The Influence of Radio-Frequency Transmit Field Inhomogeneities on the Accuracy of G-ratio Weighted Imaging

Abstract

G-ratio weighted imaging is a non-invasive, in-vivo MRI-based technique that aims at estimating an aggregated measure of relative myelination of axons across the entire brain white matter. The MR g-ratio and its constituents (axonal and myelin volume fraction) are more specific to the tissue microstructure than conventional MRI metrics targeting either the myelin or axonal compartment. To calculate the MR g-ratio, an MRI-based myelin-mapping technique is combined with an axon-sensitive MR technique (such as diffusion MRI). Correction for radio-frequency transmit (B1+) field inhomogeneities is crucial for myelin mapping techniques such as magnetization transfer saturation. Here we assessed the effect of B1+ correction on g-ratio weighted imaging. To this end, the B1+ field was measured and the B1+ corrected MR g-ratio was used as the reference in a Bland-Altman analysis. We found a substantial bias (≈-89%) and error (≈37%) relative to the dynamic range of g-ratio values in the white matter if the B1+ correction was not applied. Moreover, we tested the efficiency of a data-driven B1+ correction approach that was applied retrospectively without additional reference measurements. We found that it reduced the bias and error in the MR g-ratio by a factor of three. The data-driven correction is readily available in the open-source hMRI toolbox (www.hmri.info) which is embedded in the statistical parameter mapping (SPM) framework.

Deciphering the fibre-orientation independent component of R2* (R2,iso*) in the human brain with a single multi-echo gradient-recalled-echo measurement under varying microstructural conditions

“…By using the FVF and proton density values from the in silico data (Table A1), we found a median g-ratio of 0.79. The estimated g-ratio value is higher than typical MRI-based gratio values reported for the in vivo brain, which ranges between 0.65 and 0.70 (Berman et al, 2018;Emmenegger et al, 2021;Stikov et al, 2015) but is close to the value used in the work by (Wharton and Bowtell, 2013). The reason for the dissimilarity between predicted g-ratio and its counterpart from literature might be related to the additional assumptions that were made to estimate the g-ratio: while the estimation of MWF only requires knowledge of the compartmental R2 values, the g-ratio estimation requires additional knowledge of fibre volume fraction (FVF) and proton density values (Equation 6).…”

“…The variable parameters, or parameter space, of the in silico MR data are listed as follows: (Emmenegger et al, 2021) and (Wharton and Bowtell, 2013), respectively. The mean value of 0.73 was arbitrarily defined.…”

Hippocampal microstructure, but not macrostructure, mediates age differences in episodic memory

“…Mean diffusion metrics were then calculated separately for each hemisphere by multiplying each registered hippocampus mask by the corresponding voxel-wise NODDI image and taking the average across voxels. All diffusion metrics were limited to voxels with sufficient signal (excluding voxels with intracellular diffusion >0.99) (Emmenegger et al, 2021) and the intracellular and dispersion of diffusion metrics were further limited to voxels with sufficient cellular sources of the diffusion signal (excluding voxels with free diffusion >0.9).…”

“…Neurite Orientation Dispersion and Density Imaging (NODDI) is a multi-compartment modelling approach that yields separate estimates of diffusion within (intracellular) and between (dispersion) cells and from non-cellular sources (free)(Zhang et al, 2012); which may be more sensitive that single tensor metrics to diffusion in gray matter (Venkatesh et al, 2020). NODDI studies have shown age-related increases in all diffusion metrics in the hippocampus (Franco et al, 2021; Metzler-Baddeley et al, 2019; Nazeri et al, 2015; Radhakrishnan et al, 2020; Venkatesh et al, 2020, 2021) and that higher hippocampal diffusion relates to worse delayed free recall in younger and older adults (Radhakrishnan et al, 2020; Venkatesh et al, 2021). Thus, whereas both hippocampal macrostructure and microstructure decline with age, hippocampal microstructure may uniquely contribute to age-related declines in episodic memory performance, although this is based on very few NODDI studies that examined diffusion-memory relationships with select memory measures.…”

White matter microstructural correlates of associative learning in the oldest-old

“…Prior to extracting diffusion metrics, the region of interest masks were further limited to voxels with restricted diffusion below 0.99 to account for artifactual, mathematical errors in regions with insufficient signal (Emmenegger et al, 2021). For each participant, the resulting masks were then separately multiplied by each diffusion metric image (free, intracellular, and dispersed) and values were averaged across voxels within each mask.…”