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Assessing tissue heterogeneity by non-Gaussian measures in a permeable environment

Brusini, Lorenza ; Menegaz, Gloria and Nilsson, Markus LU (2018) 26th European Signal Processing Conference, EUSIPCO 2018 2018-September. p.1147-1151
Abstract

In diffusion MRI, the deviation of the Ensemble Average Propagator (EAP) from Gaussianity conveys information about the microstructural heterogeneity within an imaging voxel. Different measures have been proposed for assessing this heterogeneity. This paper assesses the performance of the Diffusional Kurtosis Imaging (DKI) and Simple Harmonics Oscillator Reconstruction and Estimation (SHORE) approaches using Monte Carlo simulations of water diffusion within synthetic axons with a permeable myelin sheath. The aim was also to understand the impact of myelin features such as its number of wrappings and relaxation (T2) rate on MR-observable parameters. To this end, a substrate consisting of parallel cylinders coated by a multi-layer sheet... (More)

In diffusion MRI, the deviation of the Ensemble Average Propagator (EAP) from Gaussianity conveys information about the microstructural heterogeneity within an imaging voxel. Different measures have been proposed for assessing this heterogeneity. This paper assesses the performance of the Diffusional Kurtosis Imaging (DKI) and Simple Harmonics Oscillator Reconstruction and Estimation (SHORE) approaches using Monte Carlo simulations of water diffusion within synthetic axons with a permeable myelin sheath. The aim was also to understand the impact of myelin features such as its number of wrappings and relaxation (T2) rate on MR-observable parameters. To this end, a substrate consisting of parallel cylinders coated by a multi-layer sheet was considered, and simulations were used to generate the synthetic diffusion-weighted signal. Results show that myelin features affects the parameters quantified by both DKI and SHORE. A strong agreement was found between DKI and SHORE parameters, highlighting the consistency of the methods in characterising the diffusion-weighted signal.

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Please use this url to cite or link to this publication:
author
; and
organization
publishing date
type
Chapter in Book/Report/Conference proceeding
publication status
published
subject
keywords
DKI, Kurtosis, Non Gaussianity, SHORE, T2-weighting
host publication
2018 26th European Signal Processing Conference, EUSIPCO 2018
volume
2018-September
article number
8552929
pages
5 pages
publisher
IEEE - Institute of Electrical and Electronics Engineers Inc.
conference name
26th European Signal Processing Conference, EUSIPCO 2018
conference location
Rome, Italy
conference dates
2018-09-03 - 2018-09-07
external identifiers
  • scopus:85059803417
ISBN
9789082797015
DOI
10.23919/EUSIPCO.2018.8552929
language
English
LU publication?
yes
id
8410549f-142e-40d4-9e4a-b1daaad27e07
date added to LUP
2019-01-24 11:12:42
date last changed
2021-08-18 02:42:45
@inproceedings{8410549f-142e-40d4-9e4a-b1daaad27e07,
  abstract     = {<p>In diffusion MRI, the deviation of the Ensemble Average Propagator (EAP) from Gaussianity conveys information about the microstructural heterogeneity within an imaging voxel. Different measures have been proposed for assessing this heterogeneity. This paper assesses the performance of the Diffusional Kurtosis Imaging (DKI) and Simple Harmonics Oscillator Reconstruction and Estimation (SHORE) approaches using Monte Carlo simulations of water diffusion within synthetic axons with a permeable myelin sheath. The aim was also to understand the impact of myelin features such as its number of wrappings and relaxation (T2) rate on MR-observable parameters. To this end, a substrate consisting of parallel cylinders coated by a multi-layer sheet was considered, and simulations were used to generate the synthetic diffusion-weighted signal. Results show that myelin features affects the parameters quantified by both DKI and SHORE. A strong agreement was found between DKI and SHORE parameters, highlighting the consistency of the methods in characterising the diffusion-weighted signal.</p>},
  author       = {Brusini, Lorenza and Menegaz, Gloria and Nilsson, Markus},
  booktitle    = {2018 26th European Signal Processing Conference, EUSIPCO 2018},
  isbn         = {9789082797015},
  language     = {eng},
  month        = {11},
  pages        = {1147--1151},
  publisher    = {IEEE - Institute of Electrical and Electronics Engineers Inc.},
  title        = {Assessing tissue heterogeneity by non-Gaussian measures in a permeable environment},
  url          = {http://dx.doi.org/10.23919/EUSIPCO.2018.8552929},
  doi          = {10.23919/EUSIPCO.2018.8552929},
  volume       = {2018-September},
  year         = {2018},
}