We provide a quantitative comparison of characteristic diffusion metrics obtained by four diffusion models (DTI, DKI, two- and three-compartment models of diffusion) in ability to distinguish the glioma malignancy grades II, III, and IV from in vivo datasets.

A group of 15 patients with different supratentorial gliomas: 5 subjects with grade II, 5 subjects with grade III, and 5 subjects with grade IV, underwent the diffusion weighted measurements (b = 1000, 2500 s/mm², 60 diffusion directions, 3 mm³ isotropic resolution). The used workflow is represented in Fig.1. Initially, all datasets were corrected for subject motion and eddy current distortions using FSL. Appropriate b-matrix rotations were performed. The noise was corrected using a χ² distribution function¹. All raw datasets were interpolated using the cubic spline method and anisotropic diffusion filtering up to 1.5 mm³ isotropic resolution². The DTI, DKI, and two-compartment model metrics such as fractional anisotropy (FA), kurtosis anisotropy³ (KA), apparent water fraction⁴ (AWF), and tortuosity⁴ (TORT) were evaluated using ExploreDTI⁵ and the linear weighted estimator. Neurite orientation dispersion and density imaging (NODDI) metrics such as orientation dispersion index (ODI) and intra-axonal volume fraction (TD) were estimated in two stage: at the beginning the whole brain maps were evaluated⁶. Next the new masks (see Fig.1) were created in regions with the suspicious results of an optimisation (the values equal to 0.704833 in ODI maps), and a two-step optimisation was rerun for these regions. This improvement allows us to obtain corrected values of ODI/TD in the area of tumour and edema. For the statistical analysis we used only highest glioma grade regions in each subject chosen by hand by a trained radiologist.In Figure 2 we present an example of three patients with different gliomas and corresponding masks. One can see histograms of estimated metrics (normalised and averaged over the groups) FA, KA, AWF, TORT, TD, and ODI. In Figure 3 we presented the scatter plots of different diffusion metrics.Diffusion-weighted imaging provides useful information about microstructure difference between healthy and diseased brain tissue. At the moment, a clinical “gold standard” such as DTI and the recently popular, DKI metrics are used as the biomarkers in a differentiation of glioma grades⁷. However, as we have shown (see Figs. 2,3) the diffusion models based on two- and three-compartment models allow one to get better contrast and richer information about the structure changes in tumour tissue depending on the glioma grade. In particular, the NODDI metrics which originally were developed for a healthy tissue exhibit a growing contribution of extra compartment based on cancer cells to the diffusion signal. It can be treated as a very sensitive biomarker of glioma malignancy and provides a good signaling metrics in glioma grade analysis.In conclusion we demonstrated the feasibility of using diffusion models based on compartmentalization of brain tissue^7.8. We have shown that ODI, TD, and AWF maps provided unique contrasts within regions with highest glioma malignancy and allow one to make a separation within low- and high glioma grades, including splitting inside of high-grade gliomas.