G-ratio-weighted imaging is an active field of research with the goal of better characterizing white matter in both health and disease. However, clinical adoption is significantly hampered by the fact that most g-ratio protocols rely on time-intensive multi-shell diffusion data which is typically not available in clinical settings. In this study, we adopted the recently introduced NODDI-DTI in combination with magnetization transfer saturation to calculate g-ratio maps based on a single diffusion shell in healthy subjects. The so-acquired g-ratio maps greatly resembled maps from the literature and had high scan-rescan repeatability, which has great implications for clinical g-ratio-weighted imaging.
G-ratio is an intrinsic property of white matter, representing the ratio between the inner and outer diameter of the myelinated axons. In recent years, in-vivo g-ratio-weighted imaging has received great attention because of its ability to express relative myelination and distinguish demyelination from axonal loss. G-ratio can be estimated using the formula1:
g=sqrt(1/(1+MVF/AVF)) (Formula 1)
where MVF and AVF represent myelin and axonal volume fraction, respectively, which can be measured in-vivo using MRI techniques. However, in most implementations (such as NODDI), estimation of AVF relies on multi-shell diffusion data, which is rarely acquired in clinical settings and also prevents retrospective use of single-shell DTI data.
Recently, Edwards et al. derived relations connecting NODDI and DTI parameters under the assumption of no free water compartment in the diffusion model2. This NODDI-DTI model has been shown to provide reasonable estimates of intra-cellular component (νic) on the basis of a single-shell DTI data using the formula2:
νic=1-sqrt(3/2d·(MD+b/6(∑i,j(1+2δi,j)λiλj/15))-1/2) (Formula 2)
where λ and MD denote the DTI eigenvalues and mean diffusivity, respectively, while b is the b-value and d is fixed parameter (1.7·10-3 mm2/s), and δ is the Kronecker delta. Using νic as proxy for AVF in combination with MT saturation (MTsat) as proxy for MVF, the formula for g-ratio becomes1
g=sqrt(1/(1+α·MTsat/(1-α·MTsat)/νic)) (Formula 3)
where α is a scaling factor.
In this study, we calculated g-ratio maps by estimating νic using NODDI-DTI instead of NODDI. Relying on single-shell data, the feasibility of this approach would pave the way for clinical application of g-ratio-weighted imaging.
27 healthy volunteers (all males, age: 37.9±12.54 yrs) were scanned on a 3T Siemens SkyraFit system. Subjects were scanned twice with an interval of 3 months. A multi-parametric mapping protocol consisting of three 3D multi-echo gradient-echo FLASH sequences with predominant PD-, MT-, and T1-weighting was applied. Acquisition parameters were (number of echoes/TR/flip angle): PDw (8/25ms/4°), MTw (6/37ms/9°), T1w (8/25ms/23°). Other parameters: resolution=1x1x1mm3, FOV=176x224x256 mm3, TA=18:00 min. MTsat maps were generated using the VBQ3.
DTI data were acquired using a single-shot spin-echo EPI sequence with 60 diffusion-weighted (b=1200 s/mm2) and 7 T2w (b=0 s/mm2) images covering the whole brain with 56 slices of 2.5 mm. Acquisition parameters were: resolution=2.5x2.5 mm2, FOV=220x220 mm2, TE/TR=80/7600 ms, TA=08:54 min. The dataset was corrected for motion-, eddy-current-, and susceptibility-artifacts using FSL’s eddy. DTI eigenvalues were obtained using the ACID toolbox4.
G-ratio maps were computed according to Formula(3) after co-registering the DTI images to the MTw image using SPM’s coreg. The MTsat maps were normalized using DARTEL and the resulting transformation was applied on the g-ratio maps. α was determined similar to Ellerbrock et al.5, by scaling the group-average g-ratio map in the splenium to match the histologically determined value of 0.7. Note that the same α was used for both time points.
Maps of AVF, MVF, and g-ratio were created and averaged across subjects in the whole white matter and in three selected WM tracts including corticospinal tract (CST), corpus callosum (CC) and superior longitudinal fasciculus (SLF). Scan-rescan repeatability was assessed using coefficient of variation (CoV) and intra-class coefficient (ICC).
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