by Jingyun "Josh" Chen at Neurology Dept, NYU School of Medicine
Subdivide white matter hyperintensities (WMH) into Periventricular WMHs (PVWMHs), Deep WMHs (DWMHs), and (optional) Juxtaventricular WMHs (JVWMHs).
Usage: Out = wmhs_method(Mask_WMH,Mask_WM,Dmap_Vent,Dmap_Cort,Method)
- Mask_WMH - binary mask of total WMHs.
- Mask_WM - birnary mask of total white matter.
- Dmap_Vent - distance map to lateral ventricles.
- Dmap_Cort - distance map to cortex.
- Method - Subdivision methods, must be one of following options:
- "DM10" - Classic Dilation Mask (DM10) method classifies WMH voxels less than 10mm to ventricle walls as PVWMHs, and the rest as DWMHs [2].
- "DM313" - Modified Dilation Mask (DM313) method classifies WMH voxels less than 3mm to ventricle walls as JVWMHs, between 3mm-13mm as PVWMHs, and the rest as DWMHs.
- "CC" - Connected Component (CC) method classifies continued WMH clusters touching ventricle walls as PVWMHs, and the rest as DWMHs [2].
- "BD" - Bilateral Distance (BD) method computes a WMH voxel’s closest distances to ventricle and to crebral cortex, and classifies the voxel as PVWMH if it is closer to ventricle than to cortex, otherwise as DWMH [1].
- Out - mask of WMHs subdivisions, of same dimension as M.
- Label values: 0 - Background; 1 - DWMHs; 2 - PVWMHs; 3 - JVWMHs (for "DM313" method only).
- Dimension must be same for all inputs masks and distance maps.
- Matlab Image Processing Toolbox must be installed for "CC" subdivision method.
- J. Chen, A. Mikheev, H. Yu, M. D. Gruen, H. Rusinek, Y. Ge. Bilateral distance classification of periventricular and deep white matter hyperintensities with applications to mild cognitive impairment and Alzheimer’s disease study, Academic Radiology, 2020 (in press) https://doi.org/10.1016/j.acra.2020.07.039.
- Griffanti L, Jenkinson M, Suri S, Zsoldos E, Mahmood A, Filippini N, et al. Classification and characterization of periventricular and deep white matter hyperintensities on MRI: A study in older adults. NeuroImage. 2018;170:174-81. doi:https://doi.org/10.1016/j.neuroimage.2017.03.024.