This work introduces a compartment-based model for apparent cell body (namely 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/m 2 , it has been also shown to fail in GM at high b values (b>>3 ms/m 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 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/m 2 ) and human (bmax = 10 ms/m 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 key component of a microstructural diffusion MRI 'super-scanner' is a dedicated high-strength gradient system that enables stronger diffusion weightings per unit time compared to conventional gradient designs. This can, in turn, drastically shorten the time needed for diffusion encoding, increase the signal-to-noise ratio, and facilitate measurements at shorter diffusion times. This review, written from the perspective of the UK National Facility for In Vivo MR Imaging of Human Tissue Microstructure, an initiative to establish a shared 300 mT/m-gradient facility amongst the microstructural imaging community, describes ten advantages of ultra-strong gradients for microstructural imaging. Specifically, we will discuss how the increase of the accessible measurement space compared to a lower-gradient systems (in terms of Δ, b-value, and TE) can accelerate developments in the areas of 1) axon diameter distribution mapping; 2) microstructural parameter estimation; 3) mapping micro-vs macroscopic anisotropy features with gradient waveforms beyond a single pair of pulsed-gradients; 4) multi-contrast experiments, e.g. diffusion-relaxometry; 5) tractography and high-resolution imaging in vivo and 6) post mortem; 7) diffusion-weighted spectroscopy of metabolites other than water; 8) tumour characterisation; 9) functional diffusion MRI; and 10) quality enhancement of images acquired on lower-gradient systems. We finally discuss practical barriers in the use of ultra-strong gradients, and provide an outlook on the next generation of 'super-scanners'.

The brain is one of the most complex organs, and tools are lacking to assess its cellular morphology in vivo. Here we combine original diffusion-weighted magnetic resonance (MR) spectroscopy acquisition and novel modeling strategies to explore the possibility of quantifying brain cell morphology noninvasively. First, the diffusion of cellspecific metabolites is measured at ultra-long diffusion times in the rodent and primate brain in vivo to observe how cell long-range morphology constrains metabolite diffusion. Massive simulations of particles diffusing in synthetic cells parameterized by morphometric statistics are then iterated to fit experimental data. This method yields synthetic cells (tentatively neurons and astrocytes) that exhibit striking qualitative and quantitative similarities with histology (e.g., using Sholl analysis). With our approach, we measure major interspecies difference regarding astrocytes, whereas dendritic organization appears better conserved throughout species. This work suggests that the time dependence of metabolite diffusion coefficient allows distinguishing and quantitatively characterizing brain cell morphologies noninvasively.cell morphology | noninvasive histology | diffusion-weighted NMR spectroscopy | numerical simulations | metabolites T he brain is one of the most complex organs, and it has defined an inexhaustible field of research over the last centuries. Unfortunately, brain's complexity is paralleled by the difficulty in examining it noninvasively. Some fundamental questions regarding morphological modifications of neurons and astrocytes along brain development, aging, or disease, as well as interspecies differences, can only be investigated postmortem using histology, the current gold standard to study cellular morphology. The development of a noninvasive neuroimaging tool to evaluate and monitor brain cell morphology under normal and pathological conditions in vivo would thus represent a major breakthrough.MRI and magnetic resonance spectroscopy (MRS) techniques have opened new doors for examining brain tissues in vivo at both meso-and macroscales. Diffusion-weighted (DW)-MRI and -MRS, which allow the investigation of the diffusion process of endogenous molecules in biological tissues at these scales (1), have made it clear that cell architecture has a critical influence on molecular displacement (2-5). To quantitatively evaluate the impact of cell structure on measured molecular diffusion, mainly two modeling strategies have been developed. The first approach consists in performing numerical simulations of many particles diffusing in arbitrary geometries (e.g., defined by 3D meshes) mimicking "realistic" cell architectures (6-9). Because these realistic geometries are generally built directly from microscopy data rather than being described and generated by a (small) set of parameters, and because simulations are extremely computationally demanding, this approach does not seem adapted to fit experimental data. The second approach consists in simplifying cell architec...