Fiber Tracking from DTI Using Linear State Space Models: Detectability of the Pyramidal Tract

Gössl, Christoff; Fahrmeir, Ludwig; Pütz, Benno; Auer, L. M.; Auer, Dorothee P.

doi:10.1006/nimg.2002.1055

Cited by 128 publications

(89 citation statements)

References 17 publications

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“…[34][35][36]85,86 The clinical and scientific utility of DTI fiber tracking is found in both the localization and the quantitative assessment of specific neuronal pathways, as applied to basic neuroscience, 87 cognitive neuroscience, 88,89 and diagnostic neuroradiology. [90][91][92] In each brain voxel, the dominant direction of axonal tracts can be assumed to be parallel to the primary eigenvector of the diffusion tensor.…”

Section: Dti Fiber Tracking: Methodologymentioning

confidence: 99%

Diffusion Tensor MR Imaging and Fiber Tractography: Theoretic Underpinnings

Mukherjee¹,

Berman²,

Chung³

et al. 2008

AJNR Am J Neuroradiol

428

312

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SUMMARY:In this article, the underlying theory of clinical diffusion MR imaging, including diffusion tensor imaging (DTI) and fiber tractography, is reviewed. First, a brief explanation of the basic physics of diffusion-weighted imaging (DWI) and apparent diffusion coefficient (ADC) mapping is provided. This is followed by an overview of the additional information that can be derived from the diffusion tensor, including diffusion anisotropy, color-encoded fiber orientation maps, and 3D fiber tractography. This article provides the requisite background for the second article in this 2-part review to appear next month, which covers the major technical factors that affect image quality in diffusion MR imaging, including the acquisition sequence, magnet field strength, gradient amplitude and slew rate, and multichannel radio-frequency coils and parallel imaging. The emphasis is on optimizing these factors for state-of-the-art DWI and DTI based on the best available evidence in the literature. Diffusion MR imaging of the brain was first adopted for use in clinical neuroradiology during the early 1990s and was found to have immediate utility for the evaluation of suspected acute ischemic stroke. Since that time, enormous strides forward in the technology of diffusion imaging have greatly improved image quality and enabled many new clinical applications. These include the diagnosis of intracranial pyogenic infections, masses, trauma, and vasogenic-versus-cytotoxic edema. Furthermore, the advent of diffusion tensor imaging (DTI) and fiber tractography has opened an entirely new noninvasive window on the white matter connectivity of the human brain. DTI and fiber tractography have already advanced the scientific understanding of many neurologic and psychiatric disorders and have been applied clinically for the presurgical mapping of eloquent white matter tracts before intracranial mass resections.This 2-part review explores the current state of the art for the acquisition and analysis of clinical diffusion imaging, including DTI and fiber tractography. This first article begins with an explanation of the basic physics and underlying theory of diffusion-weighted imaging (DWI) and apparent diffusion coefficient (ADC) mapping. This is followed by an overview of the additional information that can be derived from the diffusion tensor, including diffusion anisotropy, color-encoded fiber-orientation maps, and 3D fiber tractography. This article provides the necessary background for the second article, which focuses on the major technical factors that affect image quality in diffusion imaging, with an emphasis on optimizing these factors for state-of-the-art DWI and DTI based on the best available evidence in the literature. Theoretic Underpinnings of Diffusion ImagingBrownian Motion and Tissue Ultrastructure Diffusion, also known as "Brownian motion," refers to constant random microscopic molecular motion due to heat. At a fixed temperature, the rate of diffusion can be described by the Einstein equation 1 :where Ͻr 2 Ͼ refers...

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Section: Dti Fiber Tracking: Methodologymentioning

confidence: 99%

Diffusion Tensor MR Imaging and Fiber Tractography: Theoretic Underpinnings

Mukherjee¹,

Berman²,

Chung³

et al. 2008

AJNR Am J Neuroradiol

428

312

View full text Add to dashboard Cite

show abstract

“…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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“…Thus, standard linear state space techniques such as the Kalman filter are inappropriate here, and a nonlinear filter is necessary. In contrast to the work of Gossl et al (2002) which used the Kalman filter to locate the optimal path with regard to smoothness constraint for the fibers, our method deals with both smoothness regularity and uncertainties of fiber orientations induced by noise and partial volume effects.…”

Section: Tracking Algorithmmentioning

confidence: 99%

“…The second group of methods are based on global optimisation techniques (Gossl et al, 2002;Parker et al, 2002;Pichon et al, 2005). Starting from a seed point, they attempt to locate an improved estimate of the true fiber pathway using energy minimisation techniques.…”

Section: Introductionmentioning

confidence: 99%

“…For instance, Parker et al (2002) apply the fast marching technique to propagate connection paths determined by the principal eigenvectors of the tensors. Gossl et al (2002) et al apply Kalman filtering to track globally optimal paths, according to a fiber smoothness criterion. Pichon et al (2005) determine the optimal path between two voxels by solving the Hamilton-JacobiBellman equation using dynamic programming.…”

Section: Introductionmentioning

confidence: 99%

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Probabilistic white matter fiber tracking using particle filtering and von Mises–Fisher sampling

Zhang

Hancock

Goodlett

et al. 2009

Medical Image Analysis

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Standard particle filtering technique have previously been applied to the problem of fiber tracking by Brun et al. (2002) and Bjornemo et al. (2002). However, these previous attempts have not utilised the full power of the technique, and as a result the fiber paths were tracked in a goal directed way. In this paper we provide an advanced technique by presenting a fast and novel probabilistic method for white matter fiber tracking in diffusion weighted MRI (DWI), which takes advantage of the weighting and resampling mechanism of particle filtering. We formulate fiber tracking using a nonlinear state space model which captures both smoothness regularity of the fibers and the uncertainties in the local fiber orientations due to noise and partial volume effects. Global fiber tracking is then posed as a problem of particle filtering. To model the posterior distribution, we classify voxels of the white matter as either prolate or oblate tensors. We then construct the orientation distributions for prolate and oblate tensors separately. Finally, the importance density function for particle filtering is modeled using the von Mises-Fisher distribution on a unit sphere. Fast and efficient sampling is achieved using Ulrich-Wood's simulation algorithm. Given a seed point, the method is able to rapidly locate the globally optimal fiber and also provides a probability map for potential connections. The proposed method is validated and compared to alternative methods both on synthetic data and real-world brain MRI datasets.

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Fiber Tracking from DTI Using Linear State Space Models: Detectability of the Pyramidal Tract

Cited by 128 publications

References 17 publications

Diffusion Tensor MR Imaging and Fiber Tractography: Theoretic Underpinnings

Diffusion Tensor MR Imaging and Fiber Tractography: Theoretic Underpinnings

Spatial normalization of diffusion tensor MRI using multiple channels

Probabilistic white matter fiber tracking using particle filtering and von Mises–Fisher sampling

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