White matter tractography by anisotropic wavefront evolution and diffusion tensor imaging

Jackowski, Marcel P.; Kao, Chiu Yen; Qiu, Maolin; Constable, R. Todd; Staib, Lawrence H.

doi:10.1016/j.media.2005.05.008

Cited by 66 publications

(77 citation statements)

References 32 publications

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“…The extraction of fibers can be done, for instance, by estimating diffusion tensor images and then using streamline tractography (Basser et al, 2000;Fillard et al, 2007a;Malcolm et al, 2010), with possible uncertainty regions (Jackowski et al, 2005;Staempfli et al, 2006) or by using higher order orientation distribution functions Kumar et al, 2009). These algorithms return curves which give an estimation of the location and the orientation of the underlying neural fibers, which are compatible with the measures of diffusivity.…”

Section: Analysis Of Images Versus Analysis Of Anatomical Structuresmentioning

confidence: 99%

Registration, atlas estimation and variability analysis of white matter fiber bundles modeled as currents

et al. 2011

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Section: Analysis Of Images Versus Analysis Of Anatomical Structuresmentioning

confidence: 99%

Registration, atlas estimation and variability analysis of white matter fiber bundles modeled as currents

et al. 2011

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“…5,18 The speed of the "wavefront" toward all directions is determined by the colinearity of the principal eigenvectors in the neighboring voxels, following which the direction of fiber tracking in a region-growing manner is assigned as the one with maximum propagation speed. 19 In more advanced fast-marching algorithms, the propagation speed can also be modified on the basis of the magnitude of the eigenvalues rather than on the directions of eigenvectors alone. 20 Other variations capable of resolving crossing fibers have also been proposed.…”

Section: Fast-marching Algorithmsmentioning

confidence: 99%

Principles and Limitations of Computational Algorithms in Clinical Diffusion Tensor MR Tractography

Chung¹,

Chou²,

Chen³

2010

AJNR Am J Neuroradiol

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SUMMARY:There have been numerous reports documenting the graphic reconstruction of 3D white matter architecture in the human brain by means of diffusion tensor MR tractography. Different from other reviews addressing the physics and clinical applications of DTI, this article reviews the computational principles of tractography algorithms appearing in the literature. The simplest voxel-based method and 2 widely used subvoxel approaches are illustrated first, together with brief notes on parameter selection and the restrictions arising from the distinct attributes of tract estimations. Subsequently, some advanced techniques attempting to offer improvement in various aspects are briefly introduced, including the increasingly popular research tracking tool using HARDI. The article explains the inherent technical limitations in most of the algorithms reported to date and concludes by providing a reference guideline for formulating routine applications of this important tool to clinical neuroradiology in an objective and reproducible manner.ABBREVIATIONS CC ϭ corpus callosum; DTI ϭ diffusion tensor imaging; FA ϭ fractional anisotropy; HARDI ϭ high angular resolution diffusion imaging A t the beginning of the 21st century, the radiologic community witnessed the tremendous technical progress toward noninvasive investigations of the white matter architecture in the central nervous system. Thanks to the advancement of DTI plus the ever-growing computer technology of the socalled tractography methods, a comprehensive visualization of the white matter fiber bundles and their relationships to tumors (Fig 1) can be readily displayed without the need for any surgery.1 Needless to say, the potential implications in presurgical planning are huge, particularly if the related technology can be executed objectively and automatically on a daily basis, with the reliability approaching a level that is highly acceptable in routine practice.This article attempts to provide a pedagogic overview of the aspects of diffusion MR tractography that clinical neuroradiologists may find helpful for routine use. Because an overview of data acquisition via DTI and potential clinical applications has been provided in several review articles, 1-3 only the computational means to perform the tractography will be introduced here. In addition, only selective methods will be mentioned, because detailed comprehensive explanations about the massive amount of newly proposed algorithms are neither necessary nor insightful. At the same time, the ultimate restrictions originating from the computation methods will be addressed. With the basic concepts understood, extensions to more sophisticated algorithms would ideally be easily comprehensible. Finally, we conclude with a suggestive guideline for optimal use of diffusion MR tractography in clinical neuroradiology, emphasizing particularly the preparatory work that ought to be done before routine application. The mathematics in this entire article, though inevitable, will be kept at a minimum to ensure readabili...

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“…These global approaches are less sensitive to noise. Front propagation techniques (8)(9)(10)(11) identify the best paths from a seed to all other voxels by evolving a surface from this seed. The surface front evolves faster along the fiber direction estimates of the underlying tissue.…”

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confidence: 99%

A graph‐based approach for automatic cardiac tractography

Frindel

Robini

Schaerer

et al. 2010

Magnetic Resonance in Med

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A new automatic algorithm for assessing fiber-bundle organization in the human heart using diffusion-tensor magnetic resonance imaging is presented. The proposed approach distinguishes from the locally "greedy" paradigm, which uses voxel-wise seed initialization intrinsic to conventional tracking algorithms. It formulates the fiber tracking problem as the global problem of computing paths in a boolean-weighted undirected graph, where each voxel is a vertex and each pair of neighboring voxels is connected with an edge. This leads to a global optimization task that can be solved by iterated conditional modes-like algorithms or Metropolis-type annealing. A new deterministic optimization strategy, namely iterated conditional modes with α-relaxation using (t 2 )-and (t 4 )-moves, is also proposed; it has similar performance to annealing but offers a substantial computational gain. This approach offers some important benefits. The global nature of our tractography method reduces sensitivity to noise and modeling errors. The discrete framework allows an optimal balance between the density of fiber bundles and the amount of available data. Besides, seed points are no longer needed; fibers are predicted in one shot for the whole diffusion-tensor magnetic resonance imaging volume, in a completely automatic way. Magn Reson Med 64:1215-1229, 2010.

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White matter tractography by anisotropic wavefront evolution and diffusion tensor imaging

Cited by 66 publications

References 32 publications

Registration, atlas estimation and variability analysis of white matter fiber bundles modeled as currents

Registration, atlas estimation and variability analysis of white matter fiber bundles modeled as currents

Principles and Limitations of Computational Algorithms in Clinical Diffusion Tensor MR Tractography

A graph‐based approach for automatic cardiac tractography

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