Literature DB >> 32387626

MTE-NODDI: Multi-TE NODDI for disentangling non-T2-weighted signal fractions from compartment-specific T2 relaxation times.

Ting Gong1, Qiqi Tong2, Hongjian He3, Yi Sun4, Jianhui Zhong5, Hui Zhang6.   

Abstract

Neurite orientation dispersion and density imaging (NODDI) has become a popular diffusion MRI technique for investigating microstructural alternations during brain development, maturation and aging in health and disease. However, the NODDI model of diffusion does not explicitly account for compartment-specific T2 relaxation and its model parameters are usually estimated from data acquired with a single echo time (TE). Thus, the NODDI-derived measures, such as the intra-neurite signal fraction, also known as the neurite density index, could be T2-weighted and TE-dependent. This may confound the interpretation of studies as one cannot disentangle differences in diffusion from those in T2 relaxation. To address this challenge, we propose a multi-TE NODDI (MTE-NODDI) technique, inspired by recent studies exploiting the synergy between diffusion and T2 relaxation. MTE-NODDI could give robust estimates of the non-T2-weighted signal fractions and compartment-specific T2 values, as demonstrated by both simulation and in vivo data experiments. Results showed that the estimated non-T2 weighted intra-neurite fraction and compartment-specific T2 values in white matter were consistent with previous studies. The T2-weighted intra-neurite fractions from the original NODDI were found to be overestimated compared to their non-T2-weighted estimates; the overestimation increases with TE, consistent with the reported intra-neurite T2 being larger than extra-neurite T2. Finally, the inclusion of the free water compartment reduces the estimation error in intra-neurite T2 in the presence of cerebrospinal fluid contamination. With the ability to disentangle non-T2-weighted signal fractions from compartment-specific T2 relaxation, MTE-NODDI could help improve the interpretability of future neuroimaging studies, especially those in brain development, maturation and aging.
Copyright © 2020 The Author(s). Published by Elsevier Inc. All rights reserved.

Entities:  

Keywords:  Aging; Brain development; Brain maturation; Diffusion MRI; NODDI; T2 relaxation

Mesh:

Year:  2020        PMID: 32387626     DOI: 10.1016/j.neuroimage.2020.116906

Source DB:  PubMed          Journal:  Neuroimage        ISSN: 1053-8119            Impact factor:   6.556


  11 in total

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Authors:  Paddy J Slator; Marco Palombo; Karla L Miller; Carl-Fredrik Westin; Frederik Laun; Daeun Kim; Justin P Haldar; Dan Benjamini; Gregory Lemberskiy; Joao P de Almeida Martins; Jana Hutter
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Journal:  Brain Imaging Behav       Date:  2021-04-06       Impact factor: 3.224

4.  Age-related estimates of aggregate g-ratio of white matter structures assessed using quantitative magnetic resonance neuroimaging.

Authors:  Mustapha Bouhrara; Richard W Kim; Nikkita Khattar; Wenshu Qian; Christopher M Bergeron; Denise Melvin; Linda M Zukley; Luigi Ferrucci; Susan M Resnick; Richard G Spencer
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5.  Quantifying myelin in crossing fibers using diffusion-prepared phase imaging: Theory and simulations.

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Review 6.  Advanced Diffusion MR Imaging for Multiple Sclerosis in the Brain and Spinal Cord.

Authors:  Masaaki Hori; Tomoko Maekawa; Kouhei Kamiya; Akifumi Hagiwara; Masami Goto; Mariko Yoshida Takemura; Shohei Fujita; Christina Andica; Koji Kamagata; Julien Cohen-Adad; Shigeki Aoki
Journal:  Magn Reson Med Sci       Date:  2022-02-15       Impact factor: 2.760

7.  Comparison of Neurite Orientation Dispersion and Density Imaging and Two-Compartment Spherical Mean Technique Parameter Maps in Multiple Sclerosis.

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Journal:  Front Neurol       Date:  2021-06-14       Impact factor: 4.003

8.  Diffusion magnetic resonance imaging assessment of regional white matter maturation in preterm neonates.

Authors:  J A Kimpton; D Batalle; M L Barnett; E J Hughes; A T M Chew; S Falconer; J D Tournier; D Alexander; H Zhang; A D Edwards; S J Counsell
Journal:  Neuroradiology       Date:  2020-10-29       Impact factor: 2.804

9.  Towards in vivo g-ratio mapping using MRI: Unifying myelin and diffusion imaging.

Authors:  Siawoosh Mohammadi; Martina F Callaghan
Journal:  J Neurosci Methods       Date:  2020-10-28       Impact factor: 2.390

10.  Data-Driven multi-Contrast spectral microstructure imaging with InSpect: INtegrated SPECTral component estimation and mapping.

Authors:  Paddy J Slator; Jana Hutter; Razvan V Marinescu; Marco Palombo; Laurence H Jackson; Alison Ho; Lucy C Chappell; Mary Rutherford; Joseph V Hajnal; Daniel C Alexander
Journal:  Med Image Anal       Date:  2021-04-20       Impact factor: 8.545

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