13,475 research outputs found
Evaluating the accuracy of diffusion MRI models in white matter
Models of diffusion MRI within a voxel are useful for making inferences about
the properties of the tissue and inferring fiber orientation distribution used
by tractography algorithms. A useful model must fit the data accurately.
However, evaluations of model-accuracy of some of the models that are commonly
used in analyzing human white matter have not been published before. Here, we
evaluate model-accuracy of the two main classes of diffusion MRI models. The
diffusion tensor model (DTM) summarizes diffusion as a 3-dimensional Gaussian
distribution. Sparse fascicle models (SFM) summarize the signal as a linear sum
of signals originating from a collection of fascicles oriented in different
directions. We use cross-validation to assess model-accuracy at different
gradient amplitudes (b-values) throughout the white matter. Specifically, we
fit each model to all the white matter voxels in one data set and then use the
model to predict a second, independent data set. This is the first evaluation
of model-accuracy of these models. In most of the white matter the DTM predicts
the data more accurately than test-retest reliability; SFM model-accuracy is
higher than test-retest reliability and also higher than the DTM, particularly
for measurements with (a) a b-value above 1000 in locations containing fiber
crossings, and (b) in the regions of the brain surrounding the optic
radiations. The SFM also has better parameter-validity: it more accurately
estimates the fiber orientation distribution function (fODF) in each voxel,
which is useful for fiber tracking
Towards in vivo g-ratio mapping using MRI: unifying myelin and diffusion imaging
The g-ratio, quantifying the comparative thickness of the myelin sheath
encasing an axon, is a geometrical invariant that has high functional relevance
because of its importance in determining neuronal conduction velocity. Advances
in MRI data acquisition and signal modelling have put in vivo mapping of the
g-ratio, across the entire white matter, within our reach. This capacity would
greatly increase our knowledge of the nervous system: how it functions, and how
it is impacted by disease. This is the second review on the topic of g-ratio
mapping using MRI. As such, it summarizes the most recent developments in the
field, while also providing methodological background pertinent to aggregate
g-ratio weighted mapping, and discussing pitfalls associated with these
approaches. Using simulations based on recently published data, this review
demonstrates the relevance of the calibration step for three myelin-markers
(macromolecular tissue volume, myelin water fraction, and bound pool fraction).
It highlights the need to estimate both the slope and offset of the
relationship between these MRI-based markers and the true myelin volume
fraction if we are really to achieve the goal of precise, high sensitivity
g-ratio mapping in vivo. Other challenges discussed in this review further
evidence the need for gold standard measurements of human brain tissue from ex
vivo histology. We conclude that the quest to find the most appropriate MRI
biomarkers to enable in vivo g-ratio mapping is ongoing, with the potential of
many novel techniques yet to be investigated.Comment: Will be published as a review article in Journal of Neuroscience
Methods as parf of the Special Issue with Hu Cheng and Vince Calhoun as Guest
Editor
Ensemble tractography
Fiber tractography uses diffusion MRI to estimate the trajectory and cortical projection zones of white matter fascicles in the living human brain. There are many different tractography algorithms and each requires the user to set several parameters, such as curvature threshold. Choosing a single algorithm with a specific parameters sets poses two challenges. First, different algorithms and parameter values produce different results. Second, the optimal choice of algorithm and parameter value may differ between different white matter regions or different fascicles, subjects, and acquisition parameters. We propose using ensemble methods to reduce algorithm and parameter dependencies. To do so we separate the processes of fascicle generation and evaluation. Specifically, we analyze the value of creating optimized connectomes by systematically combining candidate fascicles from an ensemble of algorithms (deterministic and probabilistic) and sweeping through key parameters (curvature and stopping criterion). The ensemble approach leads to optimized connectomes that provide better cross-validatedprediction error of the diffusion MRI data than optimized connectomes generated using the singlealgorithms or parameter set. Furthermore, the ensemble approach produces connectomes that contain both short- and long-range fascicles, whereas single-parameter connectomes are biased towards one or the other. In summary, a systematic ensemble tractography approach can produce connectomes that are superior to standard single parameter estimates both for predicting the diffusion measurements and estimating white matter fascicles.Fil: Takemura, Hiromasa. University of Stanford; Estados Unidos. Osaka University; JapónFil: Caiafa, César Federico. Provincia de Buenos Aires. Gobernación. Comisión de Investigaciones CientÃficas. Instituto Argentino de RadioastronomÃa. Consejo Nacional de Investigaciones CientÃficas y Técnicas. Centro CientÃfico Tecnológico Conicet - La Plata. Instituto Argentino de RadioastronomÃa; ArgentinaFil: Wandell, Brian A.. University of Stanford; Estados UnidosFil: Pestilli, Franco. Indiana University; Estados Unido
The effect of realistic geometries on the susceptibility-weighted MR signal in white matter
Purpose: To investigate the effect of realistic microstructural geometry on
the susceptibility-weighted magnetic resonance (MR) signal in white matter
(WM), with application to demyelination.
Methods: Previous work has modeled susceptibility-weighted signals under the
assumption that axons are cylindrical. In this work, we explore the
implications of this assumption by considering the effect of more realistic
geometries. A three-compartment WM model incorporating relevant properties
based on literature was used to predict the MR signal. Myelinated axons were
modeled with several cross-sectional geometries of increasing realism: nested
circles, warped/elliptical circles and measured axonal geometries from electron
micrographs. Signal simulations from the different microstructural geometries
were compared to measured signals from a Cuprizone mouse model with varying
degrees of demyelination.
Results: Results from simulation suggest that axonal geometry affects the MR
signal. Predictions with realistic models were significantly different compared
to circular models under the same microstructural tissue properties, for
simulations with and without diffusion.
Conclusion: The geometry of axons affects the MR signal significantly.
Literature estimates of myelin susceptibility, which are based on fitting
biophysical models to the MR signal, are likely to be biased by the assumed
geometry, as will any derived microstructural properties.Comment: Accepted March 4 2017, in publication at Magnetic Resonance in
Medicin
Empirical comparison of diffusion kurtosis imaging and diffusion basis spectrum imaging using the same acquisition in healthy young adults
As diffusion tensor imaging gains widespread use, many researchers have been motivated to go beyond the tensor model and fit more complex diffusion models, to gain a more complete description of white matter microstructure and associated pathology. Two such models are diffusion kurtosis imaging (DKI) and diffusion basis spectrum imaging (DBSI). It is not clear which DKI parameters are most closely related to DBSI parameters, so in the interest of enabling comparisons between DKI and DBSI studies, we conducted an empirical survey of the interrelation of these models in 12 healthy volunteers using the same diffusion acquisition. We found that mean kurtosis is positively associated with the DBSI fiber ratio and negatively associated with the hindered ratio. This was primarily driven by the radial component of kurtosis. The axial component of kurtosis was strongly and specifically correlated with the restricted ratio. The joint spatial distributions of DBSI and DKI parameters are tissue-dependent and stable across healthy individuals. Our contribution is a better understanding of the biological interpretability of the parameters generated by the two models in healthy individuals
Voxel-wise comparisons of cellular microstructure and diffusion-MRI in mouse hippocampus using 3D Bridging of Optically-clear histology with Neuroimaging Data (3D-BOND)
A key challenge in medical imaging is determining a precise correspondence between image properties and tissue microstructure. This comparison is hindered by disparate scales and resolutions between medical imaging and histology. We present a new technique, 3D Bridging of Optically-clear histology with Neuroimaging Data (3D-BOND), for registering medical images with 3D histology to overcome these limitations. Ex vivo 120 × 120 × 200 μm resolution diffusion-MRI (dMRI) data was acquired at 7 T from adult C57Bl/6 mouse hippocampus. Tissue was then optically cleared using CLARITY and stained with cellular markers and confocal microscopy used to produce high-resolution images of the 3D-tissue microstructure. For each sample, a dense array of hippocampal landmarks was used to drive registration between upsampled dMRI data and the corresponding confocal images. The cell population in each MRI voxel was determined within hippocampal subregions and compared to MRI-derived metrics. 3D-BOND provided robust voxel-wise, cellular correlates of dMRI data. CA1 pyramidal and dentate gyrus granular layers had significantly different mean diffusivity (p > 0.001), which was related to microstructural features. Overall, mean and radial diffusivity correlated with cell and axon density and fractional anisotropy with astrocyte density, while apparent fibre density correlated negatively with axon density. Astrocytes, axons and blood vessels correlated to tensor orientation
Fuzzy Fibers: Uncertainty in dMRI Tractography
Fiber tracking based on diffusion weighted Magnetic Resonance Imaging (dMRI)
allows for noninvasive reconstruction of fiber bundles in the human brain. In
this chapter, we discuss sources of error and uncertainty in this technique,
and review strategies that afford a more reliable interpretation of the
results. This includes methods for computing and rendering probabilistic
tractograms, which estimate precision in the face of measurement noise and
artifacts. However, we also address aspects that have received less attention
so far, such as model selection, partial voluming, and the impact of
parameters, both in preprocessing and in fiber tracking itself. We conclude by
giving impulses for future research
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Quasi-diffusion magnetic resonance imaging (QDI): A fast, high b-value diffusion imaging technique.
To enable application of non-Gaussian diffusion magnetic resonance imaging (dMRI) techniques in large-scale clinical trials and facilitate translation to clinical practice there is a requirement for fast, high contrast, techniques that are sensitive to changes in tissue structure which provide diagnostic signatures at the early stages of disease. Here we describe a new way to compress the acquisition of multi-shell b-value diffusion data, Quasi-Diffusion MRI (QDI), which provides a probe of subvoxel tissue complexity using short acquisition times (1-4 min). We also describe a coherent framework for multi-directional diffusion gradient acquisition and data processing that allows computation of rotationally invariant quasi-diffusion tensor imaging (QDTI) maps. QDI is a quantitative technique that is based on a special case of the Continuous Time Random Walk model of diffusion dynamics and assumes the presence of non-Gaussian diffusion properties within tissue microstructure. QDI parameterises the diffusion signal attenuation according to the rate of decay (i.e. diffusion coefficient, D in mm2 s-1) and the shape of the power law tail (i.e. the fractional exponent, α). QDI provides analogous tissue contrast to Diffusional Kurtosis Imaging (DKI) by calculation of normalised entropy of the parameterised diffusion signal decay curve, Hn, but does so without the limitations of a maximum b-value. We show that QDI generates images with superior tissue contrast to conventional diffusion imaging within clinically acceptable acquisition times of between 84 and 228 s. We show that QDI provides clinically meaningful images in cerebral small vessel disease and brain tumour case studies. Our initial findings suggest that QDI may be added to routine conventional dMRI acquisitions allowing simple application in clinical trials and translation to the clinical arena
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