626 research outputs found
Double oscillating diffusion encoding and sensitivity to microscopic anisotropy
PURPOSE: To introduce a novel diffusion pulse sequence, namely double oscillating diffusion encoding (DODE), and to investigate whether it adds sensitivity to microscopic diffusion anisotropy (”A) compared to the well-established double diffusion encoding (DDE) methodology. METHODS: We simulate measurements from DODE and DDE sequences for different types of microstructures exhibiting restricted diffusion. First, we compare the effect of varying pulse sequence parameters on the DODE and DDE signal. Then, we analyse the sensitivity of the two sequences to the microstructural parameters (pore diameter and length) which determine ”A. Finally, we investigate specificity of measurements to particular substrate configurations. RESULTS: Simulations show that DODE sequences exhibit similar signal dependence on the relative angle between the two gradients as DDE sequences, however, the effect of varying the mixing time is less pronounced. The sensitivity analysis shows that in substrates with elongated pores and various orientations, DODE sequences increase the sensitivity to pore diameter, while DDE sequences are more sensitive to pore length. Moreover, DDE and DODE sequence parameters can be tailored to enhance/suppress the signal from a particular range of substrates. CONCLUSIONS: A combination of DODE and DDE sequences maximize sensitivity to ”A, compared to using just the DDE method. Magn Reson Med, 2016. © 2016 The Authors Magnetic Resonance in Medicine published by Wiley Periodicals, Inc. on behalf of International Society for Magnetic Resonance in Medicine
Abundance of cell bodies can explain the stick modelâs failure in grey matter at high bvalue
This work investigates the validity of the stick model used in diffusion-weighted MRI for modelling cellular projections in brain tissue. We hypothesize that the model will fail to describe the signals from grey matter due to an abundance of cell bodies. Using high b-value (â„3 ms/”m ) data from rat and human brain, we show that the assumption fails for grey matter. Using diffusion simulation in realistic digital models of neurons/glia, we demonstrate the breakdown of the assumption can be explained by the presence of cell bodies. Our findings suggest that high b-value data may be used to probe cell bodies
A compartment based model for non-invasive cell body imaging by diffusion MRI
This study aims to open a new window onto brain tissue microstructure by proposing a new technique to estimate cell body (namely soma) size/density non-invasively. Using Monte-Carlo simulation and data from rat brain, we show that somaâs size and density have a specific signature on the direction-averaged DW-MRI signal at high b values. Simulation shows that, at reasonably short diffusion times, soma and neurites can be approximated as two non-exchanging compartments, modelled as âsphereâ and âsticksâ respectively. Fitting this simple compartment model to rat data produces maps with contrast consistent with published histological data
Validation and noise robustness assessment of microscopic anisotropy estimation with clinically feasible double diffusion encoding MRI
Purpose: Double diffusion encoding (DDE) MRI enables the estimation of microscopic diffusion anisotropy, yielding valuable information on tissue microstructure. A recent study proposed that the acquisition of rotationally invariant DDE metrics, typically obtained using a spherical â5âdesign,â could be greatly simplified by assuming Gaussian diffusion, facilitating reduced acquisition times that are more compatible with clinical settings. Here, we aim to validate the new minimal acquisition scheme against the standard DDE 5âdesign, and to quantify the proposed method's noise robustness to facilitate future clinical use. /
Theory and Methods: DDE MRI experiments were performed on both ex vivo and in vivo rat brains at 9.4 T using the 5âdesign and the proposed minimal design and taking into account the difference in the number of acquisitions. The ensuing microscopic fractional anisotropy (ÎŒFA) maps were compared over a range of bâvalues up to 5000 s/mm2. Noise robustness was studied using analytical calculations and numerical simulations. /
Results: The minimal protocol quantified ÎŒFA at an accuracy comparable to the estimates obtained by means of the more theoretically robust DDE 5âdesign. ÎŒFA's sensitivity to noise was found to strongly depend on compartment anisotropy and tensor magnitude in a nonlinear manner. When ÎŒFA < 0.75 or when mean diffusivity is particularly low, very high signalâtoânoise ratio is required for precise quantification of ”FA. /
Conclusion: Our work supports using DDE for quantifying microscopic diffusion anisotropy in clinical settings but raises hitherto overlooked precision issues when measuring ÎŒFA with DDE and typical clinical signalâtoânoise ratio
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 brainâs 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
MP-PCA denoising of fMRI time-series data can lead to artificial activation "spreading"
MP-PCA denoising has become the method of choice for denoising in MRI since
it provides an objective threshold to separate the desired signal from unwanted
thermal noise components. In rodents, thermal noise in the coils is an
important source of noise that can reduce the accuracy of activation mapping in
fMRI. Further confounding this problem, vendor data often contains zero-filling
and other effects that may violate MP-PCA assumptions. Here, we develop an
approach to denoise vendor data and assess activation "spreading" caused by
MP-PCA denoising in rodent task-based fMRI data. Data was obtained from N = 3
mice using conventional multislice and ultrafast acquisitions (1 s and 50 ms
temporal resolution, respectively), during visual stimulation. MP-PCA denoising
produced SNR gains of 64% and 39% and Fourier spectral amplitude (FSA)
increases in BOLD maps of 9% and 7% for multislice and ultrafast data,
respectively, when using a small [2 2] denoising window. Larger windows
provided higher SNR and FSA gains with increased spatial extent of activation
that may or may not represent real activation. Simulations showed that MP-PCA
denoising causes activation "spreading" with an increase in false positive rate
and smoother functional maps due to local "bleeding" of principal components,
and that the optimal denoising window for improved specificity of functional
mapping, based on Dice score calculations, depends on the data's tSNR and
functional CNR. This "spreading" effect applies also to another recently
proposed low-rank denoising method (NORDIC). Our results bode well for
dramatically enhancing spatial and/or temporal resolution in future fMRI work,
while taking into account the sensitivity/specificity trade-offs of low-rank
denoising methods
Double diffusion encoding and applications for biomedical imaging
Diffusion Magnetic Resonance Imaging (dMRI) is one of the most important
contemporary non-invasive modalities for probing tissue structure at the
microscopic scale. The majority of dMRI techniques employ standard single
diffusion encoding (SDE) measurements, covering different sequence parameter
ranges depending on the complexity of the method. Although many signal
representations and biophysical models have been proposed for SDE data, they
are intrinsically limited by a lack of specificity. Advanced dMRI methods have
been proposed to provide additional microstructural information beyond what can
be inferred from SDE. These enhanced contrasts can play important roles in
characterizing biological tissues, for instance upon diseases (e.g.
neurodegenerative, cancer, stroke), aging, learning, and development.
In this review we focus on double diffusion encoding (DDE), which stands out
among other advanced acquisitions for its versatility, ability to probe more
specific diffusion correlations, and feasibility for preclinical and clinical
applications. Various DDE methodologies have been employed to probe compartment
sizes (Section 3), decouple the effects of microscopic diffusion anisotropy
from orientation dispersion (Section 4), probe displacement correlations, study
exchange, or suppress fast diffusing compartments (Section 6). DDE measurements
can also be used to improve the robustness of biophysical models (Section 5)
and study intra-cellular diffusion via magnetic resonance spectroscopy of
metabolites (Section 7). This review discusses all these topics as well as
important practical aspects related to the implementation and contrast in
preclinical and clinical settings (Section 9) and aims to provide the readers a
guide for deciding on the right DDE acquisition for their specific application
Accurate estimation of microscopic diffusion anisotropy and its time dependence in the mouse brain
Microscopic diffusion anisotropy (ÎŒA) has been recently gaining increasing attention for its ability to decouple the average compartment anisotropy from orientation dispersion. Advanced diffusion MRI sequences, such as double diffusion encoding (DDE) and double oscillating diffusion encoding (DODE) have been used for mapping ÎŒA, usually using measurements from a single b shell. However, the accuracy of ÎŒA estimation vis-Ă -vis different b-values was not assessed. Moreover, the time-dependence of this metric, which could offer additional insights into tissue microstructure, has not been studied so far. Here, we investigate both these concepts using theory, simulation, and experiments performed at 16.4T in the mouse brain, ex-vivo. In the first part, simulations and experimental results show that the conventional estimation of microscopic anisotropy from the difference of D(O)DE sequences with parallel and orthogonal gradient directions yields values that highly depend on the choice of b-value. To mitigate this undesirable bias, we propose a multi-shell approach that harnesses a polynomial fit of the signal difference up to third order terms in b-value. In simulations, this approach yields more accurate ÎŒA metrics, which are similar to the ground-truth values. The second part of this work uses the proposed multi-shell method to estimate the time/frequency dependence of ÎŒA. The data shows either an increase or no change in ÎŒA with frequency depending on the region of interest, both in white and gray matter. When comparing the experimental results with simulations, it emerges that simple geometric models such as infinite cylinders with either negligible or finite radii cannot replicate the measured trend, and more complex models, which, for example, incorporate structure along the fibre direction are required. Thus, measuring the time dependence of microscopic anisotropy can provide valuable information for characterizing tissue microstructure
Layer-specific connectivity revealed by diffusion-weighted functional MRI in the rat thalamocortical pathway
Investigating neural activity from a global brain perspective in-vivo has been in the domain of functional Magnetic Resonance Imaging (fMRI) over the past few decades. The intricate neurovascular couplings that govern fMRI's blood-oxygenation-level-dependent (BOLD) functional contrast are invaluable in mapping active brain regions, but they also entail significant limitations, such as non-specificity of the signal to active foci. Diffusion-weighted functional MRI (dfMRI) with relatively high diffusion-weighting strives to ameliorate this shortcoming as it offers functional contrasts more intimately linked with the underlying activity. Insofar, apart from somewhat smaller activation foci, dfMRI's contrasts have not been convincingly shown to offer significant advantages over BOLD-driven fMRI, and its activation maps relied on significant modelling. Here, we study whether dfMRI could offer a better representation of neural activity in the thalamocortical pathway compared to its (spin-echo (SE)) BOLD counterpart. Using high-end forepaw stimulation experiments in the rat at 9.4âŻT, and with significant sensitivity enhancements due to the use of cryocoils, we show for the first time that dfMRI signals exhibit layer specificity, and, additionally, display signals in areas devoid of SE-BOLD responses. We find that dfMRI signals in the thalamocortical pathway cohere with each other, namely, dfMRI signals in the ventral posterolateral (VPL) thalamic nucleus cohere specifically with layers IV and V in the somatosensory cortex. These activity patterns are much better correlated (compared with SE-BOLD signals) with literature-based electrophysiological recordings in the cortex as well as thalamus. All these findings suggest that dfMRI signals better represent the underlying neural activity in the pathway. In turn, these advanatages may have significant implications towards a much more specific and accurate mapping of neural activity in the global brain in-vivo
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