Biology subjects

Komlosh, M. E.

Publications and source records attributed to Komlosh, M. E..

4 recordsLinked to original sources

bioRxiv2022

Novel Pore Size-Controlled, Susceptibility Matched, 3D-Printed MRI Phantoms

Diffusion magnetic resonance imaging (dMRI) methods are commonly employed to infer changes in tissue microstructure. Quantities like the apparent diffusion coefficient (mADC), and the fractional anisotropy (FA), derived from diffusion tensor imaging (DTI) data, characterize voxel-averaged diffusion properties, whereas double pulse field gradient (dPFG) or double diffusion encoded (DDE) MR methods can be used to characterize heterogeneous diffusion processes occurring within the voxel. Owing to its unique modular design, our novel 3D-printed dMRI phantom exhibits both macroscopic and microscopic anisotropy and can serve to calibrate measures of them. Our phantom susceptibility is close to that of waters, enabling fast diffusion weighted echo-planar image (DW-EPI) acquisitions to be used to scan it. 3D printed microstructures offer a new medium with which to vet and validate theoretical models of diffusion and pipelines used to estimate it. HighlightsO_LIResearch highlight 1: We report the design concept and fabrication of dimensionally stable, uniformly oriented blocks or modules that can be assembled into large-scale MRI phantoms. Waffle-like structures containing blocks of aligned microcapillaries can be stacked into even larger arrays to construct diameter distribution phantoms, or fractured, to create a "powder-averaged" emulsion of randomly oriented blocks. C_LIO_LIResearch highlight 2: This phantom can be used to vet and calibrate various MRI methods, such as DTI, AxCaliber MRI, MAP-MRI, and various multiple pulsed field gradient (PFG) or multiple diffusion-encoded microstructure imaging methods. C_LI Graphical Abstract

biophysics↗

bioRxiv2021

Multidimensional MRI for characterization of subtle axonal injury accelerated using an adaptive nonlocal multispectral filter

Multidimensional MRI is an emerging approach that simultaneously encodes water relaxation (T1 and T2) and mobility (diffusion) and replaces voxel-averaged values with subvoxel distributions of those MR properties. While conventional (i.e., voxel-averaged) MRI methods cannot adequately quantify the microscopic heterogeneity of biological tissue, using subvoxel information allows to selectively map a specific T1-T2-diffusion spectral range that corresponds to a group of tissue elements. The major obstacle to the adoption of rich, multidimensional MRI protocols for diagnostic or monitoring purposes is the prolonged scan time. Our main goal in the present study is to evaluate the performance of a nonlocal estimation of multispectral magnitudes (NESMA) filter on reduced datasets to limit the total acquisition time required for reliable multidimensional MRI characterization of the brain. Here we focused and reprocessed results from a recent study that identified potential imaging biomarkers of axonal injury pathology from the joint analysis of multidimensional MRI, in particular voxelwise T1-T2 and diffusion-T2 spectra in human Corpus Callosum, and histopathological data. We tested the performance of NESMA and its effect on the accuracy of the injury biomarker maps, relative to the co-registered histological reference. Noise reduction improved the accuracy of the resulting injury biomarker maps, while permitting data reduction of 35.7% and 59.6% from the full dataset for T1-T2 and diffusion-T2 cases, respectively. As successful clinical proof-of-concept applications of multidimensional MRI are continuously being introduced, reliable and robust noise removal and consequent acquisition acceleration would advance the field towards clinically-feasible diagnostic multidimensional MRI protocols.

neuroscience↗

bioRxiv2020

Direct and specific assessment of axonal injury and spinal cord microenvironments using diffusion correlation imaging

We describe a practical two-dimensional (2D) diffusion MRI framework to deliver specificity and improve sensitivity to axonal injury in the spinal cord. This approach provides intravoxel distributions of correlations of water mobilities in orthogonal directions, revealing sub-voxel diffusion components. Here we use it to investigate water diffusivities along axial and radial orientations within spinal cord specimens with confirmed, tract-specific axonal injury. First, we show using transmission electron microscopy and immunohistochemistry that tract-specific axonal beading occurs following Wallerian degeneration in the cortico-spinal tract as direct sequelae to closed head injury. We demonstrate that although some voxel-averaged diffusion tensor imaging (DTI) metrics are sensitive to this axonal injury, they are non-specific, i.e., they do not reveal an underlying biophysical mechanism of injury. Then we employ 2D diffusion correlation imaging (DCI) to improve discrimination of different water microenvironments by measuring and mapping the joint water mobility distributions perpendicular and parallel to the spinal cord axis. We determine six distinct diffusion spectral components that differ according to their microscopic anisotropy and mobility. We show that at the injury site a highly anisotropic diffusion component completely disappears and instead becomes more isotropic. Based on these findings, an injury-specific MR image of the spinal cord was generated, and a radiological-pathological correlation with histological silver staining % area was performed. The resulting strong and significant correlation (r = 0.70, p < 0.0001) indicates the high specificity with which DCI detects injury-induced tissue alterations. We predict that the ability to selectively image microstructural changes following axonal injury in the spinal cord can be useful in clinical and research applications by enabling specific detection and increased sensitivity to injury-induced microstructural alterations. These results also encourage us to translate DCI to higher spatial dimensions to enable assessment of traumatic axonal injury, and possibly other diseases and disorders in the brain.

neuroscience↗

bioRxiv2020

Limits to flow detection in pulsed gradient spin echo NMR

Pulsed gradient spin echo (PGSE) complex signal behavior becomes dominated by attenuation rather than oscillation when displacements due to flow are similar or less than diffusive displacements. In this "slow-flow" regime, the optimal displacement encoding parameter q for phase contrast velocimetry depends on the diffusive length scale [Formula] rather than the velocity encoding parameter venc = {pi}/(q{Delta}). The minimum detectable mean velocity using the difference between the phase at +qslow and -qslow is [Formula]. These theories are then validated and applied to MRI by performing PGSE echo planar imaging experiments on water flowing through a column with a bulk region and a beadpack region at controlled flow rates. Velocities as slow as 6 {micro}m/s are detected with velocimetry. Theories, MRI experimental protocols, and validation on a controlled phantom help to bridge the gap between porous media NMR and pre-clinical phase contrast and diffusion MRI.

biophysics↗