Regularization of Diffusion-Based Direction Maps for the Tracking of Brain White Matter Fascicles

Poupon, Cyril; Clark, Chris A.; Frouin, Vincent; Régis, Jean; Bloch, Isabelle; Bihan, Denis Le; Mangin, Jean‐François

doi:10.1006/nimg.2000.0607

Cited by 388 publications

(303 citation statements)

References 33 publications

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“…2,3 Various groups have used DTT to track fibers in fixed rodent brain 1,25 and in living humans. [2][3][4][5]26 Physical basis of using DT-MRI to track ®bers DTT is based on diffusion tensor-encoded MRI (DT-MRI), in which diffusion-weighted imaging (DWI) data are acquired for computing the effective diffusion tensor 27 and its derived properties such as directionally averaged diffusion 28,29 and anisotropy. [30][31][32][33] Early DWI studies observed a faster diffusion along the WM fibers, [34][35][36] thought to be due in part to membranes that slow diffusion perpendicular to the fiber.…”

Section: Biological Backgroundmentioning

confidence: 99%

“…[40][41][42][43] Information on the direction of fastest diffusion has formed the basis for reconstructing the 3D trajectories of WM fiber bundles. [1][2][3][4][5]25,26 In DTT, the three-dimensional trajectory of a WM pathway (a tract) is approximated by a set of computed lines representing the trajectories of fastest water diffusion (the tracks). The WM pathways in Conturo et al 3 empirically contained typically $20-500 tracks.…”

Section: Biological Backgroundmentioning

confidence: 99%

“…2,3 Brief descriptions of various methods have been presented previously. [1][2][3][4][5] In this paper we present a detailed description of our DTT data acquisition, some illustrative results, and a detailed reliability analysis using computer simulations. Error simulations are essential for providing scientific credibility to experimental DTT results.…”

Section: Introduction Purposementioning

confidence: 99%

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Diffusion tensor fiber tracking of human brain connectivity: aquisition methods, reliability analysis and biological results

et al. 2002

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We present a description, biological results and a reliability analysis for the method of diffusion tensor tracking (DTT) of white matter fiber pathways. In DTT, diffusion-tensor MRI (DT-MRI) data are collected and processed to visualize the line trajectories of fiber bundles within white matter (WM) pathways of living humans. A detailed description of the data acquisition is given. Technical aspects and experimental results are illustrated for the geniculo-calcarine tract with broad projections to visual cortex, occipital and parietal U-fibers, and the temporocalcarine ventral pathway. To better understand sources of error and to optimize the method, accuracy and precision were analyzed by computer simulations. In the simulations, noisy DT-MRI data were computed that would be obtained for a WM pathway having a helical trajectory passing through gray matter. The error vector between the real and ideal track was computed, and random errors accumulated with the square root of track length consistent with a random-walk process. Random error was most dependent on signal-to-noise ratio, followed by number of averages, pathway anisotropy and voxel size, in decreasing order. Systematic error only occurred for a few conditions, and was most dependent on the stepping algorithm, anisotropy of the surrounding tissue, and non-equal voxel dimensions. Both random and systematic errors were typically below the voxel dimension. Other effects such as track rebound and track recovery also depended on experimental conditions. The methods, biological results and error analysis herein may improve the understanding and optimization of DTT for use in various applications in neuroscience and medicine.

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Section: Biological Backgroundmentioning

confidence: 99%

Section: Biological Backgroundmentioning

confidence: 99%

Section: Introduction Purposementioning

confidence: 99%

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Diffusion tensor fiber tracking of human brain connectivity: aquisition methods, reliability analysis and biological results

et al. 2002

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“…By comparing this information with conventional MR parameter maps, we can identify specific white matter tracts that are affected by the lesions. We can extend this approach in a more systematic way by identifying the 3D trajectories of individual white matter tracts using 3D tract reconstruction, or tractography (Conturo et al, 1999;Mori et al, 1999;Basser et al, 2000;Poupon et al, 2000;Parker et al, 2002). Once a tract of interest is defined in three dimensions, we can superimpose its coordinates on MR parameter maps to perform quantitative tract-specific monitoring of pathological conditions (Virta et al, 1999;Xue et al, 1999;Stieltjes et al, 2001;Glenn et al, 2003;Wilson et al, 2003;Partridge et al, 2004;Pagani et al, 2005).…”

Section: Introductionmentioning

confidence: 99%

Tract probability maps in stereotaxic spaces: Analyses of white matter anatomy and tract-specific quantification

et al. 2008

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Diffusion tensor imaging (DTI) is an exciting new MRI modality that can reveal detailed anatomy of the white matter. DTI also allows us to approximate the 3D trajectories of major white matter bundles. By combining the identified tract coordinates with various types of MR parameter maps, such as T 2 and diffusion properties, we can perform tract-specific analysis of these parameters. Unfortunately, 3D tract reconstruction is marred by noise, partial volume effects, and complicated axonal structures. Furthermore, changes in diffusion anisotropy under pathological conditions could alter the results of 3D tract reconstruction. In this study, we created a white matter parcellation atlas based on probabilistic maps of 11 major white matter tracts derived from the DTI data from 28 normal subjects. Using these probabilistic maps, automated tract-specific quantification of fractional anisotropy and mean diffusivity were performed. Excellent correlation was found between the automated and the individual tractography-based results. This tool allows efficient initial screening of the status of multiple white matter tracts.

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“…[25][26][27] However, it was not until the end of the decade and the beginning of the new millenium that the first successful in vivo fiber tracking results were published. [28][29][30][31][32][33][34][35][36][37][38][39][40][41][42] The reason for this time lag between the availability of the tensor diagonalization techniques that provide the vector information and the actual mapping of the fibers is due to the inherent complexity of connecting these macroscopic voxel-based vectors in a reproducible three-dimensional manner. At present, new tracking algorithms are being developed rapidly, and in this review we will provide an overview of the current state-of-the-art approaches.…”

Section: Introductionmentioning

confidence: 99%

Fiber tracking: principles and strategies – a technical review

2002

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The state of the art of reconstruction of the axonal tracts in the central nervous system (CNS) using diffusion tensor imaging (DTI) is reviewed. This relatively new technique has generated much enthusiasm and high expectations because it presently is the only approach available to non-invasively study the three-dimensional architecture of white matter tracts. While there is no doubt that DTI fiber tracking is providing exciting new opportunities to study CNS anatomy, it is very important to understand its limitations. In this review we therefore assess the basic principles and the assumptions that need to be made for each step of the study, including both data acquisition and the elaborate fiber reconstruction algorithms. Special attention is paid to situations where complications may arise, and possible solutions are reviewed. Validation issues and potential future directions and improvements are also discussed.

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Regularization of Diffusion-Based Direction Maps for the Tracking of Brain White Matter Fascicles

Cited by 388 publications

References 33 publications

Diffusion tensor fiber tracking of human brain connectivity: aquisition methods, reliability analysis and biological results

Diffusion tensor fiber tracking of human brain connectivity: aquisition methods, reliability analysis and biological results

Tract probability maps in stereotaxic spaces: Analyses of white matter anatomy and tract-specific quantification

Fiber tracking: principles and strategies – a technical review

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