Detecting microstructural properties of white matter based on compartmentalization of magnetic susceptibility

Chen, Way Cherng; Foxley, Sean; Miller, Karla L.

doi:10.1016/j.neuroimage.2012.12.032

Cited by 76 publications

(110 citation statements)

References 47 publications

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“…[6] to model local exchangeinduced, u-independent frequency offsets in WM (15), as well as u-independent WM frequency shifts due to microstructure (19,20,23,34). Previous work by our group (19) and others (15,21,43) has suggested that there are exchange-related frequency offsets on the order of 0.01ppm in WM relative to GM, which at 7T corresponds to a frequency offset of %3Hz.…”

Section: Simulating Whole Brain Frequency Mapsmentioning

confidence: 99%

“…Recently, however, several research groups have described additional mechanisms that could give rise to phase contrast in GE images: (i) exchange processes (15); (ii) nuclear magnetic resonance (NMR)-invisible microstructure (16); (iii) anisotropic magnetic susceptibility (17,18). Despite an increasing effort to quantify the contributions of each of these contrast mechanisms to phase measurements (19)(20)(21)(22)(23), the effect that these contributions have on susceptibility maps, calculated using QSM methods that assume isotropic magnetic susceptibility is the only cause of phase variation, has not been characterized. Only by understanding the impact of the additional phase contrast mechanisms on susceptibility mapping can the true value of QSM and other related methods be fully appreciated.…”

Section: Introductionmentioning

confidence: 99%

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Effects of white matter microstructure on phase and susceptibility maps

Wharton

Bowtell

2015

Magn. Reson. Med.

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Purpose: To investigate the effects on quantitative susceptibility mapping (QSM) and susceptibility tensor imaging (STI) of the frequency variation produced by the microstructure of white matter (WM). Methods: The frequency offsets in a WM tissue sample that are not explained by the effect of bulk isotropic or anisotropic magnetic susceptibility, but rather result from the local microstructure, were characterized for the first time. QSM and STI were then applied to simulated frequency maps that were calculated using a digitized whole-brain, WM model formed from anatomical and diffusion tensor imaging data acquired from a volunteer. In this model, the magnitudes of the frequency contributions due to anisotropy and microstructure were derived from the results of the tissue experiments. Results: The simulations suggest that the frequency contribution of microstructure is much larger than that due to bulk effects of anisotropic magnetic susceptibility. In QSM, the microstructure contribution introduced artificial WM heterogeneity. For the STI processing, the microstructure contribution caused the susceptibility anisotropy to be significantly overestimated. Conclusion: Microstructure-related phase offsets in WM yield artifacts in the calculated susceptibility maps. If susceptibility mapping is to become a robust MRI technique, further research should be carried out to reduce the confounding effects of microstructure-related frequency contributions.

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Section: Simulating Whole Brain Frequency Mapsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Effects of white matter microstructure on phase and susceptibility maps

Wharton

Bowtell

2015

Magn. Reson. Med.

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“…Subsequently, phase was changed iteratively until the integral under the absorption spectrum was maximized. Because models of water spectra from white matter [22][23][24] indicate that the frequency shifts between compartments is on the order of tens of Hz, a spectral bandwidth of 111 Hz was considered sufficient to resolve the water resonance. Additionally, at 9.4 T fat is ∼1380 Hz from water (3.5 ppm).…”

Section: C Data Processingmentioning

confidence: 99%

“…As a result, conventional single echoacquisitiontechniqueshavelimitedsensitivitytothesubtle variations in magnetic susceptibility due to microstructure predictedbywhitematterMRIsignalmodels.Thesemodelspredict small (on the order of Hz) but distinct changes in resonance frequency associated with white matter anatomy. [22][23][24] In addition, sensitivity to non-Lorentzian features of the water resonance is limited because the apparent phase within a voxel detected by single echo methods is an average value over all frequency components of the measured signal. This is a necessary trade-off between spectral resolution, spatial resolution, and the volume of tissue that can be imaged with acceptable run-times.…”

Section: Introductionmentioning

confidence: 99%

3D high spectral and spatial resolution imaging of ex vivo mouse brain

et al. 2015

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Purpose: Widely used MRI methods show brain morphology both in vivo and ex vivo at very high resolution. Many of these methods (e.g., T 2 * -weighted imaging, phase-sensitive imaging, or susceptibility-weighted imaging) are sensitive to local magnetic susceptibility gradients produced by subtle variations in tissue composition. However, the spectral resolution of commonly used methods is limited to maintain reasonable run-time combined with very high spatial resolution. Here, the authors report on data acquisition at increased spectral resolution, with 3-dimensional high spectral and spatial resolution MRI, in order to analyze subtle variations in water proton resonance frequency and lineshape that reflect local anatomy. The resulting information compliments previous studies based on T 2 * and resonance frequency. Methods: The proton free induction decay was sampled at high resolution and Fourier transformed to produce a high-resolution water spectrum for each image voxel in a 3D volume. Data were acquired using a multigradient echo pulse sequence (i.e., echo-planar spectroscopic imaging) with a spatial resolution of 50 × 50 × 70 µm 3 and spectral resolution of 3.5 Hz. Data were analyzed in the spectral domain, and images were produced from the various Fourier components of the water resonance. This allowed precise measurement of local variations in water resonance frequency and lineshape, at the expense of significantly increased run time (16-24 h). Results: High contrast T 2 * -weighted images were produced from the peak of the water resonance (peak height image), revealing a high degree of anatomical detail, specifically in the hippocampus and cerebellum. In images produced from Fourier components of the water resonance at −7.0 Hz from the peak, the contrast between deep white matter tracts and the surrounding tissue is the reverse of the contrast in water peak height images. This indicates the presence of a shoulder in the water resonance that is not present at +7.0 Hz and may be specific to white matter anatomy. Moreover, a frequency shift of 6.76 ± 0.55 Hz was measured between the molecular and granular layers of the cerebellum. This shift is demonstrated in corresponding spectra; water peaks from voxels in the molecular and granular layers are consistently 2 bins apart (7.0 Hz, as dictated by the spectral resolution) from one another. Conclusions: High spectral and spatial resolution MR imaging has the potential to accurately measure the changes in the water resonance in small voxels. This information can guide optimization and interpretation of more commonly used, more rapid imaging methods that depend on image contrast produced by local susceptibility gradients. In addition, with improved sampling methods, high spectral and spatial resolution data could be acquired in reasonable run times, and used for in vivo scans to increase sensitivity to variations in local susceptibility. C 2015 American Association of Physicists in Medicine. [http://dx

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“…An increasing number of studies have aimed to spatially map, characterize and quantify susceptibility effects and represent them as unique intra-voxel signal compartments (Chen et al, 2013;Hernando et al, 2008;Wu et al, 2017;Xu et al, 2015). For example, a technique known as myelin water fraction (MWF) imaging has greatly benefitted from the use of mGRE-MRI.…”

Section: Decoding the Mgre-mri Signal Through Compartmentalizationmentioning

confidence: 99%

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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Detecting microstructural properties of white matter based on compartmentalization of magnetic susceptibility

Cited by 76 publications

References 47 publications

Effects of white matter microstructure on phase and susceptibility maps

Effects of white matter microstructure on phase and susceptibility maps

3D high spectral and spatial resolution imaging of ex vivo mouse brain

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

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