Improved estimation of myelin water fraction using complex model fitting

Yoonho Nam, Jongho Lee, Dosik Hwang, Dong Hyun Kim

Research output: Contribution to journalArticle

35 Citations (Scopus)

Abstract

In gradient echo (GRE) imaging, three compartment water modeling (myelin water, axonal water and extracellular water) in white matter has been demonstrated to show different frequency shifts that depend on the relative orientation of fibers and the B0 field. This finding suggests that in GRE-based myelin water imaging, a signal model may need to incorporate frequency offset terms and become a complex-valued model. In the current study, three different signal models and fitting approaches (a magnitude model fitted to magnitude data, a complex model fitted to magnitude data, and a complex model fitted to complex data) were investigated to address the reliability of each model in the estimation of the myelin water signal. For the complex model fitted to complex data, a new fitting approach that does not require background phase removal was proposed. When the three models were compared, the results from the new complex model fitting showed the most stable parameter estimation.

Original languageEnglish
Pages (from-to)214-221
Number of pages8
JournalNeuroImage
Volume116
DOIs
Publication statusPublished - 2015 Aug 1

Fingerprint

Myelin Sheath
Water

All Science Journal Classification (ASJC) codes

  • Neurology
  • Cognitive Neuroscience

Cite this

@article{c5ef60e46f8d4069a32baa07e494a0aa,
title = "Improved estimation of myelin water fraction using complex model fitting",
abstract = "In gradient echo (GRE) imaging, three compartment water modeling (myelin water, axonal water and extracellular water) in white matter has been demonstrated to show different frequency shifts that depend on the relative orientation of fibers and the B0 field. This finding suggests that in GRE-based myelin water imaging, a signal model may need to incorporate frequency offset terms and become a complex-valued model. In the current study, three different signal models and fitting approaches (a magnitude model fitted to magnitude data, a complex model fitted to magnitude data, and a complex model fitted to complex data) were investigated to address the reliability of each model in the estimation of the myelin water signal. For the complex model fitted to complex data, a new fitting approach that does not require background phase removal was proposed. When the three models were compared, the results from the new complex model fitting showed the most stable parameter estimation.",
author = "Yoonho Nam and Jongho Lee and Dosik Hwang and Kim, {Dong Hyun}",
year = "2015",
month = "8",
day = "1",
doi = "10.1016/j.neuroimage.2015.03.081",
language = "English",
volume = "116",
pages = "214--221",
journal = "NeuroImage",
issn = "1053-8119",
publisher = "Academic Press Inc.",

}

Improved estimation of myelin water fraction using complex model fitting. / Nam, Yoonho; Lee, Jongho; Hwang, Dosik; Kim, Dong Hyun.

In: NeuroImage, Vol. 116, 01.08.2015, p. 214-221.

Research output: Contribution to journalArticle

TY - JOUR

T1 - Improved estimation of myelin water fraction using complex model fitting

AU - Nam, Yoonho

AU - Lee, Jongho

AU - Hwang, Dosik

AU - Kim, Dong Hyun

PY - 2015/8/1

Y1 - 2015/8/1

N2 - In gradient echo (GRE) imaging, three compartment water modeling (myelin water, axonal water and extracellular water) in white matter has been demonstrated to show different frequency shifts that depend on the relative orientation of fibers and the B0 field. This finding suggests that in GRE-based myelin water imaging, a signal model may need to incorporate frequency offset terms and become a complex-valued model. In the current study, three different signal models and fitting approaches (a magnitude model fitted to magnitude data, a complex model fitted to magnitude data, and a complex model fitted to complex data) were investigated to address the reliability of each model in the estimation of the myelin water signal. For the complex model fitted to complex data, a new fitting approach that does not require background phase removal was proposed. When the three models were compared, the results from the new complex model fitting showed the most stable parameter estimation.

AB - In gradient echo (GRE) imaging, three compartment water modeling (myelin water, axonal water and extracellular water) in white matter has been demonstrated to show different frequency shifts that depend on the relative orientation of fibers and the B0 field. This finding suggests that in GRE-based myelin water imaging, a signal model may need to incorporate frequency offset terms and become a complex-valued model. In the current study, three different signal models and fitting approaches (a magnitude model fitted to magnitude data, a complex model fitted to magnitude data, and a complex model fitted to complex data) were investigated to address the reliability of each model in the estimation of the myelin water signal. For the complex model fitted to complex data, a new fitting approach that does not require background phase removal was proposed. When the three models were compared, the results from the new complex model fitting showed the most stable parameter estimation.

UR - http://www.scopus.com/inward/record.url?scp=84938692609&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84938692609&partnerID=8YFLogxK

U2 - 10.1016/j.neuroimage.2015.03.081

DO - 10.1016/j.neuroimage.2015.03.081

M3 - Article

C2 - 25858448

AN - SCOPUS:84938692609

VL - 116

SP - 214

EP - 221

JO - NeuroImage

JF - NeuroImage

SN - 1053-8119

ER -