Magnetic resonance imaging (MRI) data were acquired on a 4 Tesla (4T) Bruker MedSpec system; 3D T1-weighted images were obtained with Magnetization Prepared Rapid Gradient (MPR) imaging, and 3D T2-weighted images via turbo spin-echo [see for (Durazzo et al., 2018 (link)) full MRI acquisition details]. The 4T structural images were segmented into grey matter (GM), white matter (WM) and cerebrospinal fluid (CSF) via the Expectation Maximization Segmentation (EMS) method (Van Leemput et al., 1999 (link)) and co-aligned with the SVS volumes of interest for determination of their tissue contribution (i.e., GM, WM, CSF) (Mon et al., 2012 (link); Studholme et al., 2001 ).
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Multimodal MRI Tissue Segmentation
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Corresponding Organization : Stanford University
Other organizations : San Francisco VA Medical Center, University of California, San Francisco
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Variable analysis
independent variables
- Magnetic resonance imaging (MRI) data acquired on a 4 Tesla (4T) Bruker MedSpec system
- 3D T1-weighted images obtained with Magnetization Prepared Rapid Gradient (MPR) imaging
- 3D T2-weighted images via turbo spin-echo
- Grey matter (GM)
- White matter (WM)
- Cerebrospinal fluid (CSF)
- The 4T structural images were segmented into GM, WM and CSF via the Expectation Maximization Segmentation (EMS) method
- The 4T structural images were co-aligned with the SVS volumes of interest for determination of their tissue contribution (i.e., GM, WM, CSF)
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