| Popis: |
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. |
