TY - UNPB
T1 - Quantifying white matter hyperintensity and brain volumes in heterogeneous clinical and low-field portable MRI
AU - Laso, Pablo
AU - Cerri, Stefano
AU - Sorby-Adams, Annabel
AU - Guo, Jennifer
AU - Mateen, Farrah
AU - Goebl, Philipp
AU - Wu, Jiaming
AU - Liu, Peirong
AU - Li, Hongwei
AU - Young, Sean I.
AU - Billot, Benjamin
AU - Puonti, Oula
AU - Sze, Gordon
AU - Payabavash, Sam
AU - DeHavenon, Adam
AU - Sheth, Kevin N.
AU - Rosen, Matthew S.
AU - Kirsch, John
AU - Strisciuglio, Nicola
AU - Wolterink, Jelmer M.
AU - Eshaghi, Arman
AU - Barkhof, Frederik
AU - Kimberly, W. Taylor
AU - Iglesias, Juan Eugenio
PY - 2023/12/8
Y1 - 2023/12/8
N2 - 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 ($\rho$=.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.
AB - 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 ($\rho$=.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.
KW - eess.IV
KW - cs.CV
U2 - 10.48550/arXiv.2312.05119
DO - 10.48550/arXiv.2312.05119
M3 - Preprint
BT - Quantifying white matter hyperintensity and brain volumes in heterogeneous clinical and low-field portable MRI
PB - ArXiv.org
ER -