Assessment of the structural complexity of diffusion MRI voxels using 3D electron microscopy in the rat brain

• Published in: NeuroImage (Orlando, Fla.) Fla.), 2021-01, Vol.225, p.117529-117529, Article 117529
• Main Authors: Salo, Raimo A., Belevich, Ilya, Jokitalo, Eija, Gröhn, Olli, Sierra, Alejandra
• Format: Article
• Language: English
• Subjects: 3D electron microscopy; Algorithms; Animals; Brain; Brain - diagnostic imaging; Brain - ultrastructure; Brain microstructure; Cell membranes; Diffusion Magnetic Resonance Imaging; Diffusion MRI; Diffusion Tensor Imaging; Fiber orientation distribution; Gray Matter - diagnostic imaging; Gray Matter - ultrastructure; Imaging, Three-Dimensional; Magnetic resonance imaging; Microscopy; Microscopy, Electron; Neuroimaging; Rats; Scanning electron microscopy; Structure tensor; Substantia alba; Substantia grisea; White Matter - diagnostic imaging; White Matter - ultrastructure
• ISSN: 1053-8119; 1095-9572
• DOI: 10.1016/j.neuroimage.2020.117529

Validation and interpretation of diffusion magnetic resonance imaging (dMRI) requires detailed understanding of the actual microstructure restricting the diffusion of water molecules. In this study, we used serial block-face scanning electron microscopy (SBEM), a three-dimensional electron microscopy (3D-EM) technique, to image seven white and grey matter volumes in the rat brain. SBEM shows excellent contrast of cellular membranes, which are the major components restricting the diffusion of water in tissue. Additionally, we performed 3D structure tensor (3D-ST) analysis on the SBEM volumes and parameterised the resulting orientation distributions using Watson and angular central Gaussian (ACG) probability distributions as well as spherical harmonic (SH) decomposition. We analysed how these parameterisations described the underlying orientation distributions and compared their orientation and dispersion with corresponding parameters from two dMRI methods, neurite orientation dispersion and density imaging (NODDI) and constrained spherical deconvolution (CSD). Watson and ACG parameterisations and SH decomposition captured well the 3D-ST orientation distributions, but ACG and SH better represented the distributions due to its ability to model asymmetric dispersion. The dMRI parameters corresponded well with the 3D-ST parameters in the white matter volumes, but the correspondence was less evident in the more complex grey matter. SBEM imaging and 3D-ST analysis also revealed that the orientation distributions were often not axially symmetric, a property neatly captured by the ACG distribution. Overall, the ability of SBEM to image diffusion barriers in intricate detail, combined with 3D-ST analysis and parameterisation, provides a step forward toward interpreting and validating the dMRI signals in complex brain tissue microstructure. [Display omitted]