Abstract
Quantitative assessment of tissue microstructure is important in studying human brain diseases and disorders. Ultra-high field magnetic resonance imaging (MRI) data obtained using a multi-echo gradient echo sequence have been shown to contain information on myelin, axonal, and extracellular compartments in tissue. Quantitative assessment of water fraction, relaxation time (T2*), and frequency shift using multi-compartment models has been shown to be useful in studying white matter properties via specific tissue parameters. It remains unclear how tissue parameters vary with model selection based on 7T multiple echo time gradient-recalled echo (GRE) MRI data. We applied existing signal compartment models to the corpus callosum and investigated whether a three-compartment model can be reduced to two compartments and still resolve white matter parameters [i.e., myelin water fraction (MWF) and g-ratio]. We show that MWF should be computed using a three-compartment model in the corpus callosum, and the g-ratios obtained using three compartment models are consistent with previous reports. We provide results for other parameters, such as signal compartment frequency shifts.
Highlights
Myelin is one of the main components of the white matter tissue in the brain that acts as an axonal insulator to help conduct neuronal signals (Caminiti et al, 2009)
We extended the model to 11 parameters in prior work, where the additional parameter was used to account for the noise floor in the gradient-recalled echo (GRE)-magnetic resonance imaging (MRI) data and applied it to regions of the corpus callosum (Thapaliya et al, 2018)
The 2COMP model produced the largest variation in the mean myelin water fraction (MWF) value across the corpus callosum regions of interest (ROIs), and it has the largest inter-participant variation
Summary
Myelin is one of the main components of the white matter tissue in the brain that acts as an axonal insulator to help conduct neuronal signals (Caminiti et al, 2009). The demyelination has been associated with different white matter diseases like multiple sclerosis, schizophrenia, brain stroke, and even Alzheimer’s disease (Moore et al, 2000; Laule et al, 2004; MacKay et al, 2006; Bejanin et al, 2016; Lehto et al, 2016). Different methods based on magnetic resonance (MR) such as T1 and T2 relaxation (McDESPOT) (Deoni et al, 2011), diffusion tensor imaging (Billiet et al, 2015; Davies-Thompson et al, 2016), magnetization transfer ratio (Grossman et al, 1994; Schmierer et al, 2004), ultra-short echo time (UTE) (Horch et al, 2011; Wilhelm et al, 2012), and T1-weighted/T2-weighted image ratio mapping methods have been used as a sensitive biomarker for myelin in multiple sclerosis and schizophrenia (Beer et al, 2016; Granberg et al, 2017) and to visualize that myelin contrast in the brain (Ganzetti et al, 2014)
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