A framework for a streamline‐based probabilistic index of connectivity (PICo) using a structural interpretation of MRI diffusion measurements

Parker, Geoffrey; Haroon, Hamied; Wheeler‐Kingshott, Claudia A. M.

doi:10.1002/jmri.10350

Cited by 483 publications

(381 citation statements)

References 26 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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“…algorithm [2] was used for tractography. Voxels in which a single tensor fitted the data poorly were identified using a spherical-harmonic voxel-classification algorithm [13].…”

Section: Automated Tractography Validationmentioning

confidence: 99%

“…Tractography algorithms have been proposed to characterise and delimit white matter fibre bundles [2]. However, diffusion imaging and subsequently tractography techniques are prone to imaging artefacts and algorithmic limitations [3].…”

Section: Introductionmentioning

confidence: 99%

Geodesic Shape-Based Averaging

Cardoso

Winston

Modat

et al. 2012

Medical Image Computing and Computer-Assisted Intervention – MICCAI 2012

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Abstract.A new method for the geometrical averaging of labels or landmarks is presented. This method expands the shape-based averaging [1] framework from an Euclidean to a geodesic based distance, incorporating a spatially varying similarity term as time cost. This framework has unique geometrical properties, making it ideal for propagating very small structures following rigorous labelling protocols. The method is used to automate the seeding and way-pointing of optic radiation tractography in DTI imaging. The propagated seeds and waypoints follow a strict clinical protocol by being geometrically constrained to one single slice and by guaranteeing spatial contiguity. The proposed method not only reduces the fragmentation of the propagated areas but also significantly increases the seed positioning accuracy and subsequent tractography results when compared to state-of-the-art label fusion techniques.

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“…Given the sensitivity of DT imaging to the local direction of nerve fiber bundles it has the potential to provide information on brain connectivity in a non-invasive manner. Many approaches have been used to derive connectivity information from DT data [15][16][17] and some have made good use of Bayesian probability theory to estimate the model parameters [15]; however most of these attempts have solved each pixel independently of its neighbors. In this project we extend the standard DT model by including a local connectivity parameter, Λ, and then use Bayesian probability theory to compute the probability that a given pixel is connected to its neighbors.…”

Section: Introductionmentioning

confidence: 99%

Computing the Probability of Local Brain Connectivity using Diffusion Tensor Imaging

Shimony¹,

Epstein²,

Bretthorst³

2007

AIP Conference Proceedings

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Abstract. The diffusion tensor model has been used to analyze magnetic resonance diffusion data and has been successful in both neuroscientific and clinical applications. We propose an enhancement of this model with a local connectivity parameter that better accords with the known structure of white-matter. In addition to providing diffusion tensor parameter estimation the calculation provides the probability that a given pixel is connected to one of its nearest neighbors. These probabilities can be used in further calculations to determine the probability of connectivity between different brain regions. Implementation of the algorithm is discussed in addition to its usage in simulated and in-vivo magnetic resonance diffusion tensor data.

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A framework for a streamline‐based probabilistic index of connectivity (PICo) using a structural interpretation of MRI diffusion measurements

Cited by 483 publications

References 26 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

Geodesic Shape-Based Averaging

Computing the Probability of Local Brain Connectivity using Diffusion Tensor Imaging

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