BACKGROUND
Enlarged perivascular spaces (ePVS) are a marker of cerebral small vessel disease, potentially reflecting reduced waste clearance. Because manual quantification is unfeasible in large datasets, we developed and evaluated an automated tool.
METHODS
Detection Of Regions of Enlarged perivascular Spaces (DORES), a 3D nnU-Net-based deep learning algorithm was developed for ePVS segmentation using T1-weighted and fluid-attenuated inversion recovery magnetic resonance imaging (MRI). DORES was developed in two stages: an initial model trained on 35 manually segmented scans and a final model on 1460 pseudo-labeled sessions from the Vanderbilt Memory and Aging Project (VMAP). A subset of VMAP participants with 3 T brain MRI underwent whole-brain manual ePVS tracing (n = 35, 73 ± 9 years, 51% male) and visual rating (n = 388, 71 ± 8 years, 54% male) by a neuroradiologist. DORES was evaluated and compared against three other segmentation tools using Dice and F1 scores, absolute volume and element differences, correlation, and agreement. External validation used an Alzheimer's Disease Neuroimaging Initiative 3 subset with manual tracings (ADNI3, n = 18, 73 ± 9 years, 67% female).
RESULTS
DORES achieved Dice scores of 0.61 ± 0.16 (white matter) and 0.72 ± 0.08 (basal ganglia) in VMAP, with strong correlations and agreement for ePVS count and volume. Performances modestly declined in ADNI3 across algorithms. Scanner-stratified analyses showed stronger correlations for Philips versus Siemens images in the basal ganglia, indicating scanner-dependent differences in measurement consistency.
CONCLUSIONS
DORES provides a multimodal nnU-Net-based pipeline for ePVS segmentation in older adults. The model demonstrates robust within-cohort performance and reasonable external validity, though scanner-related effects limit application across sites.
James D. LeFevre, W. Robb, Dandan Liu et al.· Magnetic Resonance Imaging· 0 citations
Nonlinear gradients alter the diffusion encoding in brain diffusion tensor imaging (DTI), leading to spatially varying diffusion weighting which bias quantitative measures if uncorrected. Although the overall effects of gradient nonlinearity correction in brain studies are typically minimal and often fall below the detection limits of traditional imaging resolutions and sensitivities, their cumulative impact on clinical outcomes requires further study. This study investigates the significance and effects of correcting gradient nonlinearity in DW-MRI, focusing on the microstructural and macrostructural changes in white matter (WM) and gray matter (GM) across a clinical cohort. Our primary aim is to clarify whether the observed nonlinearity significantly alters the interpretation of aging in clinical settings, particularly in studies comparing healthy individuals to those with neurological conditions. We assess the extent of nonlinear fields impact on individual scans, interscanner observations, and a tract-based analysis. Using data from the Vanderbilt Memory & Aging Project (n = 948 imaging sessions, 933 on Scanner B and 15 on Scanner A acquired with single-shell diffusion tensor imaging protocol), we find 1%, 3.3%, and 5-degree changes in microstructure measures, fractional anisotropy (FA), mean diffusivity (MD), and primary eigen vector (V1) respectively, affecting at least 20% of the brain. Across sessions, head positioning sampled typical clinical variability, with head offsets of approximately 0–10 mm and rotations of 0–10° relative to magnet isocenter. Subcortical regions in the superior regions, occipital lobules, and parietal lobules exhibit relatively higher impacts. Macrostructural measures show changes up to 12% after nonlinear field correction. GNL effects are 5% and 0.33% of FA and MD changes between mild cognitive impairment and controls. A simple power analysis indicates that these subtle effects of gradient nonlinearity correction can become statistically detectable in larger multi-site studies exceeding ~1000 subjects, suggesting that GNL should be considered and, where possible, corrected or at least quantified in such settings.
Praitayini Kanakaraj, Tianyuan Yao, Zhiyuan Li et al.· PLoS ONE· 0 citations