41 research outputs found
Microstructural imaging of the human brain with a 'super-scanner': 10 key advantages of ultra-strong gradients for diffusion MRI
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'
DW-MRS with ultra-strong diffusion gradients
Diffusion-weighted magnetic resonance spectroscopy benefits from the use of ultra-strong gradients. Slow diffusing metabolites necessitate a large range of b-values to accurately model the diffusion properties. Ultra-strong gradients open the possibility of higher b-values and reduced diffusion times, alleviating some of these constraints. We present initial data acquired with DW-PRESS on a 300mT/m gradient Connectom scanner, and introduce the practical considerations associated with ultra-strong gradients
Maxwell-compensated design of asymmetric gradient waveforms for tensor-valued diffusion encoding
Purpose: Asymmetric gradient waveforms are attractive for diffusion encoding
due to their superior efficiency, however, the asymmetry may cause a residual
gradient moment at the end of the encoding. Depending on the experiment setup,
this residual moment may cause significant signal bias and image artifacts. The
purpose of this study was to develop an asymmetric gradient waveform design for
tensor-valued diffusion encoding that is not affected by concomitant gradient.
Methods: The Maxwell index was proposed as a scalar invariant that captures the
effect of concomitant gradients and was constrained in the numerical
optimization to 100 (mT/m)ms to yield Maxwell-compensated waveforms. The
efficacy of this design was tested in an oil phantom, and in a healthy human
brain. For reference, waveforms from literature were included in the analysis.
Simulations were performed to investigate if the design was valid for a wide
range of experiments and if it could predict the signal bias. Results:
Maxwell-compensated waveforms showed no signal bias in oil or in the brain. By
contrast, several waveforms from literature showed gross signal bias. In the
brain, the bias was large enough to markedly affect both signal and parameter
maps, and the bias could be accurately predicted by theory. Conclusion:
Constraining the Maxwell index in the optimization of asymmetric gradient
waveforms yields efficient tensor-valued encoding with concomitant gradients
that have a negligible effect on the signal. This waveform design is especially
relevant in combination with strong gradients, long encoding times, thick
slices, simultaneous multi-slice acquisition and large/oblique FOVs
Brain health measurement: a scoping review
Objectives Preservation of brain health is an urgent priority for the world’s ageing population. The evidence base for brain health optimisation strategies is rapidly expanding, but clear recommendations have been limited by heterogeneity in measurement of brain health outcomes. We performed a scoping review to systematically evaluate brain health measurement in the scientific literature to date, informing development of a core outcome set.
Design Scoping review.
Data sources Medline, APA PsycArticles and Embase were searched through until 25 January 2023.
Eligibility criteria for selecting studies Studies were included if they described brain health evaluation methods in sufficient detail in human adults and were in English language.
Data extraction and synthesis Two reviewers independently screened titles, abstracts and full texts for inclusion and extracted data using Covidence software.
Results From 6987 articles identified by the search, 727 studies met inclusion criteria. Study publication increased by 22 times in the last decade. Cohort study was the most common study design (n=609, 84%). 479 unique methods of measuring brain health were identified, comprising imaging, cognitive, mental health, biological and clinical categories. Seven of the top 10 most frequently used brain health measurement methods were imaging based, including structural imaging of grey matter and hippocampal volumes and white matter hyperintensities. Cognitive tests such as the trail making test accounted for 286 (59.7%) of all brain health measurement methods.
Conclusions The scientific literature surrounding brain health has increased exponentially, yet measurement methods are highly heterogeneous across studies which may explain the lack of clinical translation. Future studies should aim to develop a selected group of measures that should be included in all brain health studies to aid interstudy comparison (core outcome set), and broaden from the current focus on neuroimaging outcomes to include a range of outcomes
Impact of b-value on estimates of apparent fibre density
Recent advances in diffusion magnetic resonance imaging (dMRI) analysis techniques have improved our understanding of fibre-specific variations in white matter microstructure. Increasingly, studies are adopting multi-shell dMRI acquisitions to improve the robustness of dMRI-based inferences. However, the impact of b-value choice on the estimation of dMRI measures such as apparent fibre density (AFD) derived from spherical deconvolution is not known. Here, we investigate the impact of b-value sampling scheme on estimates of AFD. First, we performed simulations to assess the correspondence between AFD and simulated intra-axonal signal fraction across multiple b-value sampling schemes. We then studied the impact of sampling scheme on the relationship between AFD and age in a developmental population (n=78) aged 8-18 (mean=12.4, SD=2.9 years) using hierarchical clustering and whole brain fixel-based analyses. Multi-shell dMRI data were collected at 3.0T using ultra-strong gradients (300 mT/m), using 6 diffusion-weighted shells ranging from 0 – 6000 s/mm2. Simulations revealed that the correspondence between estimated AFD and simulated intra-axonal signal fraction was improved with high b-value shells due to increased suppression of the extra-axonal signal. These results were supported by in vivo data, as sensitivity to developmental age-relationships was improved with increasing b-value (b=6000 s/mm2, median R2 = .34; b=4000 s/mm2, median R2 = .29; b=2400 s/mm2, median R2 = .21; b=1200 s/mm2, median R2 = .17) in a tract-specific fashion. Overall, estimates of AFD and age-related microstructural development were better characterised at high diffusion-weightings due to improved correspondence with intra-axonal properties
Strong diffusion gradients allow the separation of intra- and extra-axonal gradient-echo signals in the human brain
The quantification of brain white matter properties is a key area of application of Magnetic Resonance Imaging (MRI), with much effort focused on using MR techniques to quantify tissue microstructure. While diffusion MRI probes white matter (WM) microstructure by characterising the sensitivity of Brownian motion of water molecules to anisotropic structures, susceptibility-based techniques probe the tissue microstructure by observing the effect of interaction between the tissue and the magnetic field. Here, we unify these two complementary approaches by combining ultra-strong () gradients with a novel Diffusion-Filtered Asymmetric Spin Echo (D-FASE) technique. Using D-FASE we can separately assess the evolution of the intra- and extra-axonal signals under the action of susceptibility effects, revealing differences in the behaviour in different fibre tracts. We observed that the effective relaxation rate of the ASE signal in the corpus callosum decreases with increasing b-value in all subjects (from at to at ), while this dependence on b in the corticospinal tract is less pronounced (from at to at ). Voxelwise analysis of the signal evolution with respect to b-factor and acquisition delay using a microscopic model demonstrated differences in gradient echo signal evolution between the intra- and extra-axonal pools
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