Multimodal MEMPRAGE, FLAIR, and R2* Segmentation to Resolve Dura and Vessels from Cortical Gray Matter

Abstract

While widely in use in automated segmentation approaches for the detection of group differences or of changes associated with continuous predictors in gray matter volume, T1-weighted images are known to represent dura and cortical vessels with signal intensities similar to those of gray matter. By considering multiple signal sources at once, multimodal segmentation approaches may be able to resolve these different tissue classes and address this potential confound. We explored here the simultaneous use of FLAIR and apparent transverse relaxation rates (a signal related to T2* relaxation maps and having similar contrast) with T1-weighted images. Relative to T1-weighted images alone, multimodal segmentation had marked positive effects on 1. the separation of gray matter from dura, 2. the exclusion of vessels from the gray matter compartment, and 3. the contrast with extracerebral connective tissue. While obtainable together with the T1-weighted images without increasing scanning times, apparent transverse relaxation rates were less effective than added FLAIR images in providing the above mentioned advantages. FLAIR images also improved the detection of cortical matter in areas prone to susceptibility artifacts in standard MPRAGE T1-weighted images, while the addition of transverse relaxation maps exacerbated the effect of these artifacts on segmentation. Our results confirm that standard MPRAGE segmentation may overestimate gray matter volume by wrongly assigning vessels and dura to this compartment and show that multimodal approaches may greatly improve the specificity of cortical segmentation. Since multimodal segmentation is easily implemented, these benefits are immediately available to studies focusing on translational applications of structural imaging.

MP2RAGE multispectral voxel‐based morphometry in focal epilepsy

“…One important reason is the misclassification of dura and vessel sinuses as gray matter. The overestimation is mainly due to the close proximity and similarity in signal intensity of these structures to the cortex (Lindig et al, ; Viviani, Pracht, et al, ). The addition of FLAIR did resolve this misclassification for both the T1 and MP2.…”

“…Improved tissue segmentation can be achieved through combining multispectral MRI contrasts in the segmentation process (Alfano et al, 1997;Ashburner & Friston, 1997;Ashburner & Friston, 2005;Fletcher, Barsotti, & Hornak, 1993;Lambert, Lutti, Helms, Frackowiak, & Ashburner, 2013;Vannier et al, 1985). Recent studies have confirmed superiority of multispectral over conventional T1-only segmentation by addressing issues such as overestimation of dura and vessels as GM and also improve cortical GM segmentation Viviani, Pracht, et al, 2017;. With the addition of T2/T2-FLAIR weighted, VBM variants (T1 + FLAIR, T1 + T2) have also shown improved lesion detection over T1-only VBM, in both lesional and non lesional MRI focal epilepsy Lindig et al, 2018).…”

The morphology of the human cerebrovascular system

“…It is therefore possible that brain areas with inherently large vessels (or with a high density of small vessels), and hence high CBV, affect the T1-weighted intensity of nearby gray matter voxels, leading to an overestimation of thickness. In addition, other studies have investigated the potential misclassification of vessels as gray matter (Helms et al, 2006;Viviani et al, 2017). In addition, other studies have investigated the potential misclassification of vessels as gray matter (Helms et al, 2006;Viviani et al, 2017).…”

The impact of correction on MP2RAGE cortical T1 and apparent cortical thickness at 7T

“…Several software packages, for example, FreeSurfer (Dale, Fischl, & Sereno, 1999), FMRIB's software library (FSL; Smith et al, 2004), Statistical Parametric Mapping (SPM; Ashburner, 2009), and CBS High‐Res Brain Processing tools (Bazin et al, 2014), have been developed to automatically classify different tissue classes based on image‐specific criteria using varying segmentation algorithms, hereby putting a large emphasis on bias field removal. In addition, many of these packages allow incorporation of complementary MRI data, such as ${\mathrm{normalT}}_2^{(*)}$‐ or proton density (PD)‐weighted images, to improve the accuracy of the (i.e., multimodal) segmentation algorithm by identifying nonbrain tissue, for example, dura mater and blood vessels (Helms, Kallenberg, & Dechent, 2006; Lambert, Lutti, Helms, Frackowiak, & Ashburner, 2013; Viviani et al, 2017). …”