Quantifying white matter hyperintensity and brain volumes in heterogeneous clinical and low-field portable MRI
This enables large-scale tracking of brain atrophy and WMH progression, especially in underserved areas where portable MRI is crucial, addressing a bottleneck in neuroimaging for cerebrovascular disease and multiple sclerosis.
The authors tackled the problem of automated segmentation and quantification of white matter hyperintensity and brain volumes from heterogeneous clinical and low-field portable MRI scans, which existing methods cannot handle due to resolution and SNR requirements. They presented a method that segments these features without retraining, achieving strong correlations (e.g., ρ=.85 for WMH, r=.89 for hippocampal volumes) between high- and low-field scans on multiple datasets.
Brain atrophy and white matter hyperintensity (WMH) are critical neuroimaging features for ascertaining brain injury in cerebrovascular disease and multiple sclerosis. Automated segmentation and quantification is desirable but existing methods require high-resolution MRI with good signal-to-noise ratio (SNR). This precludes application to clinical and low-field portable MRI (pMRI) scans, thus hampering large-scale tracking of atrophy and WMH progression, especially in underserved areas where pMRI has huge potential. Here we present a method that segments white matter hyperintensity and 36 brain regions from scans of any resolution and contrast (including pMRI) without retraining. We show results on eight public datasets and on a private dataset with paired high- and low-field scans (3T and 64mT), where we attain strong correlation between the WMH ($ρ$=.85) and hippocampal volumes (r=.89) estimated at both fields. Our method is publicly available as part of FreeSurfer, at: http://surfer.nmr.mgh.harvard.edu/fswiki/WMH-SynthSeg.