Processing and visualization for diffusion tensor MRI

By Carl-Fredrik Westin, S. Maier, H. Mamata, A. Nabavi, F. Jolesz, Ron Kikinis. Medical Image Anal., 2002

945 citations recorded by the source corpus.

Subfields: Computational Mathematics, Pediatrics, Perinatology and Child Health, Radiology, Nuclear Medicine and Imaging

View Paper: https://www.semanticscholar.org/paper/9af39881b04dfeb139ec0296f581aaf4750740f2

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In Computational Mathematics

In Pediatrics, Perinatology and Child Health

In Radiology, Nuclear Medicine and Imaging

Citations in the corpus

NODDI: Practical in vivo neurite orientation dispersion and density imaging of the human brain (2012, methodology): “For the remaining voxels, the gray matter partition is defined as the voxels with linearity (Westin et al., 2002) below 0.2, also estimated from the diffusion tensor fit.”
Deterministic and Probabilistic Tractography Based on Complex Fibre Orientation Distributions (2009, methodology): “The most intuitive tracking algorithms are the classical deterministic Streamline (STR) tracking algorithms [24], [25], [26] and the slightly more complex Tensor Deflection (TEND) algorithms [27], [28] used in many applications.”
Characterization of Cerebral White Matter Properties Using Quantitative MRI Stains (2011, background): “Many different stains may be derived from the eigenvalues, which are invariant to the tensor orientation [e.g., Alexander et al. (2000); Basser (1997); Ennis and Kindlmann (2006); Westin et al. (2002)].”
Automatic Tractography Segmentation Using a High-Dimensional White Matter Atlas (2007, background): “We have three threshold parameters for tractography: , , and . The first two are anisotropy thresholds based on the linear anisotropy measure [ 47 ]”
Diffusion-weighted magnetic resonance imaging as a cancer biomarker: consensus and recommendations. (2009, background): “This mean diffusivity bears the same desirable rotational independence as the trace of the full diffusion tensor without having to acquire or process DTI [70].”
The LONI Pipeline Processing Environment (2003, background): “…Halgren, 2001; Mohamed et al., 2001), and vector or tensor field calculations (Chung et al., 2001; Arfanakis et al., 2002; Haueisen et al., 2002; Ruiz-Alzola et al., 2002; Westin et al., 2002) are impractical, or impossible, on large datasets without parallel and/or distributed computing solutions.”
Clinical DT-MRI estimation, smoothing and fiber tracking with Log-Euclidean metrics (2006, background): “To get a linear system, one usually takes the logarithm [2] of the DWI.”
A review of diffusion tensor imaging studies in schizophrenia. (2007, neutral): “…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…”
Diffusion Tensor Imaging (DTI)-based White Matter Mapping in Brain Research: A Review (2007, background): “Dozens of diffusivity-based measures have been proposed (Pierpaoli and Basser 1996; Papadakis et al. 1999; Ulug and van Zijl 1999; Westin et al. 2002), but FA became the most widely used DTI-based index in brain research.”
Simulation of anisotropic growth of low‐grade gliomas using diffusion tensor imaging (2005, background): “Let cl, cp, and cs be, respectively, the linear, planar and spherical indices for the tensor, defined by (30) Simulations of anisotropic brain tumor growth 623”
White matter hemisphere asymmetries in healthy subjects and in schizophrenia: a diffusion tensor MRI study (2004, background): “The former, which is a local averaging of the tensor field with a Gaussian mask, is a powerful way to measure the local coherence of the fibers and results in stable estimates of the tensor directionality in the area where there is a clear bias in one direction (Westin et al., 2002).”
Diffusion tensor imaging of the brain (2007, neutral): “methods based on the full diffusion tensor field have also been developed.(36,49)”
Medical Image Computing and Computer-Assisted Intervention – MICCAI 2004 (2004, methodology): “In the past, many techniques have been proposed to classify gray matter, white matter and cephalo-spinal fluid from T1weighted MR [20] images but the literature addressing the issue of white matter internal structures segmentation is just beginning [21], [7], [17], [19].”