In vivo three-dimensional reconstruction of rat brain axonal projections by diffusion tensor imaging

Xue, Rong; Zijl, Peter C.M. van; Crain, Barbara J.; Solaiyappan, Meiyappan; Mori, Susumu

doi:10.1002/(sici)1522-2594(199912)42:6<1123::aid-mrm17>3.0.co;2-h

Cited by 353 publications

(232 citation statements)

References 17 publications

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“…For the 3D tract reconstruction, the Fiber Assignment by Continuous Tractography (FACT) method Xue et al, 1999) was used with a fractional anisotropy threshold of 0.2 and a principal eigenvector turning angle threshold of 40° between two connected pixels. The fiber tracking based on FACT was performed by DtiStudio (Jiang et al, 2006).…”

Section: Fiber Tracking and Roi Drawing Strategymentioning

confidence: 99%

“…Second, the maps can be used as templates for the parcellation of the white matter and tract-specific monitoring of MR parameters. In the past, tractography results have been used as ROI to perform tract-specific quantification of MR parameters (Virta et al, 1999;Xue et al, 1999;Stieltjes et al, 2001;Glenn et al, 2003;Wilson et al, 2003;Partridge et al, 2004;Berman et al, 2005;Pagani et al, 2005;Jones et al, 2006;Kanaan et al, 2006). The probabilistic approach described in this paper can be considered as an extension of this idea.…”

Section: Use Of the Probabilistic Map As An Anatomical Templatementioning

confidence: 99%

“…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%

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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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Section: Fiber Tracking and Roi Drawing Strategymentioning

confidence: 99%

Section: Use Of the Probabilistic Map As An Anatomical Templatementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

et al. 2008

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“…This motivated the development of DTT for non-invasive human studies. 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.…”

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%

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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Sensorimotor Function and Axonal Integrity in Adrenomyeloneuropathy

Zackowski¹,

Raymond²,

Mori³

et al. 2006

Arch Neurol

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In vivo three-dimensional reconstruction of rat brain axonal projections by diffusion tensor imaging

Cited by 353 publications

References 17 publications

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

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

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

Sensorimotor Function and Axonal Integrity in Adrenomyeloneuropathy

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