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扩散模型使得对阿尔茨海默病的定量CBF分析成为可能

Qinyang Shou¹, Steven Cen², Nan-Kuei Chen³

¹Laboratory of Functional MRI Technology (LOFT), Stevens Neuroimaging and Informatics Institute, University of Southern California, Los Angeles, CA, United States.

medRxiv : the preprint server for health sciences

|July 15, 2024

概括

生成性扩散模型可以将缺失的M0图像归咎于动脉旋转标记 (ASL),以便在阿尔茨海默氏症 (AD) 研究中量化大脑血流 (CBF). 这种方法准确量化CBF,有助于AD诊断.

科学领域:

神经成像是一种神经成像.
人工智能的人工智能
生物医学工程生物医学工程

背景情况:

通过动脉旋转标记 (ASL) 测量的大脑血流 (CBF) 是阿尔茨海默病 (AD) 的关键生物标志物.
阿尔茨海默病神经成像计划 (ADNI) 数据集包含来自多个供应商的ASL数据,但西门子扫描仪中缺少的M0图像阻碍了CBF量化.
输入缺失的M0图像对于利用AD研究中大规模ASL数据集至关重要.

研究的目的:

开发和验证一个生成扩散模型,用于在西门子ASL数据中赋值缺失的M0图像.
通过使用ADNI数据集,为AD研究提供准确的CBF量化.
为了比较假定的CBF数据与获得的CBF数据在区分AD阶段和预测AD的性能.

主要方法:

训练了一种有条件隐性扩散模型,以生成M0图像.
该模型使用图像相似度指标,CBF量化准确度和物理模型一致性在内部数据集 (N=55) 上进行了验证.
已验证的模型应用于ADNI西门子数据集 (N=211) 以计算CBF的M0图像,并与GE获得的数据进行比较.

主要成果:

扩散模型产生了高保真度的M0图像 (SSIM=0.924±0.019,PSNR=33.348±1.831).这些图像的分辨率是:
假设的CBF数据显示了最小的偏差 (平均差异=1.07±2.12ml/100g/min) 和在AD阶段和分类性能中可比较的差异化模式与获得的数据.
与定性输液数据相比,生成的CBF数据改善了AD阶段分类的准确性.

结论:

生成性扩散模型对于在ASL数据中赋值缺失的M0模式是有效的.
这种方法可以在大型神经成像研究中进行强大的CBF量化,特别是在阿尔茨海默病中.
该方法具有很大的潜力,可以通过释放现有的ASL数据集的全部价值来推动AD研究.

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