QuickBundles, a Method for Tractography Simplification

Garyfallidis, Eleftherios; Brett, Matthew; Correia, Marta; Williams, Guy; Nimmo‐Smith, Ian

doi:10.3389/fnins.2012.00175

Cited by 260 publications

(253 citation statements)

References 39 publications

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“…Streamline tractography was performed in the native diffusion space based on the Euler Delta Crossings algorithm using Diffusion Imaging in Python software (http:// dipy.org) and the number of these "tracts" quantified. 20 Ten thousand seed points were used with a FA threshold of 0.2.…”

Section: Diffusion Tensor Imaging Analysesmentioning

confidence: 99%

Dynamic Changes in White Matter Abnormalities Correlate With Late Improvement and Deterioration Following TBI

Newcombe

Correia

Ledig

et al. 2015

Neurorehabil Neural Repair

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Objective. Traumatic brain injury (TBI) is not a single insult with monophasic resolution, but a chronic disease, with dynamic processes that remain active for years. We aimed to assess patient trajectories over the entire disease narrative, from ictus to late outcome. Methods. Twelve patients with moderate-to-severe TBI underwent magnetic resonance imaging in the acute phase (within 1 week of injury) and twice in the chronic phase of injury (median 7 and 21 months), with some undergoing imaging at up to 2 additional time points. Longitudinal imaging changes were assessed using structural volumetry, deterministic tractography, voxel-based diffusion tensor analysis, and region of interest analyses (including corpus callosum, parasagittal white matter, and thalamus). Imaging changes were related to behavior. Results. Changes in structural volumes, fractional anisotropy, and mean diffusivity continued for months to years postictus. Changes in diffusion tensor imaging were driven by increases in both axial and radial diffusivity except for the earliest time point, and were associated with changes in reaction time and performance in a visual memory and learning task (paired associates learning). Dynamic structural changes after TBI can be detected using diffusion tensor imaging and could explain changes in behavior. Conclusions. These data can provide further insight into early and late pathophysiology, and begin to provide a framework that allows magnetic resonance imaging to be used as an imaging biomarker of therapy response. Knowledge of the temporal pattern of changes in TBI patient populations also provides a contextual framework for assessing imaging changes in individuals at any given time point.

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Section: Diffusion Tensor Imaging Analysesmentioning

confidence: 99%

Dynamic Changes in White Matter Abnormalities Correlate With Late Improvement and Deterioration Following TBI

Newcombe

Correia

Ledig

et al. 2015

Neurorehabil Neural Repair

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“…As highlighted in several fiber-related studies [77,76], fiber trajectories below 40 mm in length represent short-range connections, having lower clinical relevance (e.g., surgical planning). In applications like automated fiber grouping, such fiber trajectories may pose a considerable challenge [77]. For 455 long fiber trajectories (i.e., 80 mm to 250 mm), we observe a similar trend where the distance between distribution medians increases with minimum fiber length.…”

mentioning

confidence: 99%

“…of this approach is that fiber trajectories can now be represented based on a similarity measure tailored to this type of data, such as the Hausdorff distance [44,45,49], the mean of closest points (MCP) distance [44,50,45,49] or the Minimum average Direct Flip (MDF) distance [77]. In this work, we considered the MDF distance, which computes the average distance between points on a 240 fiber trajectory and corresponding points in a second fiber trajectory, or in the reverse point sequence of the second fiber trajectory if it leads to a smaller distance.…”

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Fiberprint: A subject fingerprint based on sparse code pooling for white matter fiber analysis

et al. 2017

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“…We use QuickBundles algorithm [6] to obtain a clustering of the fibers, and used the representatives given by the algorithm for registration. The threshold value for the bundles spread width was set to 10mm, which gives a trade-off between low cluster variability and number of fibers per cluster.…”

Section: Data Descriptionmentioning

confidence: 99%

Unsupervised Fiber Bundles Registration Using Weighted Measures Geometric Demons

Siless

Medina

Fillard

et al. 2013

Multimodal Brain Image Analysis

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Abstract. Brain image registration aims at reducing anatomical variability across subjects to create a common space for group analysis. Multi-modal approaches intend to minimize cortex shape variations along with internal structures, such as fiber bundles. A difficulty is that it requires a prior identification of these structures, which remains a challenging task in the absence of a complete reference atlas. We propose an extension of the log-Geometric Demons for jointly registering images and fiber bundles without the need of point or fiber correspondences. By representing fiber bundles as Weighted Measures we can register subjects with different numbers of fiber bundles. The efficacy of our algorithm is demonstrated by registering simultaneously T1 images and between 37 and 88 fiber bundles depending on each of the ten subject used. We compare results with a multi-modal T1 + Fractional Anisotropy (FA) and a tensor-based registration algorithms and obtain superior performance with our approach.

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QuickBundles, a Method for Tractography Simplification

Cited by 260 publications

References 39 publications

Dynamic Changes in White Matter Abnormalities Correlate With Late Improvement and Deterioration Following TBI

Dynamic Changes in White Matter Abnormalities Correlate With Late Improvement and Deterioration Following TBI

Fiberprint: A subject fingerprint based on sparse code pooling for white matter fiber analysis

Unsupervised Fiber Bundles Registration Using Weighted Measures Geometric Demons

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