Apparent Fibre Density: A novel measure for the analysis of diffusion-weighted magnetic resonance images

Raffelt, David; Tournier, Jacques‐Donald; Rose, Stephen; Ridgway, Gerard R.; Henderson, Robert D.; Crozier, Stuart; Salvado, Olivier; Connelly, Alan

doi:10.1016/j.neuroimage.2011.10.045

Cited by 543 publications

(652 citation statements)

References 79 publications

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“…Quantification is therefore better performed on the basis of, for example, other complementary track-based parameters, such as track-weighted apparent diffusion coefficient (TW-ADC), trackweighted fractional anisotropy (TW-FA), and track-weighted fiber-orientation distribution (TW-FOD), 12,17 or even on the basis of other properties of the streamlines themselves (such as their lengths in the average pathlength map [APM] method) 13 or on measures of the voxelwise fiber-orientation distribution (such as those related to the apparent fiber density [AFD] method). 14,18 While these maps have reduced anatomic contrast relative to that seen in TDI maps, they have more reliable quantitative properties 12,13 and are therefore more suitable for quantitative analysis in clinical applications.…”

Section: -14mentioning

confidence: 99%

Super-Resolution Track Density Imaging: Anatomic Detail versus Quantification

Calamante

2016

AJNR Am J Neuroradiol

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Section: -14mentioning

confidence: 99%

Super-Resolution Track Density Imaging: Anatomic Detail versus Quantification

Calamante

2016

AJNR Am J Neuroradiol

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“…While GFA is a global measure in each voxel, Ghosh and Deriche (2011) introduced a bundle specific metric using a polynomial approach in order to represent the dODF and extracted geometric information to define a peak FA. Similarly, Raffelt et al (2012b) as well as Dell' Acqua et al (2013) introduced the length of an fODF peak as bundle specific metric.…”

Section: Metricsmentioning

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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“…Such HR and UHR D W i m a g e s w i t h h i g h S N R c o u l d p r o v i d e a b e t t e r s e n s i t i v i t y f o r the study of brain microstructure (Mori and van Zijl, 2002;Mukherjee et al, 2008;Zeineh et al, 2012). Moreover, these images could play an important role in fiber tractography to enable tracking of finer bundles or to better handle the fiber crossing problem (Dyrby et al, 2011;Miller et al, 2011;Raffelt et al, 2012). However, the acquisition of such HR or UHR images remains a challenging problem in clinical conditions since improvement in resolution is obtained at the cost of either lower SNR, longer acquisition time or both.…”

Section: Introductionmentioning

confidence: 99%

Collaborative patch-based super-resolution for diffusion-weighted images

et al. 2013

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To cite this version:Pierrick Coupé, José V Manjón, Maxime Chamberland, Maxime Descoteaux, Bassem Hiba. Collaborative patch-based super-resolution for diffusion-weighted images.. NeuroImage, Elsevier, 2013 AbstractIn this paper, a new single image acquisition super-resolution method is proposed to increase image resolution of diffusion weighted (DW) images. Based on a nonlocal patch-based strategy, the proposed method uses a non diffusion image (b0) t o c o n s t r a i n t h e r e c o n s t r u c t i o n o f DW i m a g e s .An extensive validation is presented with a gold standard built on averaging 10 high-resolution DW acquisitions. A comparison with classical interpolation methods such as trilinear and B-spline demonstrates the competitive results of our proposed approach in terms o f improvements on image reconstruction, fractional anisotropy (FA) estimation, generalized FA and angular reconstruction for tensor and high angular resolution diffusion imaging (HARDI) models. Besides, first results of reconstructed ultra high resolution DW images a r e p r e s e n t e d a t 0 . 6 x 0 . 6 x 0 . 6 m m 3 a n d 0.4x0.4x0.4mm 3 using our gold standard b a s e d o n the average of 10 acquisitions, and on a single acquisition. Finally, fiber tracking results show the potential of the proposed super-resolution approach t o a c c u r a t e l y analyze white matter brain architecture.

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Apparent Fibre Density: A novel measure for the analysis of diffusion-weighted magnetic resonance images

Cited by 543 publications

References 79 publications

Super-Resolution Track Density Imaging: Anatomic Detail versus Quantification

Super-Resolution Track Density Imaging: Anatomic Detail versus Quantification

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

Collaborative patch-based super-resolution for diffusion-weighted images

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