DiGregorio J, Gibicar A, Khosravani H, Maralani P Jabehdar, Tardif J-C, Tyrrell P N, Moody A R, Khademi A
Electrical, Computer and Biomedical Engineering Dept., Ryerson University, Toronto, ON, Canada.
Division of Neurology, Department of Medicine, University of Toronto, Toronto, ON, Canada.
Neuroimage Rep. 2022 Mar 8;2(2):100091. doi: 10.1016/j.ynirp.2022.100091. eCollection 2022 Jun.
Fluid-attenuated inversion recovery (FLAIR) MRI has emerged as an important sequence for the analysis of cerebrovascular (CVD) and Alzheimer's disease (AD). Large-scale, automated cross-sectional and longitudinal cerebral biomarker extraction from FLAIR datasets could progress disease characterization, improve disease monitoring, and help to determine optimal intervention times. Despite this, most automated biomarker extraction algorithms are designed for T1-weighted or multi-modal inputs. In this work, automated tools were used to extract biomarkers from large, FLAIR-only datasets to evaluate the feasibility of this sequence to characterize healthy, AD, and CVD subjects in a similar manner to traditional approaches. Total brain volume (TBV), cerebrospinal fluid (CSF) volume, and white matter lesion (WML) volume was measured for the cross-sectional biomarkers and the corresponding annual rates of change over multiple scans represented the longitudinal biomarkers. Biomarkers were extracted from two dementia cohorts (4356 vol, 162 233 images) and one vascular disease cohort (869 vol, 42 850 images) using deep learning-based segmentation algorithms designed specifically for FLAIR. Biomarkers from all cohorts were summarized using descriptive statistics, correlation analysis, and ANCOVA to assess differences in diagnostic labels while accounting for demographic and acquisition factors. Biomarkers from FLAIR MRI had similar trends with those extracted from traditional modalities in the literature for characterizing healthy, AD, and CVD subjects. This demonstrates that FLAIR MRI can be used for end-to-end analysis of large AD and CVD datasets, which can lower acquisition costs, simplify clinical translation, and reduce measurement error associated with multi-modal approaches.
液体衰减反转恢复（FLAIR）磁共振成像已成为分析脑血管疾病（CVD）和阿尔茨海默病（AD）的重要序列。从FLAIR数据集中大规模、自动提取横断面和纵向脑生物标志物，有助于疾病特征描述、改善疾病监测，并有助于确定最佳干预时间。尽管如此，大多数自动生物标志物提取算法是为T1加权或多模态输入设计的。在这项工作中，使用自动化工具从仅包含FLAIR图像的大型数据集中提取生物标志物，以评估该序列以与传统方法类似的方式对健康、AD和CVD受试者进行特征描述的可行性。测量了全脑体积（TBV）、脑脊液（CSF）体积和白质病变（WML）体积作为横断面生物标志物，多次扫描中相应的年变化率则代表纵向生物标志物。使用专门为FLAIR设计的基于深度学习的分割算法，从两个痴呆队列（4356例，162233张图像）和一个血管疾病队列（869例，42850张图像）中提取生物标志物。使用描述性统计、相关性分析和协方差分析对所有队列的生物标志物进行总结，以评估诊断标签的差异，同时考虑人口统计学和采集因素。在对健康、AD和CVD受试者进行特征描述时，FLAIR磁共振成像提取的生物标志物与文献中从传统模态提取的生物标志物具有相似的趋势。这表明FLAIR磁共振成像可用于对大型AD和CVD数据集进行端到端分析，这可以降低采集成本、简化临床转化并减少与多模态方法相关的测量误差。
