Diffusion tensor imaging: Structural adaptive smoothing

Tabelow, Karsten; Polzehl, Jörg; Spokoiny, Vladimir; Voss, Henning U.

doi:10.1016/j.neuroimage.2007.10.024

Cited by 52 publications

(87 citation statements)

References 44 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Additionally, because of the imperfect matching of individual brain structure, and the nonGaussian nature of MRI voxelwise signal, the normalized volumes need to be adjusted by application of a smoothing kernel [49]. Often, a Gaussian full-widthat-half-maximum (FWHM) smoothing algorithm is applied to the data across a specified search region, although smoothing algorithms other than Gaussian have been proposed specifically for application with DTI data (e.g., [84]). The effect of this smoothing is to modify extreme values within the search region to promote a normal distribution of scores.…”

Section: Methodological Approaches To Dti Studies Of Ad and MCImentioning

confidence: 99%

Diffusion Tensor Imaging in Alzheimer’s Disease and Mild Cognitive Impairment

Stebbins

Murphy

2009

Behavioural Neurology

194

View full text Add to dashboard Cite

Abstract. Structural magnetic resonance imaging (MRI) studies of Alzheimer's disease and mild cognitive impairment (MCI) have focused on the hippocampus and entorhinal cortex; gray matter structures in the medial temporal lobe. Few studies have investigated the integrity of white matter in patients with AD or MCI. Diffusion tensor imaging (DTI) is a MRI technique that allows for the interrogation of the microstructural integrity of white matter. Based on increases in translational diffusion (mean diffusivity: MD) and decreases directional diffusion (fractional anisotropy: FA) damage to white matter can be assessed. Studies have identified regions of increased MD and decreased FA in patients with AD and MCI in all lobes of the brain, as well as medial temporal lobe structures including the hippocampus, entorhinal cortex and parahippocampal white matter. The pattern of white matter integrity disruption tends to follow an anterior to posterior gradient with greater damage noted in posterior regions in AD and MCI. Recent studies have exploited inter-voxel directional similarities to develop models of white matter pathways, and have used these models to assess the integrity of inter-cerebral connections. Particular focus has been applied to the parahippocampal white matter (including the perforant path) and the posterior cingulum. Although many studies have found DTI indicators of impaired white matter in AD and MCI, other studies have failed to detect any differences in MD or FA between the groups, demonstrating the need for large replicative studies. DTI is an evolving technique and advances in its application ought to provide new insights into AD and MCI.

show abstract

Section: Methodological Approaches To Dti Studies Of Ad and MCImentioning

confidence: 99%

Diffusion Tensor Imaging in Alzheimer’s Disease and Mild Cognitive Impairment

Stebbins

Murphy

2009

Behavioural Neurology

194

View full text Add to dashboard Cite

show abstract

“…The effects of noise can also be partly ameliorated by filtering the individual diffusion-weighted images prior to fitting the tensor model. A simple isotropic smoothing could potentially blur out edges that provide information about anisotropy, and thus alternative strategies have been developed including, but not limited to, the application of anisotropic smoothing kernels, such as the Perona-Malik filter (Perona and Malik, 1990;Parker et al, 2000), Weickert filters (Weickert, 1999;Ding et al, 2005), total variance norm minimization (McGraw et al, 2004), and structural adaptive smoothing approaches (Tabelow et al, 2008).…”

Section: Johnson Rf (Radiofrequency) Noisementioning

confidence: 99%

Gaussian Modeling of the Diffusion Signal

Jones

2014

Diffusion MRI

View full text Add to dashboard Cite

“…Besides simple isotropic smoothing, which potentially blurs fine structures, more sophisticated methods for noise reduction have been developed, such as the Perona-Malik filter (Perona and Malik, 1990;Parker et al, 2000), non-linear diffusion approaches (Weickert, 1998;Ding et al, 2005;Duits and Franken, 2011), or the Propagation-Separation approach (Polzehl and Spokoiny, 2006;Tabelow et al, 2008). Other attempts combine wavelet filtering with subsequent non-linear diffusion (Lohmann et al, 2010), or perform smoothing in tensor space (Fletcher and Joshi, 2007;Fillard et al, 2007).…”

Section: Introductionmentioning

confidence: 99%

Position-orientation adaptive smoothing of diffusion weighted magnetic resonance data (POAS)

Becker

Tabelow

Voss

et al. 2012

Medical Image Analysis

View full text Add to dashboard Cite

We introduce an algorithm for diffusion weighted magnetic resonance imaging data enhancement based on structural adaptive smoothing in both voxel space and diffusion-gradient space. The method, called POAS, does not refer to a specific model for the data, like the diffusion tensor or higher order models. It works by embedding the measurement space into a space with defined metric, in this case the Lie group of three-dimensional Euclidean motion SE(3). Subsequently, pairwise comparisons of the values of the diffusion weighted signal are used for adaptation. POAS preserves the edges of the observed fine and anisotropic structures. It is designed to reduce noise directly in the diffusion weighted images and consequently also to reduce bias and variability of quantities derived from the data for specific models. We evaluate the algorithm on simulated and experimental data and demonstrate that it can be used to reduce the number of applied diffusion gradients and hence acquisition time while achieving a similar quality of data, or to improve the quality of data acquired in a clinically feasible scan time setting.

show abstract

Diffusion tensor imaging: Structural adaptive smoothing

Cited by 52 publications

References 44 publications

Diffusion Tensor Imaging in Alzheimer’s Disease and Mild Cognitive Impairment

Diffusion Tensor Imaging in Alzheimer’s Disease and Mild Cognitive Impairment

Gaussian Modeling of the Diffusion Signal

Position-orientation adaptive smoothing of diffusion weighted magnetic resonance data (POAS)

Contact Info

Product

Resources

About