Improved estimation of myelin water fraction using complex model fitting

Nam, Yoonho; Lee, Jong-Ho; Hwang, Dosik; Kim, Dong Hyun

doi:10.1016/j.neuroimage.2015.03.081

Cited by 99 publications

(225 citation statements)

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“…Removal of macroscopic susceptibility effects has been attempted through shimming , filtering (e.g. high-pass, lowpass, and Gaussian) (Chen et al, 2013;Sati et al, 2013), and by incorporating a parameter for background phase into the model for data which has not been background field filtered Nam et al, 2015;Wu et al, 2017). In addition to the aforementioned techniques, more sophisticated phase unwrapping and background field removal techniques have emerged for voxel-wise quantification of tissue susceptibility.…”

Section: The Challenges Of Tissue Phasementioning

confidence: 99%

“…For this reason, temporal unwrapping techniques can only be applied to multi-echo data. Recent attempts at phase unwrapping within signal compartmentalization works include frequency offset subtraction from a linear fitting procedure in the temporal domain Sati et al, 2013) and cost-function based integer programming approaches (Hammond et al, 2008;Nam et al, 2015). A comprehensive review about phase unwrapping, as well as other methods, underlying principles, and applications related to signal phase processing can be found in a recent review article authored by Schweser et al, (2016).…”

Section: The Challenges Of Tissue Phasementioning

confidence: 99%

“…using complex terms to define signal compartments (Hernando et al, 2008;Nam et al, 2015;Sati et al, 2013Sati et al, , 2013van Gelderen et al, 2012). When examining the T2 * signal behaviour in white matter, Van Gelderen et al (2012) report higher residual errors in areas exhibiting greater structural order (e.g.…”

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confidence: 99%

“…The authors ascribe these effects to susceptibility induced shifts in the signal phase, and improve the quality of signal fit through the addition of a complex-valued frequency offset term in two signal compartments . This model, however, proved less robust in explaining white matter signal behaviour when compared to a more advanced signal model which assigns three distinct frequency shift parameters to three different signal compartments (myelin water, axonal water, and extracellular water/cerebrospinal fluid) (Alonso-Ortiz et al, 2017;Nam et al, 2015;Sati et al, 2013;Wu et al, 2017). By defining a frequency shift parameter through the phase component of each signal compartment, the complex model fitting approach has benefited from a reduction in structured residual errors and the generation of frequency shift maps (Alonso-Ortiz et al, 2017;Nam et al, 2015).…”

mentioning

confidence: 99%

“…By defining a frequency shift parameter through the phase component of each signal compartment, the complex model fitting approach has benefited from a reduction in structured residual errors and the generation of frequency shift maps (Alonso-Ortiz et al, 2017;Nam et al, 2015). This advance has played an important role in improving stability of myelin water fraction estimation (Nam et al, 2015;Wu et al, 2017).…”

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Exploring the utility of GRE-MRI signal compartments for temporal susceptibility mapping and investigation of neural microstructure

Kadamangudi¹

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Magnetic resonance imaging (MRI) has played a key role in our understanding of the brain's anatomy and physiology. In particular, gradient recalled echo magnetic resonance imaging (GRE-MRI) at ultra-high field holds great promise for new contrast mechanisms to examine brain structure non-invasively. Multi-echo GRE-MRI is affected by signal compartments which may inform structural characterization. A number of studies have adopted the three water-pool compartment model to study white matter brain regions by associating individual compartments with myelin, axonal and extracellular water. Many key questions, however, remain unanswered in the context of GRE-MRI signal compartmentalization.First, the number and identifiability of GRE-MRI signal compartments has not been fully explored. We examined these issues in the human brain using a data driven approach, as detailed in Ch. 1. Multiple echo time GRE-MRI data were acquired in five healthy participants, each brain was segmented into anatomical regions (substantia nigra, caudate, insula, putamen, thalamus, fornix, internal capsule, corpus callosum and cerebrospinal fluid) and the temporal signal fitted with models with one to six signal compartments. With the use of information criteria and cluster analysis methods we ascertained the number of distinct signal compartments within each region and established differences in their respective frequency shifts between the brain regions studied. We identified five dominant signal compartments; these contributed to the local signal frequency of each brain region differently. Maps of compartment volume fractions, resolved by fixing the respective compartment frequency shifts in each voxel, corresponded with commonly observed tissue properties.Second, the influence of the scanner field strength on the temporal evolution of the multiecho GRE-MRI signal is not known. Subsequently, the influence of scanner field strength on GRE-MRI signal compartments also remains uncharacterized. In Ch. 3, we evaluated variations in the signal frequency shifts due to changes in field strength within the putamen, CSF, and corpus callosum (representatives of gray matter, CSF, and white mater regions respectively). Multiple echo time GRE-MRI data at 3T and 7T were acquired in six healthy participants, and temporal frequency shift profiles were generated via the quantitative susceptibility pipeline. From a qualitative lens, we observed unique echo-time dependent frequency shift profiles for each brain region to broadly correspond between 3T and 7T measurements. Furthermore, inter-participant variability in frequency shifts were higher in MPhil Thesis -Shrinath Kadamangudi 3 3T measurements than at 7T. In general, signal compartment frequency shifts correspond well between 3T and 7T data, and standard error estimates indicate an improved quality-of-fit within the putamen and CSF, when compared to the corpus callosum. Compartment frequency shifts mapped using multi-echo GRE-MRI signal compartment models may thus provide new insights into tissu...

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Section: The Challenges Of Tissue Phasementioning

confidence: 99%

Section: The Challenges Of Tissue Phasementioning

confidence: 99%

mentioning

confidence: 99%

mentioning

confidence: 99%

mentioning

confidence: 99%

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Exploring the utility of GRE-MRI signal compartments for temporal susceptibility mapping and investigation of neural microstructure

Kadamangudi¹

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Improved quantification of myelin water fraction using joint sparsity of T₂* distribution

Chen

She

2019

Magnetic Resonance Imaging

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BackgroundMyelin water fraction (MWF) can be quantified with analysis of the T2* distribution, whereas deducing the T2* spectrum from several echoes is an underdetermined and ill‐posed problem.PurposeTo improve the quantification of myelin water content by using nonnegative jointly sparse (NNJS) optimization.Study TypeProspective.SubjectsNine healthy subjects.Field Strength/Sequence3T, multiecho gradient echo.AssessmentThe results of NNJS were compared with that of the nonnegative least square (NNLS)‐based algorithms. Simulated models with varied MWF at different noise levels were used to evaluate the accuracy of estimations. In human data, the MWF values of different regions were compared with previous studies and the coefficient of variation (COV) was used to assess the spatial coherence.Statistical TestPaired t‐test.ResultsIn simulation, the relative errors of MWF obtained from synthesized data with signal‐to‐noise ratio (SNR) at 500, 200, 150, and 100 were 0.08, 0.09, 0.10, and 0.12 for NNJS, 0.29, 0.43, 0.48, and 0.53 for regularized NNLS (rNNLS), and 0.19, 0.24, 0.25, and 0.26 for spatially‐regularized NNLS (srNNLS). In human data, the mean values of MWF produced by NNJS in different regions were consistent with previous studies. Compared with the NNLS‐based algorithms, lower COVs generated by NNJS were observed in genu, forceps minor, forceps major, and internal capsule, which were 0.44 ± 0.08, 0.48 ± 0.07, 0.46 ± 0.03, and 0.48 ± 0.09 in NNJS, 0.88 ± 0.28, 0.96 ± 0.18, 0.72 ± 0.03, and 0.85 ± 0.15 in rNNLS, and 0.56 ± 0.17, 0.64 ± 0.14, 0.50 ± 0.04 and 0.58 ± 0.13 in srNNLS.Data ConclusionQuantitative results of both simulated and human data show that NNJS provides more plausible estimation than the NNLS‐based algorithms. Visual advantages of NNJS in spatial consistency can be confirmed by the comparative COV index. The proposed algorithm might improve the quantification of myelin water content.Level of Evidence: 2Technical Efficacy: Stage 1J. Magn. Reson. Imaging 2020;52:146–158.

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So You Want to Image Myelin Using MRI: An Overview and Practical Guide for Myelin Water Imaging

Lee

Hyun

Lee

et al. 2020

Magnetic Resonance Imaging

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Myelin water imaging (MWI) is an MRI imaging biomarker for myelin. This method can generate an in vivo whole‐brain myelin water fraction map in approximately 10 minutes. It has been applied in various applications including neurodegenerative disease, neurodevelopmental, and neuroplasticity studies. In this review we start with a brief introduction of myelin biology and discuss the contributions of myelin in conventional MRI contrasts. Then the MRI properties of myelin water and four different MWI methods, which are categorized as T2‐, T2*‐, T1‐, and steady‐state‐based MWI, are summarized. After that, we cover more practical issues such as availability, interpretation, and validation of these methods. To illustrate the utility of MWI as a clinical research tool, MWI studies for two diseases, multiple sclerosis and neuromyelitis optica, are introduced. Additional topics about imaging myelin in gray matter and non‐MWI methods for myelin imaging are also included. Although technical and physiological limitations exist, MWI is a potent surrogate biomarker of myelin that carries valuable and useful information of myelin.Evidence Level: 5Technical Efficacy: 1J. MAGN. RESON. IMAGING 2021;53:360‐373.

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Improved estimation of myelin water fraction using complex model fitting

Cited by 99 publications

References 42 publications

Exploring the utility of GRE-MRI signal compartments for temporal susceptibility mapping and investigation of neural microstructure

Exploring the utility of GRE-MRI signal compartments for temporal susceptibility mapping and investigation of neural microstructure

Improved quantification of myelin water fraction using joint sparsity of T₂* distribution

So You Want to Image Myelin Using MRI: An Overview and Practical Guide for Myelin Water Imaging

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Improved estimation of myelin water fraction using complex model fitting

Cited by 99 publications

References 42 publications

Exploring the utility of GRE-MRI signal compartments for temporal susceptibility mapping and investigation of neural microstructure

Exploring the utility of GRE-MRI signal compartments for temporal susceptibility mapping and investigation of neural microstructure

Improved quantification of myelin water fraction using joint sparsity of T2* distribution

So You Want to Image Myelin Using MRI: An Overview and Practical Guide for Myelin Water Imaging

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Improved quantification of myelin water fraction using joint sparsity of T₂* distribution