1 research outputs found
Probing Tissue Microarchitecture of the Baby Brain via Spherical Mean Spectrum Imaging
During the first years of life, the human brain undergoes dynamic
spatially-heterogeneous changes, involving differentiation of neuronal types,
dendritic arborization, axonal ingrowth, outgrowth and retraction,
synaptogenesis, and myelination. To better quantify these changes, this article
presents a method for probing tissue microarchitecture by characterizing water
diffusion in a spectrum of length scales, factoring out the effects of
intra-voxel orientation heterogeneity. Our method is based on the spherical
means of the diffusion signal, computed over gradient directions for a fixed
set of diffusion weightings (i.e., b-values). We decompose the spherical mean
series at each voxel into a spherical mean spectrum (SMS), which essentially
encodes the fractions of spin packets undergoing fine- to coarse-scale
diffusion processes, characterizing hindered and restricted diffusion stemming
respectively from extra- and intra-neurite water compartments. From the SMS,
multiple orientation distribution invariant indices can be computed, allowing
for example the quantification of neurite density, microscopic fractional
anisotropy (FA), per-axon axial/radial diffusivity, and free/restricted
isotropic diffusivity. We show maps of these indices for baby brains,
demonstrating that microscopic tissue features can be extracted from the
developing brain for greater sensitivity and specificity to development related
changes. Also, we demonstrate that our method, called spherical mean spectrum
imaging (SMSI), is fast, accurate, and can overcome the biases associated with
other state-of-the-art microstructure models