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 assess 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.

Assessing Tissue Heterogeneity by non-Gaussian Measures in a Permeable Environment

Lorenza Brusini
;
Gloria Menegaz
;
2018-01-01

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 assess 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.
2018
978-90-827970-1-5
T2-weighting, SHORE, DKI, Non Gaussianity, Kurtosis
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11562/981435
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