Fiber tracking: principles and strategies – a technical review

Mori, Susumu; Zijl, Peter van

doi:10.1002/nbm.781

Cited by 1,818 publications

(1,298 citation statements)

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“…The assumption that water more strongly diffuses inside and along directed structures in tissues than orthogonal to them enables the visualization of this anisotropy. DTI has been successfully applied to the analysis and depiction of neuronal fibers in the living human brain (Mori and van Zijl, 2002). Furthermore, qualitative agreement between main diffusion directions and the fiber orientation in myocardium (Garrido et al, 1994;Reese et al, 1995;Scollan et al, 1998;Schmid et al, 2005) and skeletal muscle (Van Donkelaar et al, 1999;Napadow et al, 2001;Damon et al, 2002;Mori and van Zijl, 2002;Heemskerk et al, 2005) was observed.…”

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confidence: 87%

“…1C, D, and E) were inspected in order to find directional structures in the examined uteri. To delineate the global architecture, the arrangement of the fibers was traced using fiber assignment by continuous tracking (FACT) (Mori and van Zijl, 2002). Basically, the algorithm allows a user to define and trace a number of fibers that start in arbitrarily chosen voxels ( Fig.…”

Section: Data Postprocessing and Visualizationmentioning

confidence: 99%

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Three‐dimensional fiber architecture of the nonpregnant human uterus determined ex vivo using magnetic resonance diffusion tensor imaging

et al. 2005

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The global muscle and collagen fiber orientation in the human uterus has been analyzed hitherto by various standard microscopic techniques. However, no widely accepted model of the fiber architecture of the myometrium could be acquired. The purpose of the present study was to investigate the uterus by magnetic resonance (MR) diffusion tensor imaging (DTI) in a 3D macroscopic approach. Ex vivo MR DTI measurements were performed on five uteri from nonpregnant patients. The main diffusion directions reflecting the orientation of directional structures in the examined tissues were determined from diffusion-weighted spin-echo measurements. A fiber tracking algorithm was used to extrapolate the fiber architecture. The method was validated against histological slides and indirectly through the analysis of leiomyomas, which exhibit less anisotropy than normal myometrium. Significant anisotropy was found in most regions of all examined nonpregnant human uteri. But only two systems of fibers were found running circularly along the intramural part of the uterine tubes. They merged caudally and built a close fitting envelope of circular layers around the uterine cavity. On the cervix, circular fibers were observed in the outer part as well as mostly longitudinal fibers in the inner part. These results confirm the existence of directional structures in the complex fiber architecture of the human uterus. They also indicate that MR DTI is a beneficial and complementary tool to standard microscopic techniques to determine the intrinsic fiber architecture in human organs.

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confidence: 87%

Section: Data Postprocessing and Visualizationmentioning

confidence: 99%

Three‐dimensional fiber architecture of the nonpregnant human uterus determined ex vivo using magnetic resonance diffusion tensor imaging

et al. 2005

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“…This information on the local fiber layout can then be used to compute fiber pathways through the white matter (tractography; Conturo et al, 1999, Mori and van Zijl, 2002, Behrens et al, 2003, or to derive measures characterizing the state of the tissue, which can in turn be used to detect changes and differences related to, for example, development, aging, disease, learning, and cognitive performance (for an overview, see Jones et al, 2013).…”

Section: Introductionmentioning

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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“…At the same time, the anisotropic orientation of water diffusion being also dependent on the microstructure of the brain was addressed [5] and expressed as self-diffusion tensor [6]. Soon, several tracking methods for fiber tracking were proposed (for a review, see [7]). The most famous and commonly used tracking algorithms are fiber assignment by continuous tracking (FACT) [8][9][10] and probabilistic tracking [11][12][13][14][15].…”

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confidence: 99%