1,380 research outputs found
Investigating exchange, structural disorder and restriction in Gray Matter via water and metabolites diffusivity and kurtosis time-dependence
Water diffusion MRI is a very powerful tool for probing tissue
microstructure, but disentangling the contribution of compartment-specific
structural disorder from cellular restriction and inter-compartment exchange
remains an open challenge. Here, we use diffusion MR spectroscopy (dMRS) of
water and metabolites as a function of diffusion time in vivo in mouse Gray
Matter (GM) to shed light on: which of these concomitant mechanisms dominates
the MR measurements and with which specific signature. We report the diffusion
time-dependence of water with excellent SNR conditions up to 500 ms. Water
kurtosis decreases with increasing diffusion time, showing the concomitant
influence of both structural disorder and exchange. Despite the excellent SNR,
we were not able to identify clearly the nature of the structural disorder
(i.e. 1D versus 2D/3D short-range disorder). Measurements of intracellular
metabolites diffusion time-dependence (up to 500 ms) show opposite behavior to
water, with metabolites kurtosis increasing as a function of diffusion time. We
show that this is a signature of diffusion restricted in the intracellular
space from which cellular microstructural features can be estimated. Finally,
by comparing water and metabolites diffusion time-dependencies, we attempt to
disentangle the effect of intra/extracellular exchange and structural disorder
of the extracellular space (both impacting water diffusion only). Our results
suggest a relatively short intra/extracellular exchange time (1-50 ms) and
short-range disorder (still unclear if 1D or 2D/3D) most likely coming from the
extracellular compartment. This work provides novel insights to interpret water
diffusion time-dependent measurements in terms of the underlying GM
microstructure and suggests that diffusion time-dependent measurements of
intracellular metabolites may offer a new way to quantify microstructural
restrictions in GM
Lossy compression of multidimensional medical images using sinusoidal activation networks: an evaluation study
In this work, we evaluate how neural networks with periodic activation
functions can be leveraged to reliably compress large multidimensional medical
image datasets, with proof-of-concept application to 4D diffusion-weighted MRI
(dMRI). In the medical imaging landscape, multidimensional MRI is a key area of
research for developing biomarkers that are both sensitive and specific to the
underlying tissue microstructure. However, the high-dimensional nature of these
data poses a challenge in terms of both storage and sharing capabilities and
associated costs, requiring appropriate algorithms able to represent the
information in a low-dimensional space. Recent theoretical developments in deep
learning have shown how periodic activation functions are a powerful tool for
implicit neural representation of images and can be used for compression of 2D
images. Here we extend this approach to 4D images and show how any given 4D
dMRI dataset can be accurately represented through the parameters of a
sinusoidal activation network, achieving a data compression rate about 10 times
higher than the standard DEFLATE algorithm. Our results show that the proposed
approach outperforms benchmark ReLU and Tanh activation perceptron
architectures in terms of mean squared error, peak signal-to-noise ratio and
structural similarity index. Subsequent analyses using the tensor and spherical
harmonics representations demonstrate that the proposed lossy compression
reproduces accurately the characteristics of the original data, leading to
relative errors about 5 to 10 times lower than the benchmark JPEG2000 lossy
compression and similar to standard pre-processing steps such as MP-PCA
denosing, suggesting a loss of information within the currently accepted levels
for clinical application
Dysfunction of the hypothalamic-pituitary adrenal axis and its influence on aging: the role of the hypothalamus
As part of the hypothalamic-pituitary adrenal (HPA) axis, the hypothalamus exerts pivotal influence on metabolic and endocrine homeostasis. With age, these processes are subject to considerable change, resulting in increased prevalence of physical disability and cardiac disorders. Yet, research on the aging human hypothalamus is lacking. To assess detailed hypothalamic microstructure in middle adulthood, 39 healthy participants (35-65Â years) underwent comprehensive structural magnetic resonance imaging. In addition, we studied HPA axis dysfunction proxied by hair cortisol and waist circumference as potential risk factors for hypothalamic alterations. We provide first evidence of regionally different hypothalamic microstructure, with age effects in its anterior-superior subunit, a critical area for HPA axis regulation. Further, we report that waist circumference was related to increased free water and decreased iron content in this region. In age, hair cortisol was additionally associated with free water content, such that older participants with higher cortisol levels were more vulnerable to free water content increase than younger participants. Overall, our results suggest no general age-related decline in hypothalamic microstructure. Instead, older individuals could be more susceptible to risk factors of hypothalamic decline especially in the anterior-superior subregion, including HPA axis dysfunction, indicating the importance of endocrine and stress management in age
Mapping complex cell morphology in the grey matter with double diffusion encoding MR: a simulation study
This paper investigates the impact of cell body (soma) size and branching of
cellular projections on diffusion MR imaging (dMRI) and spectroscopy (dMRS)
signals for both standard single diffusion encoding (SDE) and more advanced
double diffusion encoding (DDE) measurements using numerical simulations. The
aim is to study the ability of dMRI/dMRS to characterize the complex morphology
of brain grey matter, focusing on these two distinctive features. To this end,
we employ a recently developed framework to create realistic meshes for Monte
Carlo simulations, covering a wide range of soma sizes and branching orders of
cellular projections, for diffusivities reflecting both water and metabolites.
For SDE sequences, we assess the impact of soma size and branching order on the
signal b-value dependence as well as the time dependence of the apparent
diffusion coefficient (ADC). For DDE sequences, we assess their impact on the
mixing time dependence of the signal angular modulation and of the estimated
microscopic anisotropy, a promising contrast derived from DDE measurements. The
SDE results show that soma size has a measurable impact on both the b-value and
diffusion time dependence, for both water and metabolites. On the other hand,
branching order has little impact on either, especially for water. In contrast,
the DDE results show that soma size has a measurable impact on the signal
angular modulation at short mixing times and the branching order significantly
impacts the mixing time dependence of the signal angular modulation as well as
of the derived microscopic anisotropy, for both water and metabolites. Our
results confirm that soma size can be estimated from SDE based techniques, and
most importantly, show for the first time that DDE measurements show
sensitivity to the branching of cellular projections, paving the way for
non-invasive characterization of grey matter morphology
ConFiG: Contextual Fibre Growth to generate realistic axonal packing for diffusion MRI simulation
This paper presents Contextual Fibre Growth (ConFiG), an approach to generate
white matter numerical phantoms by mimicking natural fibre genesis. ConFiG
grows fibres one-by-one, following simple rules motivated by real axonal
guidance mechanisms. These simple rules enable ConFiG to generate phantoms with
tuneable microstructural features by growing fibres while attempting to meet
morphological targets such as user-specified density and orientation
distribution. We compare ConFiG to the state-of-the-art approach based on
packing fibres together by generating phantoms in a range of fibre
configurations including crossing fibre bundles and orientation dispersion.
Results demonstrate that ConFiG produces phantoms with up to 20% higher
densities than the state-of-the-art, particularly in complex configurations
with crossing fibres. We additionally show that the microstructural morphology
of ConFiG phantoms is comparable to real tissue, producing diameter and
orientation distributions close to electron microscopy estimates from real
tissue as well as capturing complex fibre cross sections. Signals simulated
from ConFiG phantoms match real diffusion MRI data well, showing that ConFiG
phantoms can be used to generate realistic diffusion MRI data. This
demonstrates the feasibility of ConFiG to generate realistic synthetic
diffusion MRI data for developing and validating microstructure modelling
approaches
Impact of within-voxel heterogeneity in fibre geometry on spherical deconvolution
Axons in white matter have been shown to have varying geometries within a
bundle using ex vivo imaging techniques, but what does this mean for diffusion
MRI (dMRI) based spherical deconvolution (SD)? SD attempts to estimate the
fibre orientation distribution function (fODF) by assuming a single dMRI fibre
response function (FRF) for all white matter populations and deconvolving this
FRF from the dMRI signal at each voxel to estimate the fODF. Variable fibre
geometry within a bundle however suggests the FRF might not be constant even
within a single voxel. We test what impact realistic fibre geometry has on SD
by simulating the dMRI signal in a range of realistic white matter numerical
phantoms, including synthetic phantoms and real axons segmented from electron
microscopy. We demonstrate that variable fibre geometry leads to a variable FRF
across axons and that in general no single FRF is effective to recover the
underlying fibre orientation distribution function (fODF). This finding
suggests that assuming a single FRF can lead to misestimation of the fODF,
causing further downstream errors in techniques such as tractography
Mini review on anomalous diffusion by MRI: Potential advantages, pitfalls, limitations, nomenclature, and correct interpretation of literature
In this mini-review, we addressed the transient-anomalous diffusion by MRI, starting from the assumption that transient-anomalous diffusion is ubiquitously observed in biological tissues, as demonstrated by different single-particle-tracking optical experiments. The purpose of this review is to identify the main pitfalls that can be encountered when venturing into the field of anomalous diffusion quantified by diffusion-MRI methods. Therefore, the theory of anomalous diffusion deriving from its mathematical definition was reported and connected with the consolidated description and the established procedures of conventional diffusion-MRI of tissues. We highlighted the two different modalities for quantifying subdiffusion and superdiffusion parameters of anomalous diffusion. Then we showed that most of the papers concerning anomalous diffusion, actually deal with pseudo-superdiffusion due to the use of a superdiffusion signal representation. Pseudo-superdiffusion depends on water diffusion multi-compartmentalization and local magnetic in-homogeneities that mimic the superdiffusion of spins. In addition to the relatively large production of pseudosuperdiffusion images, anomalous diffusion research is still in its early stages due to the limited flexibility of conventional clinical MRI scanners that currently prevent the acquisition of diffusion-weighted images by varying the diffusion time (the necessary acquisition modality to quantify transient-subdiffusion in human tissues). Moreover, the wide diffusion gradient pulses complicates the definition of a reliable function representative of anomalous diffusion signal behavior to fit data. Nevertheless, it is important and possible to address these limitations, as one of the potentialities of anomalous diffusion imaging is to increase the resolution, sensitivity, and specificity of MRI
A Novel null homozygous mutation confirms <i>CACNA2D2</i> as a gene mutated in epileptic encephalopathy
Contribution to epileptic encephalopathy (EE) of mutations in CACNA2D2, encoding α2δ-2 subunit of Voltage Dependent Calcium Channels, is unclear. To date only one CACNA2D2 mutation altering channel functionality has been identified in a single family. In the same family, a rare CELSR3 polymorphism also segregated with disease. Involvement of CACNA2D2 in EE
is therefore not confirmed, while that of CELSR3 is questionable. In a patient with epilepsy, dyskinesia, cerebellar atrophy, psychomotor delay and dysmorphic features, offspring to consanguineous parents, we performed whole exome sequencing (WES) for homozygosity mapping and mutation detection. WES identified extended autozygosity on
chromosome 3, containing two novel homozygous candidate mutations: c.1295delA (p.Asn432fs) in CACNA2D2 and
c.G6407A (p.Gly2136Asp) in CELSR3. Gene prioritization pointed to CACNA2D2 as the most prominent candidate gene. The WES finding in CACNA2D2 resulted to be statistically significant (p = 0.032), unlike that in CELSR3. CACNA2D2 homozygous c.1295delA essentially abolished α2δ-2 expression. In summary, we identified a novel null CACNA2D2 mutation associated to a clinical phenotype strikingly similar to the Cacna2d2 null mouse model. Molecular and statistical analyses together argued
in favor of a causal contribution of CACNA2D2 mutations to EE, while suggested that finding in CELSR3, although potentially damaging, is likely incidental
Combined Diffusion-Relaxometry MRI to Identify Dysfunction in the Human Placenta
Purpose: A combined diffusion-relaxometry MR acquisition and analysis
pipeline for in-vivo human placenta, which allows for exploration of coupling
between T2* and apparent diffusion coefficient (ADC) measurements in a sub 10
minute scan time.
Methods: We present a novel acquisition combining a diffusion prepared
spin-echo with subsequent gradient echoes. The placentas of 17 pregnant women
were scanned in-vivo, including both healthy controls and participants with
various pregnancy complications. We estimate the joint T2*-ADC spectra using an
inverse Laplace transform.
Results: T2*-ADC spectra demonstrate clear quantitative separation between
normal and dysfunctional placentas.
Conclusions: Combined T2*-diffusivity MRI is promising for assessing fetal
and maternal health during pregnancy. The T2*-ADC spectrum potentially provides
additional information on tissue microstructure, compared to measuring these
two contrasts separately. The presented method is immediately applicable to the
study of other organs
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