25,168 research outputs found
Deep Learning How to Fit an Intravoxel Incoherent Motion Model to Diffusion-Weighted MRI
Purpose: This prospective clinical study assesses the feasibility of training
a deep neural network (DNN) for intravoxel incoherent motion (IVIM) model
fitting to diffusion-weighted magnetic resonance imaging (DW-MRI) data and
evaluates its performance. Methods: In May 2011, ten male volunteers (age
range: 29 to 53 years, mean: 37 years) underwent DW-MRI of the upper abdomen on
1.5T and 3.0T magnetic resonance scanners. Regions of interest in the left and
right liver lobe, pancreas, spleen, renal cortex, and renal medulla were
delineated independently by two readers. DNNs were trained for IVIM model
fitting using these data; results were compared to least-squares and Bayesian
approaches to IVIM fitting. Intraclass Correlation Coefficients (ICC) were used
to assess consistency of measurements between readers. Intersubject variability
was evaluated using Coefficients of Variation (CV). The fitting error was
calculated based on simulated data and the average fitting time of each method
was recorded. Results: DNNs were trained successfully for IVIM parameter
estimation. This approach was associated with high consistency between the two
readers (ICCs between 50 and 97%), low intersubject variability of estimated
parameter values (CVs between 9.2 and 28.4), and the lowest error when compared
with least-squares and Bayesian approaches. Fitting by DNNs was several orders
of magnitude quicker than the other methods but the networks may need to be
re-trained for different acquisition protocols or imaged anatomical regions.
Conclusion: DNNs are recommended for accurate and robust IVIM model fitting to
DW-MRI data. Suitable software is available at (1)
Probing molecular dynamics at the nanoscale via an individual paramagnetic center
Understanding the dynamics of molecules adsorbed to surfaces or confined to
small volumes is a matter of increasing scientific and technological
importance. Here, we demonstrate a pulse protocol using individual paramagnetic
nitrogen vacancy (NV) centers in diamond to observe the time evolution of 1H
spins from organic molecules located a few nanometers from the diamond surface.
The protocol records temporal correlations among the interacting 1H spins, and
thus is sensitive to the local system dynamics via its impact on the nuclear
spin relaxation and interaction with the NV. We are able to gather information
on the nanoscale rotational and translational diffusion dynamics by carefully
analyzing the time dependence of the NMR signal. Applying this technique to
various liquid and solid samples, we find evidence that liquid samples form a
semi-solid layer of 1.5 nm thickness on the surface of diamond, where
translational diffusion is suppressed while rotational diffusion remains
present. Extensions of the present technique could be adapted to highlight the
chemical composition of molecules tethered to the diamond surface or to
investigate thermally or chemically activated dynamical processes such as
molecular folding
Towards in vivo g-ratio mapping using MRI: unifying myelin and diffusion imaging
The g-ratio, quantifying the comparative thickness of the myelin sheath
encasing an axon, is a geometrical invariant that has high functional relevance
because of its importance in determining neuronal conduction velocity. Advances
in MRI data acquisition and signal modelling have put in vivo mapping of the
g-ratio, across the entire white matter, within our reach. This capacity would
greatly increase our knowledge of the nervous system: how it functions, and how
it is impacted by disease. This is the second review on the topic of g-ratio
mapping using MRI. As such, it summarizes the most recent developments in the
field, while also providing methodological background pertinent to aggregate
g-ratio weighted mapping, and discussing pitfalls associated with these
approaches. Using simulations based on recently published data, this review
demonstrates the relevance of the calibration step for three myelin-markers
(macromolecular tissue volume, myelin water fraction, and bound pool fraction).
It highlights the need to estimate both the slope and offset of the
relationship between these MRI-based markers and the true myelin volume
fraction if we are really to achieve the goal of precise, high sensitivity
g-ratio mapping in vivo. Other challenges discussed in this review further
evidence the need for gold standard measurements of human brain tissue from ex
vivo histology. We conclude that the quest to find the most appropriate MRI
biomarkers to enable in vivo g-ratio mapping is ongoing, with the potential of
many novel techniques yet to be investigated.Comment: Will be published as a review article in Journal of Neuroscience
Methods as parf of the Special Issue with Hu Cheng and Vince Calhoun as Guest
Editor
Molecular velocity auto-correlation of simple liquids observed by NMR MGSE method
The velocity auto-correlation spectra of simple liquids obtained by the NMR
method of modulated gradient spin echo show features in the low frequency range
up to a few kHz, which can be explained reasonably well by a long
time tail decay only for non-polar liquid toluene, while the spectra of polar
liquids, such as ethanol, water and glycerol, are more congruent with the model
of diffusion of particles temporarily trapped in potential wells created by
their neighbors. As the method provides the spectrum averaged over ensemble of
particle trajectories, the initial non-exponential decay of spin echoes is
attributed to a spatial heterogeneity of molecular motion in a bulk of liquid,
reflected in distribution of the echo decays for short trajectories. While at
longer time intervals, and thus with longer trajectories, heterogeneity is
averaged out, giving rise to a spectrum which is explained as a combination of
molecular self-diffusion and eddy diffusion within the vortexes of hydrodynamic
fluctuations.Comment: 8 pages, 6 figur
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