Investigating the stability of mcDESPOT myelin water fraction values derived using a stochastic region contraction approach

Deoni, Sean; Kolind, Shannon

doi:10.1002/mrm.25108

Cited by 58 publications

(59 citation statements)

References 26 publications

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“…8). Details of the SRC-NLLS algorithm can be found in (25-26). Briefly, for each iteration, 20,000 random samples were generated within the specified parameter bounds.…”

Section: Methodsmentioning

confidence: 99%

“…To correspond to actual practice (24, 26, 46), the boundary conditions for the SRC-NLLS algorithm were set to 0 ≤ f s ≤ 0.6, 2 ms ≤ T 2,s ≤ 45 ms, 75 ms ≤ T 2,l ≤ 200 ms, 100 ms ≤ T 1,s ≤ 700 ms and 700 ms ≤ T 1,l ≤ 3000 ms. The same boundary conditions were used as sampling ranges for BMC.…”

Section: Methodsmentioning

confidence: 99%

“…Δω = 0 Hz), while in the experimental analyses, B 1 and Δω were calculated using the saturated double angle method (S-DAM) (35) and the DESPOT 2 -FM method (36), respectively, and used as known parameters for the calculation of the f s maps, reflecting MWF. Further, in all cases, two different bSSFP datasets were obtained with phase increments ϑ equal to 0 or π (bSSFP 0 and bSSFP π ) respectively, corresponding to the usual implementation of mcDESPOT (18-24, 26, 32). …”

Section: Theorymentioning

confidence: 99%

“…Further, we can avoid marginalization over

M_{S P G R}^{0}, M_{b S S F P_{0}}^{0} and M_{b S S F P_{π}}^{0}

by normalizing both the experimental and theoretical signals by their respective mean values calculated over FAs (18, 24, 26, 32):

{\overset{S}{true}}_{S P G R}^{k} = K \frac{S_{S P G R}^{k}}{\sum_{k = 1}^{K} S_{S P G R}^{k}}, {\overset{S}{true}}_{b S S F P_{0}}^{n} = N \frac{S_{b S S F P_{0}}^{n}}{\sum_{k = 1}^{N} S_{b S S F P_{0}}^{n}} and {\overset{S}{true}}_{b S S F P_{π}}^{n} = N \frac{S_{b S S F P_{π}}^{n}}{\sum_{k = 1}^{N} S_{b S S F P_{π}}^{n}}, and {\overset{M}{true}}_{S P G R}^{k} = K \frac{M_{S P G R}^{k}}{\sum_{k = 1}^{K} M_{S P G R}^{k}}, {\overset{M}{true}}_{b S S F P_{0}}^{n} = N \frac{M_{b S S F P_{0}}^{n}}{\sum_{k = 1}^{N} M_{b S S F P_{0}}^{n}} and {\overset{M}{true}}_{b S S F P_{π}}^{n} = N \frac{M_{b S S F P_{π}}^{n}}{\sum...}

…”

Section: Theorymentioning

confidence: 99%

“…(24) showed that, even for a simple two-pool model, quantitative parameter estimation from mcDESPOT is problematic, especially at low-to-moderate SNR. This parameter instability was found to persist in spite of the use of stochastic region contraction (SRC) in combination with nonlinear least squares (SRC-NLLS) algorithm, which has been proposed as an effective procedure for extracting parameter values from mcDESPOT data (25-26). It has been found (24) that given the flatness of NLLS parameter fit energy surfaces, SRC-NLLS remains highly sensitive to local minima and requires, therefore, high SNR data for reliable estimation of MWF.…”

Section: Introductionmentioning

confidence: 99%

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Improved determination of the myelin water fraction in human brain using magnetic resonance imaging through Bayesian analysis of mcDESPOT

Bouhrara

Spencer

2016

NeuroImage

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Myelin water fraction (MWF) mapping with magnetic resonance imaging has led to the ability to directly observe myelination and demyelination in both the developing brain and in disease. Multicomponent driven equilibrium single pulse observation of T1 and T2 (mcDESPOT) has been proposed as a rapid approach for multicomponent relaxometry and has been applied to map MWF in human brain. However, even for the simplest two-pool signal model consisting of MWF and non-myelin-associated water, the dimensionality of the parameter space for obtaining MWF estimates remains high. This renders parameter estimation difficult, especially at low-to-moderate signal-to-noise ratios (SNR), due to the presence of local minima and the flatness of the fit residual energy surface used for parameter determination using conventional nonlinear least squares (NLLS)-based algorithms. In this study, we introduce three Bayesian approaches for analysis of the mcDESPOT signal model to determine MWF. Given the high dimensional nature of mcDESPOT signal model, and, thereby, the high dimensional marginalizations over nuisance parameters needed to derive the posterior probability distribution of MWF parameter, the introduced Bayesian analyses use different approaches to reduce the dimensionality of the parameter space. The first approach uses normalization by average signal amplitude, and assumes that noise can be accurately estimated from signal-free regions of the image. The second approach likewise uses average amplitude normalization, but incorporates a full treatment of noise as an unknown variable through marginalization. The third approach does not use amplitude normalization and incorporates marginalization over both noise and signal amplitude. Through extensive Monte Carlo numerical simulations and analysis of in-vivo human brain datasets exhibiting a range of SNR and spatial resolution, we demonstrated the markedly improved accuracy and precision in the estimation of MWF using these Bayesian methods as compared to the stochastic region contraction (SRC) implementation of NLLS.

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“…8). Details of the SRC-NLLS algorithm can be found in (25-26). Briefly, for each iteration, 20,000 random samples were generated within the specified parameter bounds.…”

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Theorymentioning

confidence: 99%

“…Further, we can avoid marginalization over

M_{S P G R}^{0}, M_{b S S F P_{0}}^{0} and M_{b S S F P_{π}}^{0}

by normalizing both the experimental and theoretical signals by their respective mean values calculated over FAs (18, 24, 26, 32):

{\overset{S}{true}}_{S P G R}^{k} = K \frac{S_{S P G R}^{k}}{\sum_{k = 1}^{K} S_{S P G R}^{k}}, {\overset{S}{true}}_{b S S F P_{0}}^{n} = N \frac{S_{b S S F P_{0}}^{n}}{\sum_{k = 1}^{N} S_{b S S F P_{0}}^{n}} and {\overset{S}{true}}_{b S S F P_{π}}^{n} = N \frac{S_{b S S F P_{π}}^{n}}{\sum_{k = 1}^{N} S_{b S S F P_{π}}^{n}}, and {\overset{M}{true}}_{S P G R}^{k} = K \frac{M_{S P G R}^{k}}{\sum_{k = 1}^{K} M_{S P G R}^{k}}, {\overset{M}{true}}_{b S S F P_{0}}^{n} = N \frac{M_{b S S F P_{0}}^{n}}{\sum_{k = 1}^{N} M_{b S S F P_{0}}^{n}} and {\overset{M}{true}}_{b S S F P_{π}}^{n} = N \frac{M_{b S S F P_{π}}^{n}}{\sum...}

…”

Section: Theorymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Improved determination of the myelin water fraction in human brain using magnetic resonance imaging through Bayesian analysis of mcDESPOT

Bouhrara

Spencer

2016

NeuroImage

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Advances in noninvasive myelin imaging

Heath

Hurley

Johansen‐Berg

et al. 2017

Developmental Neurobiology

120

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Myelin is important for the normal development and healthy function of the nervous system. Recent developments in MRI acquisition and tissue modeling aim to provide a better characterization and more specific markers for myelin. This allows for specific monitoring of myelination longitudinally and noninvasively in the healthy brain as well as assessment of treatment and intervention efficacy. Here, we offer a nontechnical review of MRI techniques developed to specifically monitor myelin such as magnetization transfer (MT) and myelin water imaging (MWI). We further summarize recent studies that employ these methods to measure myelin in relation to development and aging, learning and experience, and neuropathology and psychiatric disorders. © 2017 The Authors. Developmental Neurobiology Published by Wiley Periodicals, Inc. Develop Neurobiol 78: 136–151, 2018

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Estimating the age of healthy infants from quantitative myelin water fraction maps

Dean

O’Muircheartaigh

Dirks

et al. 2015

Human Brain Mapping

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The trajectory of the developing brain is characterized by a sequence of complex, nonlinear patterns that occur at systematic stages of maturation. Although significant prior neuroimaging research has shed light on these patterns, the challenge of accurately characterizing brain maturation, and identifying areas of accelerated or delayed development, remains. Altered brain development, particularly during the earliest stages of life, is believed to be associated with many neurological and neuropsychiatric disorders. In this work, we develop a framework to construct voxel-wise estimates of brain age based on magnetic resonance imaging measures sensitive to myelin content. 198 myelin water fraction (VFM) maps were acquired from healthy male and female infants and toddlers, 3 to 48 months of age, and used to train a sigmoidal-based maturational model. The validity of the approach was then established by testing the model on 129 different VFM datasets. Results revealed the approach to have high accuracy, with a mean absolute percent error of 13% in males and 14% in females, and high predictive ability, with correlation coefficients between estimated and true ages of 0.945 in males and 0.94 in females. This work represents a new approach toward mapping brain maturity, and may provide a more faithful staging of brain maturation in infants beyond chronological or gestation-corrected age, allowing earlier identification of atypical regional brain development. Hum Brain Mapp 36:1233–1244, 2015. © 2015 The Authors Human Brain Mapping Published by Wiley Periodicals, Inc.

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Investigating the stability of mcDESPOT myelin water fraction values derived using a stochastic region contraction approach

Abstract: Results suggest that under the range of conditions examined, mcDESPOT can provide stabile and precise values.

Cited by 58 publications

References 26 publications

Improved determination of the myelin water fraction in human brain using magnetic resonance imaging through Bayesian analysis of mcDESPOT

Improved determination of the myelin water fraction in human brain using magnetic resonance imaging through Bayesian analysis of mcDESPOT

Advances in noninvasive myelin imaging

Estimating the age of healthy infants from quantitative myelin water fraction maps

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