Estimation of fiber Orientation Probability Density Functions in High Angular Resolution Diffusion Imaging

Tristán‐Vega, Antonio; Westin, Carl‐Fredrik; Aja‐Fernández, Santiago

doi:10.1016/j.neuroimage.2009.04.049

Cited by 98 publications

(119 citation statements)

References 22 publications

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“…These methods do not require any prior assumptions on the number of underlying fiber bundles. They include Q-Ball Imaging (Tuch, 2004, Barnett, 2009, Canales-Rodríguez et al, 2009, Tristán-Vega et al, 2009, Aganj et al, 2010 approximating the diffusion orientation density function (dODF) and Spherical Deconvolution (SD) (Tournier et al, 2004, Dell'Acqua et al, 2007, Kaden et al, 2007, Tournier et al, 2007 modeling the fiber orientation density function (fODF). The fODF represents the direction dependent density of fibers in every voxel and therefore is an angular spatial fiber density.…”

Section: Local Modelsmentioning

confidence: 99%

Beyond fractional anisotropy: Extraction of bundle-specific structural metrics from crossing fiber models

et al. 2014

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Diffusion MRI (dMRI) measurements are used for inferring the microstructural properties of white matter and to reconstruct fiber pathways. Very often voxels contain complex fiber configurations comprising multiple bundles, rendering the simple diffusion tensor model unsuitable. Multi-compartment models deliver a convenient parameterization of the underlying complex fiber architecture, but pose challenges for fitting and model selection. Spherical deconvolution, in contrast, very economically produces a fiber orientation density function (fODF) without any explicit model assumptions. Since, however, the fODF is represented by spherical harmonics, a direct interpretation of the model parameters is impossible. Based on the fact that the fODF can often be interpreted as superposition of multiple peaks, each associated to one relatively coherent fiber population (bundle), we offer a solution that seeks to combine the advantages of both approaches: first the fiber configuration is modeled as fODF represented by spherical harmonics and then each of the peaks is parameterized separately in order to characterize the underlying bundle. In this work, the fODF peaks are approximated by Bingham distributions, capturing first and second order statistics of the fiber orientations, from which we derive metrics for the parametric quantification of fiber bundles. We propose meaningful relationships between these measures and the underlying microstructural properties. We focus on metrics derived directly from properties of the Bingham distribution, such as peak length, peak direction, peak spread, integral over the peak, as well as a metric derived from the comparison of the largest peaks, which probes the complexity of the underlying microstructure. We compare these metrics to the conventionally used fractional anisotropy (FA) and show how they may help to increase the specificity of the characterization of microstructural properties. While metric relying on the first moments of the Bingham distributions provide relatively robust results, second-order metrics representing the peak spread are only meaningful, if the SNR is very high and no fiber crossings are present in the voxel.

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Section: Local Modelsmentioning

confidence: 99%

Beyond fractional anisotropy: Extraction of bundle-specific structural metrics from crossing fiber models

et al. 2014

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“…This results in a spherical feature of the diffusion, however it is important to note that the isoradius is different from the ODF feature proposed by the QBI method (detailed in the previous section). Nonetheless, several groups recently proposed to estimate an approximation of the ODF from the DOT modeling (Aganj et al, 2009a;CanalesRodríguez et al, 2010;Tristan-Vega et al, 2009). We shall explain these features in greater details after describing the signal modeling process.…”

Section: Diffusion Orientation Transform (Dot) (Non-parametric)mentioning

confidence: 99%

Recent advances in diffusion MRI modeling: Angular and radial reconstruction

Assemlal

Tschumperlé

Brun

et al. 2011

Medical Image Analysis

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Recent advances in diffusion magnetic resonance image (dMRI) modeling have led to the development of several state of the art methods for reconstructing the diffusion signal. These methods allow for distinct features to be computed, which in turn reflect properties of fibrous tissue in the brain and in other organs. A practical consideration is that to choose among these approaches requires very specialized knowledge. In order to bridge the gap between theory and practice in dMRI reconstruction and analysis we present a detailed review of the dMRI modeling literature. We place an emphasis on the mathematical and algorithmic underpinnings of the subject, categorizing existing methods according to how they treat the angular and radial sampling of the diffusion signal. We describe the features that can be computed with each method and discuss its advantages and limitations. We also provide a detailed bibliography to guide the reader.

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“…To this aim, a large number of alternative reconstruction techniques have been developed to recover both the angular and radial component of the diffusion process. Among them, to mention a few, we can cite the models based on multiple [8]- [11] or higher-order tensors [12]- [14], the Q-Ball Imaging (QBI) [15] and its variant computed in constant solid angle [16], [17], the Persistent Angular Structure MRI (PAS-MRI) [18], the Composite Hindered And Restricted Model of Diffusion (CHARMED) [19], the Diffusion Orientation Transform (DOT) [20], [21], the Hybrid Diffusion Imaging (HIDY) [22], the Generalized Q-sampling Imaging (GQI) [23] and all the algorithms based on spherical deconvolution [24]- [29]. Multi-compartment models have been also proposed in the literature to explain the measured dMRI signal more accurately; see [30] for a detailed survey.…”

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confidence: 99%

Quantitative Comparison of Reconstruction Methods for Intra-Voxel Fiber Recovery From Diffusion MRI

Daducci¹,

Canales‐Rodríguez²,

Descoteaux³

et al. 2014

IEEE Trans. Med. Imaging

153

180

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Abstract-Validation is arguably the bottleneck in the diffusion MRI community. This paper evaluates and compares 20 algorithms for recovering the local intra-voxel fiber structure from diffusion MRI data and is based on the results of the "HARDI reconstruction challenge" organized in the context of the "ISBI 2012" conference. Evaluated methods encompass a mixture of classical techniques well-known in the literature such as Diffusion Tensor, Q-Ball and Diffusion Spectrum imaging, algorithms inspired by the recent theory of compressed sensing and also brand new approaches proposed for the first time at this contest. To quantitatively compare the methods under controlled conditions, two datasets with known ground-truth were synthetically generated and two main criteria were used to evaluate the quality of the reconstructions in every voxel: correct assessment of the number of fiber populations and angular accuracy in their orientation. This comparative study investigates the behavior of every algorithm with varying experimental conditions and highlights strengths and weaknesses of each approach.

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Estimation of fiber Orientation Probability Density Functions in High Angular Resolution Diffusion Imaging

Cited by 98 publications

References 22 publications

Beyond fractional anisotropy: Extraction of bundle-specific structural metrics from crossing fiber models

Beyond fractional anisotropy: Extraction of bundle-specific structural metrics from crossing fiber models

Recent advances in diffusion MRI modeling: Angular and radial reconstruction

Quantitative Comparison of Reconstruction Methods for Intra-Voxel Fiber Recovery From Diffusion MRI

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