Processing and visualization for diffusion tensor MRI

Westin, Carl‐Fredrik; Maier, Stephan E.; Mamata, Hatsuho; Nabavi, Arya; Jólesz, Ferenc A.; Kikinis, Ron

doi:10.1016/s1361-8415(02)00053-1

Cited by 733 publications

(591 citation statements)

References 20 publications

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“…The most commonly used indices, proposed by Basser and colleagues (Basser, 1995) are: (1) relative anisotropy (RA), a normalized standard deviation representing the ratio of the anisotropic part of the tensor to its isotropic part; (2) fractional anisotropy (FA), a measure of the fraction of the magnitude of the tensor that can be ascribed to the anisotropic diffusion; and, (3) volume ratio (VR), a measure representing the ratio of the ellipsoid volume to the volume as a sphere of radius λ. Other anisotropy indices, such as indices measuring intervoxel coherence (Pfefferbaum et al, 2000), and the geometric nature of diffusion and intravoxel coherence (Westin et al, 2002), have also been proposed, and are reviewed in detail by Kubicki et al (2002b) (for examples of maps representing diffusion indices see Fig. 3).…”

Section: Quantitative Representation Of Diffusion Tensormentioning

confidence: 99%

A review of diffusion tensor imaging studies in schizophrenia

Kubicki

McCarley

Westin

et al. 2007

Journal of Psychiatric Research

689

532

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Both post-mortem and neuroimaging studies have contributed significantly to what we know about the brain and schizophrenia. MRI studies of volumetric reduction in several brain regions in schizophrenia have confirmed early speculations that the brain is disordered in schizophrenia. There is also a growing body of evidence suggesting that a disturbance in connectivity between different brain regions, rather than abnormalities within the separate regions themselves, are responsible for the clinical symptoms and cognitive dysfunctions observed in this disorder. Thus an interest in white matter fiber tracts, subserving anatomical connections between distant, as well as proximal, brain regions, is emerging. This interest coincides with the recent advent of diffusion tensor imaging (DTI), which makes it possible to evaluate the organization and coherence of white matter fiber tracts. This is an important advance as conventional MRI techniques are insensitive to fiber tract direction and organization, and have not consistently demonstrated white matter abnormalities. DTI may, therefore, provide important new information about neural circuitry, and it is increasingly being used in neuroimaging studies of psychopathological disorders. Of note, in the past five years 18 DTI studies in schizophrenia have been published, most describing white matter abnormalities. Questions still remain, however, regarding what we are measuring that is abnormal in this disease, and how measures obtained using one method correspond to those obtained using other methods? Below we review the basic principles involved in MR-DTI, followed by a review of the different methods used to evaluate diffusion. Finally, we review MR-DTI findings in schizophrenia.

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Section: Quantitative Representation Of Diffusion Tensormentioning

confidence: 99%

A review of diffusion tensor imaging studies in schizophrenia

Kubicki

McCarley

Westin

et al. 2007

Journal of Psychiatric Research

689

532

View full text Add to dashboard Cite

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“…The aFM algorithm takes into account the entire information contained in the diffusion tensor. Thereby, every tensor is classified as prolate (linear), oblate (planar), or spherical ellipsoid (Westin et al, 2002(Westin et al, , 1997. During the actual tracking process, different adapted speed functions are applied for the front propagation, according to the shape classification of two adjacent voxels.…”

Section: Dti Data Analysismentioning

confidence: 99%

“…These obstacles have been addressed by recent studies. Many groups have proposed sophisticated techniques to improve fiber tracking Staempfli et al, 2006;Weinstein et al, 1999;Westin et al, 2002;Zhang et al, 2004).…”

Section: Introductionmentioning

confidence: 99%

Combining fMRI and DTI: A framework for exploring the limits of fMRI-guided DTI fiber tracking and for verifying DTI-based fiber tractography results

et al. 2008

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Combining fMRI and DTI: a framework for exploring the limits of fMRI-guided DTI fiber tracking and for verifying DTI-based fiber tractography resultsStaempfli, P; Reischauer, C; Jaermann, T; Valavanis, A; Kollias, S; Boesiger, P Staempfli, P; Reischauer, C; Jaermann, T; Valavanis, A; Kollias, S; Boesiger, P (2008 Combining fMRI and DTI: a framework for exploring the limits of fMRI-guided DTI fiber tracking and for verifying DTI-based fiber tractography results AbstractA powerful, non-invasive technique for estimating and visualizing white matter tracts in the human brain in vivo is white matter fiber tractography that uses magnetic resonance diffusion tensor imaging. The success of this method depends strongly on the capability of the applied tracking algorithm and the quality of the underlying data set. However, DTI-based fiber tractography still lacks standardized validation. In the present work, a combined fMRI/DTI study was performed, both to develop a setup for verifying fiber tracking results using fMRI-derived functional connections and to explore the limitations of fMRI based DTI fiber tracking. Therefore, a minor fiber bundle that features several fiber crossings and intersections was examined: The striatum and its connections to the primary motor cortex were examined by using two approaches to derive the somatotopic organization of the striatum. First, an fMRI-based somatotopic map of the striatum was reconstructed, based on fMRI activations that were provoked by unilateral motor tasks. Second, fMRI-guided DTI fiber tracking was performed to generate DTI-based somatotopic maps, using a standard line propagation and an advanced fast marching algorithm. The results show that the fiber connections reconstructed by the advanced fast marching algorithm are in good agreement with known anatomy, and that the DTI-revealed somatotopy is similar to the fMRI somatotopy. Furthermore, the study illustrates that the combination of fMRI with DTI can supply additional information in order to choose reasonable seed regions for generating functionally relevant networks and to validate reconstructed fibers. A powerful, non-invasive technique for estimating and visualizing white matter tracts in the human brain in vivo is white matter fiber tractography that uses magnetic resonance diffusion tensor imaging. The success of this method depends strongly on the capability of the applied tracking algorithm and the quality of the underlying data set. However, DTI-based fiber tractography still lacks standardized validation. In the present work, a combined fMRI/DTI study was performed, both to develop a setup for verifying fiber tracking results using fMRI-derived functional connections and to explore the limitations of fMRI based DTI fiber tracking. Therefore, a minor fiber bundle that features several fiber crossings and intersections was examined: The striatum and its connections to the primary motor cortex were examined by using two approaches to derive the somatotopic organization of the striatum. First, an fMRI-based somat...

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“…Thus, linking the major diffusion axes in white matter makes possible the visualization and appreciation of white matter tracts within the brain. Such fiber-tracing schemes, also called DT-MRI tractography (Basser, 1998;Conturo, et al 1999;Jones et al, 1999;Mori et al, 1999;Westin et al, 1999Westin et al, , 2002Poupon et al, 2000;Gossl et al, 2002;Mori et al, 2002), where fiber tracts follow the major eigenvector of the diffusion tensor, provide important information about the connectivity between brain regions.…”

Section: Introductionmentioning

confidence: 99%

Spatial normalization of diffusion tensor MRI using multiple channels

et al. 2003

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Diffusion Tensor MRI (DT-MRI) can provide important in vivo information for the detection of brain abnormalities in diseases characterized by compromised neural connectivity. To quantify diffusion tensor abnormalities based on voxel-based statistical analysis, spatial normalization is required to minimize the anatomical variability between studied brain structures. In this article, we used a multiple input channel registration algorithm based on a demons algorithm and evaluated the spatial normalization of diffusion tensor image in terms of the input information used for registration. Registration was performed on 16 DT-MRI data sets using different combinations of the channels, including a channel of T2-weighted intensity, a channel of the fractional anisotropy, a channel of the difference of the first and second eigenvalues, two channels of the fractional anisotropy and the trace of tensor, three channels of the eigenvalues of the tensor, and the six channel tensor components. To evaluate the registration of tensor data, we defined two similarity measures, i.e., the endpoint divergence and the mean square error, which we applied to the fiber bundles of target images and registered images at the same seed points in white matter segmentation. We also evaluated the tensor registration by examining the voxel-by-voxel alignment of tensors in a sample of 15 normalized DTMRIs. In all evaluations, nonlinear warping using six independent tensor components as input channels showed the best performance in effectively normalizing the tract morphology and tensor orientation. We also present a nonlinear method for creating a group diffusion tensor atlas using the average tensor field and the average deformation field, which we believe is a better approach than a strict linear one for representing both tensor distribution and morphological distribution of the population.

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Processing and visualization for diffusion tensor MRI

Cited by 733 publications

References 20 publications

A review of diffusion tensor imaging studies in schizophrenia

A review of diffusion tensor imaging studies in schizophrenia

Combining fMRI and DTI: A framework for exploring the limits of fMRI-guided DTI fiber tracking and for verifying DTI-based fiber tractography results

Spatial normalization of diffusion tensor MRI using multiple channels

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