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Antonios Papaioannou

Publications and source records attributed to Antonios Papaioannou.

5 recordsLinked to original sources

In vivo observation and biophysical interpretation of time-dependent diffusion in human cortical gray matter

The dependence of the diffusion MRI signal on the diffusion time $t$ is a hallmark of tissue microstructure at the scale of the diffusion length. Here we measure the time-dependence of the mean diffusivity $D(t)$ and mean kurtosis $K(t)$ in cortical gray matter and in 25 gray matter sub-regions, in 10 healthy subjects. Significant diffusivity and kurtosis time-dependence is observed for $t=21.2$-100 ms, and is characterized by a power-law tail $\sim t^{-\vartheta}$ with dynamical exponent $\vartheta$. To interpret our measurements, we systematize the relevant scenarios and mechanisms for diffusion time-dependence in the brain. Using effective medium theory formalisms, we derive an exact relation between the power-law tails in $D(t)$ and $K(t)$. The estimated power-law dynamical exponent $\vartheta\simeq1/2$ in both $D(t)$ and $K(t)$ is consistent with one-dimensional diffusion in the presence of randomly positioned restrictions along neurites. We analyze the short-range disordered statistics of synapses on axon collaterals in the cortex, and perform one-dimensional Monte Carlo simulations of diffusion restricted by permeable barriers with a similar randomness in their placement, to confirm the $\vartheta=1/2$ exponent. In contrast, the Kärger model of exchange is less consistent with the data since it does not capture the diffusivity time-dependence, and the estimated exchange time from $K(t)$ falls below our measured $t$-range. Although we cannot exclude exchange as a contributing factor, we argue that structural disorder along neurites is mainly responsible for the observed time-dependence of diffusivity and kurtosis. Our observation and theoretical interpretation of the $t^{-1/2}$ tail in $D(t)$ and $K(t)$ alltogether establish the sensitivity of a macroscopic MRI signal to micrometer-scale structural heterogeneities along neurites in human gray matter in vivo.

physics.med-ph↗

A time-dependent diffusion MRI signature of axon caliber variations and beading

MRI provides a unique non-invasive window into the brain, yet is limited to millimeter resolution, orders of magnitude coarser than cell dimensions. Here we show that diffusion MRI is sensitive to the micrometer-scale variations in axon caliber or pathological beading, by identifying a signature power-law diffusion time-dependence of the along-fiber diffusion coefficient. We observe this signature in human brain white matter, and uncover its origins by Monte Carlo simulations in realistic substrates from 3d electron microscopy of mouse corpus callosum. Simulations reveal that the time-dependence originates from axon caliber variation, rather than from mitochondria or axonal undulations. We report a decreased amplitude of time-dependence in multiple sclerosis lesions, illustrating the sensitivity of our method to axonal beading in a plethora of neurodegenerative disorders. This specificity to microstructure offers an exciting possibility of bridging across scales to image cellular-level pathology with a clinically feasible MRI technique.

physics.med-ph↗

Training a Neural Network for Gibbs and Noise Removal in Diffusion MRI

We develop and evaluate a neural network-based method for Gibbs artifact and noise removal. A convolutional neural network (CNN) was designed for artifact removal in diffusion-weighted imaging data. Two implementations were considered: one for magnitude images and one for complex images. Both models were based on the same encoder-decoder structure and were trained by simulating MRI acquisitions on synthetic non-MRI images. Both machine learning methods were able to mitigate artifacts in diffusion-weighted images and diffusion parameter maps. The CNN for complex images was also able to reduce artifacts in partial Fourier acquisitions. The proposed CNNs extend the ability of artifact correction in diffusion MRI. The machine learning method described here can be applied on each imaging slice independently, allowing it to be used flexibly in clinical applications.

eess.IV↗

Observation of structural universality in disordered systems using bulk diffusion measurement

We report on an experimental observation of classical diffusion distinguishing between structural universality classes of disordered systems in one dimension. Samples of hyperuniform and short-range disorder were designed, characterized by the statistics of the placement of $μ$m-thin parallel permeable barriers, and the time-dependent diffusion coefficient was measured by NMR methods over three orders of magnitude in time. The relation between the structural exponent, characterizing disorder universality class, and the dynamical exponent of the diffusion coefficient is experimentally verified. The experimentally established relation between structure and transport exemplifies the hierarchical nature of structural complexity --- dynamics are mainly determined by the universality class, whereas microscopic parameters affect the non-universal coefficients. These results open the way for non-invasive characterization of structural correlations in porous media, complex materials, and biological tissues via a bulk diffusion measurement.

cond-mat.mes-hall↗

Methane storage in nanoporous media as observed via high field NMR relaxometry

The storage properties of methane gas in Vycor porous glass (5.7 nm) are characterized in a wide pressure range from 0.7 MPa-89.7 MPa using Nuclear Magnetic Resonance (NMR). We demonstrate the capability of high field NMR relaxometry for the determination of the methane gas storage capacity and the measurement of the Hydrogen Index, to a high degree of accuracy. This helps determine the excess gas in the pore space which can be identified to exhibit Langmuir properties in the low pressure regime of 0.7 MPa to 39.6 Mpa. The Langmuir model enables us to determine the equilibrium density of the monolayer of adsorbed gas to be 8.5% lower than that of liquid methane. We also identify the signatures of multilayer adsorption at the high pressure regime from 39.6 Mpa to 89.7 Mpa and use the Brunauer-Emmet-Teller (BET) theory to determine the number of adsorbed layers of methane gas. We show how these measurements help us differentiate the gas stored in the Vycor pore space into free and adsorbed fractions for the entire pressure range paving way for similar applications such as studying natural gas storage in gas shale rock or hydrogen storage in carbon nanotubes.

physics.geo-ph↗