Histological validation of the brain cell body imaging with diffusion MRI at ultrahigh field

Biophysical modelling of diffusion-weighted MRI (DW-MRI) data can help to gain more insight into brain microstructure. However, models need to be validated. This work validates a recently-developed technique for non-invasive mapping of brain cell-body (soma) size/ density with DW-MRI, by using ultrahigh-field DW-MRI experiments and histology of mouse brain. Predictions from numerical simulations are experimentally confirmed and brain’s maps of MR-measured soma size/density are shown to correspond very well with histology. We provide differential contrasts between cell layers that are less expressed in tensor analyses, leading to novel complementary contrasts of the brain tissue. Limitations and future research directions are discussed

Histological validation of the brain cell body imaging with diffusion MRI at ultrahigh field

Biophysical modelling of diffusion-weighted MRI (DW-MRI) data can help to gain more insight into brain microstructure. However, models need to be validated. This work validates a recently-developed technique for non-invasive mapping of brain cell-body (soma) size/ density with DW-MRI, by using ultrahigh-field DW-MRI experiments and histology of mouse brain. Predictions from numerical simulations are experimentally confirmed and brain’s maps of MR-measured soma size/density are shown to correspond very well with histology. We provide differential contrasts between cell layers that are less expressed in tensor analyses, leading to novel complementary contrasts of the brain tissue. Limitations and future research directions are discussed

Direction-averaged diffusion-weighted MRI signal using different axisymmetric B-tensor encoding schemes

Purpose: It has been shown, theoretically and in vivo, that using the Stejskal-Tannerpulsed-gradient, or linear tensor encoding (LTE), and in exhibiting a ’stick-like’ diffusion geometry,the direction-averaged diffusion-weighted MRI signal at high b-values (7000 < b <10000 s=mm2) follows a power-law, decaying as 1=pb. It has also been shown, theoretically,that for planar tensor encoding (PTE), the direction-averaged signal decays as 1=b. We aimedto confirm this theoretical prediction in vivo. We then considered the direction-averaged signalfor arbitrary b-tensor shapes and different tissue substrates to look for other conditions underwhich a power-law exists.Methods: We considered the signal decay for high b-values for encoding geometries rangingfrom 2-dimensional PTE, through isotropic or spherical tensor encoding (STE) to LTE. Whena power-law behaviour was suggested, this was tested using in silico simulations and in vivousing ultra-strong gradients (300 mT/m).Results: Our in vivo results confirmed the predicted 1/b power law for PTE. Moreover, ouranalysis showed that using an axisymmetric b-tensor a power-law only exists under very specificconditions: (a) the tissue must have ’stick-like’ geometry; and (b) the waveform must bepurely LTE or purely PTE.Conclusion: A complete analysis of the power-law dependencies of the diffusion-weightedsignal at high b-values has been performed. Only two specific forms of encoding result in apower-law dependency, pure linear and pure planar tensor encoding and when the microstructuralgeometry is ’stick-like’. The different exponents of these encodings could be used toprovide independent validation of the presence of stick-like geometries in vivo

Diffusional exchange versus microscopic kurtosis from CTI: two conflicting interpretations of the same data

Correlation tensor imaging (CTI) is a new diffusion MRI framework that utilises double diffusion encoding (DDE) to resolve isotropic, anisotropic and microscopic kurtosis sources. Microscopic kurtosis in CTI is provided by the contrast between SDE and parallel DDE signals at the same b-value. Multi-Gaussian exchange (MGE) is a diffusion MRI framework that utilises DDE to measure exchange. The highest exchange sensitivity in MGE is obtained by contrasting SDE and DDE signals at the same b-value. CTI and MGE can thus be applied to analyse the same data but provide conflicting interpretations of that data. We perform Monte Carlo simulations in different geometries with varying levels of exchange to determine which approach is more compatible with the data. Simulations reveal that in all microstructures considered, CTI microscopic kurtosis drastically increases when exchange is introduced. Furthermore, in microstructures that are well-described by the multi-Gaussian assumption, CTI-estimated microscopic kurtosis increases with both the exchange rate and the mixing time, despite fulfilment of the long-mixing-time condition of CTI. Increasing the exchange rate by a factor of 2 positively biases CTI microscopic kurtosis by approximately the same factor. At a modest exchange rate of 10 /s, varying the mixing time from 12 to 100 ms increases CTI microscopic kurtosis by at least a factor of 3. To address this problem, we propose a heuristic approach to combine CTI and MGE to estimate intra-compartmental kurtosis unconfounded by exchange and demonstrate its feasibility using numerical simulations

Segmentation of the brain using direction-averaged signal of DWI images

Segmentation of brain tissue in diffusion MRI image space has some unique advantages. A novel segmentation method using the direction-averaged diffusion weighted imaging (DWI) signal is proposed. Two images can be obtained from the fitting of the direction-averaged DWI signal as a function of b-value: one with superior contrast between the gray matter and white matter; one with prominent CSF contrast. A pseudo T1 weighted image can be constructed and standard segmentation tools can be applied. The method was tested on the HCP dataset using SPM12, and showed good agreement with segmentation using the T1 weighted image with the same resolution. The Dice score was all greater than 0.88 for GM or WM with full DWI data and very stable against subsampling of the DWI data in number of diffusion directions, number of shells, and spatial resolution

SANDI: a compartment-based model for non-invasive apparent soma and neurite imaging by diffusion MRI

This work introduces a compartment-based model for apparent soma and neurite density imaging (SANDI) using non-invasive diffusion-weighted MRI (DW-MRI). The existing conjecture in brain microstructure imaging trough DW-MRI presents water diffusion in white (WM) and grey (GM) matter as restricted diffusion in neurites, modelled by infinite cylinders of null radius embedded in the hindered extra-neurite water. The extra-neurite pool in WM corresponds to water in the extra-axonal space, but in GM it combines water in the extra-cellular space with water in soma. While several studies showed that this microstructure model successfully describe DW-MRI data in WM and GM at b<3 ms/{\mum^2}, it has been also shown to fail in GM at high b values (b>>3 ms/{\mum^2}). Here we hypothesize that the unmodelled soma compartment may be responsible for this failure and propose SANDI as a new model of brain microstructure where soma (i.e. cell body of any brain cell type: from neuroglia to neurons) is explicitly included. We assess the effects of size and density of soma on the direction-averaged DW-MRI signal at high b values and the regime of validity of the model using numerical simulations and comparison with experimental data from mouse (bmax = 40 ms/{/mum^2}) and human (bmax = 10 ms/{\mum^2}) brain. We show that SANDI defines new contrasts representing new complementary information on the brain cyto- and myelo-architecture. Indeed, we show for the first-time maps from 25 healthy human subjects of MR soma and neurite signal fractions, that remarkably mirror contrasts of histological images of brain cyto- and myelo-architecture. Although still under validation, SANDI might provide new insight into tissue architecture by introducing a new set of biomarkers of potential great value for biomedical applications and pure neuroscience

The dot-compartment revealed? Diffusion MRI with ultra-strong gradients and spherical tensor encoding in the living human brain

The so-called “dot-compartment” is conjectured in diffusion MRI to represent small spherical spaces, such as cell bodies, in which the diffusion is restricted in all directions. Previous investigations inferred its existence from data acquired with directional diffusion encoding which does not permit a straightforward separation of signals from ‘sticks’ (axons) and signals from ‘dots’. Here we combine isotropic diffusion encoding with ultra-strong diffusion gradients (240 mT/m) to achieve high diffusion-weightings with high signal to noise ratio, while suppressing signal arising from anisotropic water compartments with significant mobility along at least one axis (e.g., axons). A dot-compartment, defined to have apparent diffusion coefficient equal to zero and no exchange, would result in a non-decaying signal at very high b-values (b 7000 s/mm2). With this unique experimental setup, a residual yet slowly decaying, signal above the noise floor for b-values as high as 15 000 s/mm2 was seen clearly in the cerebellar grey matter (GM), and in several white matter (WM) regions to some extent. Upper limits of the dot-signal-fraction were estimated to be 1.8% in cerebellar GM and 0.2% in WM. By relaxing the assumption of zero diffusivity, the signal at high b-values in cerebellar GM could be represented more accurately by an isotropic water pool with a low apparent diffusivity of 0.12 and a substantial signal fraction of 9.7%. The T2 of this component was estimated to be around 61 m s. This remaining signal at high b-values has potential to serve as a novel and simple marker for isotropically-restricted water compartments in cerebellar GM

accuracy and precision of statistical descriptors obtained from multidimensional diffusion signal inversion algorithms

n/