Estimation of Diffusion Properties in Crossing Fiber Bundles

Caan, Matthan W.A.; Khedoe, H.G.; Poot, Dirk H. J.; Dekker, Arnold J. den; Olabarriaga, Sílvia D.; Grimbergen, Kees; Vliet, Lucas J. van; Vos, Frans M.

doi:10.1109/tmi.2010.2049577

Cited by 45 publications

(73 citation statements)

References 51 publications

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“…Attempts to solve this problem include restricting the number of tensor components (eg. to two components, Parker et al, 2003, Caan et al, 2010, incorporating physiological constraints (Tuch et al, 2002), reducing the complexity of the model by only allowing identical prolate tensors (Tabelow et al, 2012), stabilizing the problem by using Monte-Carlo algorithms (Kreher et al, 2005), regularizing over a spatial neighborhood (Pasternak et al, 2008, Malcolm et al, 2010, and incorporating other local models to estimate the initial nonlinear optimization of the parameters of the multi-tensor model (Schultz and Kindlmann, 2010).…”

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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“…More complex models of diffusion (e.g. [10] or [11]) were not supported by our DW-MRI data as they require the DWIs to be acquired with more than one non-zero b-value.…”

Section: Discussionmentioning

confidence: 96%

“…Notice that now the stick direction parameters (ψ 1 , φ 1 , ψ 2 , φ 2 ) are the same for each scan k. A maximum likelihood estimator using a Rician noise distribution was used to estimate the unknown parameter vector in each voxel of the intermediate space [10].…”

Section: Longitudinal Ball-and-sticks Modelmentioning

confidence: 99%

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Longitudinal analysis of diffusion-weighted MRI with a ball-and-sticks model

Arkesteijn

Poot

Niestijl

et al. 2017

2017 IEEE 14th International Symposium on Biomedical Imaging (ISBI 2017)

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Purpose: To increase the sensitivity in longitudinal analysis of DW-MRI data with the ball-and-sticks model. Methods: Longitudinal DW-MRI data (baseline and two follow-up scans) of 25 middle-aged subjects (47 to 61 years at base line) were acquired. After coregistering all the diffusion-weighted images (DWIs) from the baseline and follow-up scans to a subject-specific intermediate space, an extended ball-and-sticks model was fitted. Stick orientations were constrained such that they did not change over time. The stick fractions were warped and projected onto the TBSS (tract-based spatial statistics) skeleton, and were compared to a reference framework in which all scans were processed independently. Results: Compared to the reference framework, the standard deviation of the apparent noise on the primary stick fractions on the TBSS skeleton was reduced with approximately a factor two. Conclusion: The use of the proposed longitudinal DW-MRI pipeline may significantly increase the precision compared to a default cross-sectional image processing pipeline.

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“…Other simulation and data analysis pipelines have been also executed on grid resources using the same methodology, e.g. [16,17].…”

Section: Grid Workflow Implementationmentioning

confidence: 99%