Quantifying white matter hyperintensity and brain volumes in heterogeneous clinical and low-field portable MRI
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arXiv
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| Format: | Preprint |
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2023
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| author | Laso, Pablo Cerri, Stefano Sorby-Adams, Annabel Guo, Jennifer Mateen, Farrah Goebl, Philipp Wu, Jiaming Liu, Peirong Li, Hongwei Young, Sean I. Billot, Benjamin Puonti, Oula Sze, Gordon Payabavash, Sam DeHavenon, Adam Sheth, Kevin N. Rosen, Matthew S. Kirsch, John Strisciuglio, Nicola Wolterink, Jelmer M. Eshaghi, Arman Barkhof, Frederik Kimberly, W. Taylor Iglesias, Juan Eugenio |
| author_facet | Laso, Pablo Cerri, Stefano Sorby-Adams, Annabel Guo, Jennifer Mateen, Farrah Goebl, Philipp Wu, Jiaming Liu, Peirong Li, Hongwei Young, Sean I. Billot, Benjamin Puonti, Oula Sze, Gordon Payabavash, Sam DeHavenon, Adam Sheth, Kevin N. Rosen, Matthew S. Kirsch, John Strisciuglio, Nicola Wolterink, Jelmer M. Eshaghi, Arman Barkhof, Frederik Kimberly, W. Taylor Iglesias, Juan Eugenio |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2312_05119 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Quantifying white matter hyperintensity and brain volumes in heterogeneous clinical and low-field portable MRI Laso, Pablo Cerri, Stefano Sorby-Adams, Annabel Guo, Jennifer Mateen, Farrah Goebl, Philipp Wu, Jiaming Liu, Peirong Li, Hongwei Young, Sean I. Billot, Benjamin Puonti, Oula Sze, Gordon Payabavash, Sam DeHavenon, Adam Sheth, Kevin N. Rosen, Matthew S. Kirsch, John Strisciuglio, Nicola Wolterink, Jelmer M. Eshaghi, Arman Barkhof, Frederik Kimberly, W. Taylor Iglesias, Juan Eugenio Image and Video Processing Computer Vision and Pattern Recognition 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. |
| title | Quantifying white matter hyperintensity and brain volumes in heterogeneous clinical and low-field portable MRI |
| topic | Image and Video Processing Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2312.05119 |