The link between diffusion MRI and tumor heterogeneity: Mapping cell eccentricity and density by diffusional variance decomposition (DIVIDE)

Abstract

The structural heterogeneity of tumor tissue can be probed by diffusion MRI (dMRI) in terms of the variance of apparent diffusivities within a voxel. However, the link between the diffusional variance and the tissue heterogeneity is not well-established. To investigate this link we test the hypothesis that diffusional variance, caused by microscopic anisotropy and isotropic heterogeneity, is associated with variable cell eccentricity and cell density in brain tumors. We performed dMRI using a novel encoding scheme for diffusional variance decomposition (DIVIDE) in 7 meningiomas and 8 gliomas prior to surgery. The diffusional variance was quantified from dMRI in terms of the total mean kurtosis (MKT), and DIVIDE was used to decompose MKT into components caused by microscopic anisotropy (MKA) and isotropic heterogeneity (MKI). Diffusion anisotropy was evaluated in terms of the fractional anisotropy (FA) and microscopic fractional anisotropy (μFA). Quantitative microscopy was performed on the excised tumor tissue, where structural anisotropy and cell density were quantified by structure tensor analysis and cell nuclei segmentation, respectively. In order to validate the DIVIDE parameters they were correlated to the corresponding parameters derived from microscopy. We found an excellent agreement between the DIVIDE parameters and corresponding microscopy parameters; MKA correlated with cell eccentricity (r = 0.95, p < 10−7) and MKI with the cell density variance (r = 0.83, p < 10−3). The diffusion anisotropy correlated with structure tensor anisotropy on the voxel-scale (FA, r = 0.80, p < 10−3) and microscopic scale (μFA, r = 0.93, p < 10−6). A multiple regression analysis showed that the conventional MKT parameter reflects both variable cell eccentricity and cell density, and therefore lacks specificity in terms of microstructure characteristics. However, specificity was obtained by decomposing the two contributions; MKA was associated only to cell eccentricity, and MKI only to cell density variance. The variance in meningiomas was caused primarily by microscopic anisotropy (mean ± s.d.) MKA = 1.11 ± 0.33 vs MKI = 0.44 ± 0.20 (p < 10−3), whereas in the gliomas, it was mostly caused by isotropic heterogeneity MKI = 0.57 ± 0.30 vs MKA = 0.26 ± 0.11 (p < 0.05). In conclusion, DIVIDE allows noninvasive mapping of parameters that reflect variable cell eccentricity and density. These results constitute convincing evidence that a link exists between specific aspects of tissue heterogeneity and parameters from dMRI. Decomposing effects of microscopic anisotropy and isotropic heterogeneity facilitates an improved interpretation of tumor heterogeneity as well as diffusion anisotropy on both the microscopic and macroscopic scale.

Time-Dependent Diffusion MRI in Cancer: Tissue Modeling and Applications

“…Since microstructural information is lost, changing cell shapes should not affect Equation (8). This EMT establishes the well-known relationship between PGSE measurements at long times and cellularity for a simple system [3,4].…”

“…Provided the cell size is of little interest, estimating the tortuosity limit with multiple PGSE in the long time regime (Figure 1C) is indeed an alternative way of estimating the size of the ICS. A priori knowledge on D 0 is however required to quantify f using Equation (8). Additional information on D 0 can be gathered in the short-time regime using Equations (1) and (2).…”

“…Interestingly, DWI can be used to probe intra-voxel parameter variance using conventional kurtosis imaging [7] and/or the recently introduced kurtosis-based DIVIDE technique [8]. These techniques can be used as safeguards to delineate areas of strong heterogeneity in order to minimize parameter bias with TDD due to strong intravoxel variance.…”

“…The estimation of the full kurtosis tensor can help characterize tissue structure more specifically, at the cost of extended scan time. Successful examples in cancer can be found in Jensen and Helpern [7] and Szczepankiewicz et al [8] but fall outside the scope of this review.…”

Local volume fraction distributions of axons, astrocytes, and myelin in deep subcortical white matter

“…The interpretation of parameters from several existing dMRI techniques such as DTI or biophysical models has been previously validated using histological sections (cf. (Chenevert et al, 2000;Assaf et al, 2008;Jespersen et al, 2010;Xu et al, 2014;Sepehrband et al, 2015;Szczepankiewicz et al, 2016)). Additionally, combined analyses of histology and dMRI have been performed to further understand the development of certain diseases and the healthy brain (Budde and Frank, 2012;Kolasinski et al, 2012;Khan et al, 2016;Mollink et al, 2017).…”