一模式方法相比,使用多模式模型观察到更高的纵向细分一致性.
结论:
- 开发的多模式MRI细分模型为阿尔茨海默病中MTL神经退行提供了强大的生物标志物.
- 该模型对图像质量变化的弹性提高了其临床实用性.
- 这一框架为其他利用多式联络神经成像数据的研究提供了潜在的好处.
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