Anisotropic Smoothing Improves DT-MRI-Based Muscle Fiber Tractography

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Abstract

Purpose

To assess the effect of anisotropic smoothing on fiber tracking measures, including pennation angle, fiber tract length, and fiber tract number in the medial gastrocnemius (MG) muscle in healthy subjects using diffusion-weighted magnetic resonance imaging (DW-MRI).

Materials and Methods

3T DW-MRI data were used for muscle fiber tractography in the MG of healthy subjects. Anisotropic smoothing was applied at three levels (5%, 10%, 15%), and pennation angle, tract length, fiber tract number, fractional anisotropy, and principal eigenvector orientation were quantified for each smoothing level.

Results

Fiber tract length increased with pre-fiber tracking smoothing, and local heterogeneities in fiber direction were reduced. However, pennation angle was not affected by smoothing.

Conclusion

Modest anisotropic smoothing (10%) improved fiber-tracking results, while preserving structural features.

Related collections

Most cited references 21

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Adaptive non-local means denoising of MR images with spatially varying noise levels.

José Manjón, Pierrick Coupé, Luis Martí-Bonmatí … (2010)

To adapt the so-called nonlocal means filter to deal with magnetic resonance (MR) images with spatially varying noise levels (for both Gaussian and Rician distributed noise). Most filtering techniques assume an equal noise distribution across the image. When this assumption is not met, the resulting filtering becomes suboptimal. This is the case of MR images with spatially varying noise levels, such as those obtained by parallel imaging (sensitivity-encoded), intensity inhomogeneity-corrected images, or surface coil-based acquisitions. We propose a new method where information regarding the local image noise level is used to adjust the amount of denoising strength of the filter. Such information is automatically obtained from the images using a new local noise estimation method. The proposed method was validated and compared with the standard nonlocal means filter on simulated and real MRI data showing an improved performance in all cases. The new noise-adaptive method was demonstrated to outperform the standard filter when spatially varying noise is present in the images. (c) 2009 Wiley-Liss, Inc.

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Diffusion anisotropy measurement of brain white matter is affected by voxel size: underestimation occurs in areas with crossing fibers.

H Oouchi, K. C. Yamada, K Sakai … (2016)

Voxel size/shape of diffusion tensor imaging (DTI) may directly affect the measurement of fractional anisotropy (FA) in regions where there are crossing fibers. The purpose of this article was to investigate the effect of voxel size/shape on measured FA by using isotropic and nonisotropic voxels. Ten healthy adult volunteers had MR imaging by using a 1.5 T clinical imager. DTI was performed with 2 different voxel sizes: a 2-mm-section isotropic voxel (2 x 2 x 2 mm(3)) and a 6-mm-section nonisotropic voxel (2 x 2 x 6 mm(3)). Images were obtained by using a single-shot echo-planar imaging technique with motion-probing gradients in 15 orientations and a b-value of 1000 s/mm(2). FA and the apparent diffusion coefficient (ADC) were measured at different sites of the brain. When smaller isotropic voxels were used, the FA was greater in areas with crossing fibers, including the superior longitudinal fasciculus, the thalamus, and the red nucleus; the FA was not significantly different in areas without crossing fibers, such as the corpus callosum, the posterior limb of the internal capsule, and the corticospinal tract at the level of the centrum semiovale (P>.05). The ADC values were not affected by voxel size/shape at any of the areas of the brain that were measured. FA values that are measured in regions containing crossing fibers are underestimated when using nonisotropic DTI.

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A unifying theoretical and algorithmic framework for least squares methods of estimation in diffusion tensor imaging.

Carlo Pierpaoli, Ching-Di Chang, Thomas J Carew … (2006)

A unifying theoretical and algorithmic framework for diffusion tensor estimation is presented. Theoretical connections among the least squares (LS) methods, (linear least squares (LLS), weighted linear least squares (WLLS), nonlinear least squares (NLS) and their constrained counterparts), are established through their respective objective functions, and higher order derivatives of these objective functions, i.e., Hessian matrices. These theoretical connections provide new insights in designing efficient algorithms for NLS and constrained NLS (CNLS) estimation. Here, we propose novel algorithms of full Newton-type for the NLS and CNLS estimations, which are evaluated with Monte Carlo simulations and compared with the commonly used Levenberg-Marquardt method. The proposed methods have a lower percent of relative error in estimating the trace and lower reduced chi2 value than those of the Levenberg-Marquardt method. These results also demonstrate that the accuracy of an estimate, particularly in a nonlinear estimation problem, is greatly affected by the Hessian matrix. In other words, the accuracy of a nonlinear estimation is algorithm-dependent. Further, this study shows that the noise variance in diffusion weighted signals is orientation dependent when signal-to-noise ratio (SNR) is low (

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Author and article information

Contributors

Christophe Lenglet: Role: Academic Editor

Journal

Journal ID (nlm-ta): PLoS One

Journal ID (iso-abbrev): PLoS ONE

Journal ID (publisher-id): plos

Journal ID (pmc): plosone

Title: PLoS ONE

Publisher: Public Library of Science (San Francisco, CA USA )

ISSN (Electronic): 1932-6203

Publication date (Electronic): 26 May 2015

Publication date Collection: 2015

Volume: 10

Issue: 5

Electronic Location Identifier: e0126953

Affiliations

[1 ]Vanderbilt University Institute of Imaging Science, Vanderbilt University, Nashville, Tennessee, United States of America

[2 ]Department of Radiology and Radiological Sciences, Vanderbilt University, Nashville, Tennessee, United States of America

[3 ]Department of Biomedical Engineering, Vanderbilt University, Nashville, Tennessee, United States of America

[4 ]Department of Electrical Engineering and Computer Science, Vanderbilt University, Nashville, Tennessee, United States of America

[5 ]Department of Physical Medicine and Rehabilitation, Vanderbilt University, Nashville, Tennessee, United States of America

[6 ]Department of Molecular Physiology and Biophysics, Vanderbilt University, Nashville, Tennessee, United States of America

University of Minnesota, UNITED STATES

Author notes

Competing Interests: The authors have declared that no competing interests exist.

Conceived and designed the experiments: AKWB BMD. Performed the experiments: AKWB CPE. Analyzed the data: AKWB. Contributed reagents/materials/analysis tools: ZD BMD. Wrote the paper: AKWB ZD CPE BMD. Refined imaging protocol: AKWB BMD TFT.

* E-mail: bruce.m.damon@ 123456vanderbilt.edu

Article

Publisher ID: PONE-D-14-28260

DOI: 10.1371/journal.pone.0126953

PMC ID: 4444336

PubMed ID: 26010830

SO-VID: 78217aef-ace0-4cc6-b2ce-2996c4447875

License:

This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited

History

Date received : 24 June 2014

Date accepted : 9 April 2015

Page count

Figures: 3, Tables: 2, Pages: 9

Funding

AKWB, ZD, CPE, TFT, and BMD received support from the National Institutes of Health, National Institute of Arthritis and Musculoskeletal and Skin Diseases; www.niams.nih.gov; R01 AR050101. TFT received support from the National Institutes of Health; National Center for Research Resources; www.nih.gov; 2 ULI TR000445-06

Custom metadata

Data Availability A de-identified dataset is available upon request to the principal investigator, Dr. Bruce Damon ( bruce.m.damon@ 123456vanderbilt.edu ).

Anisotropic Smoothing Improves DT-MRI-Based Muscle Fiber Tractography

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Abstract

Purpose

Materials and Methods

Results

Conclusion

Related collections

PLOS Climate

Most cited references 21

Adaptive non-local means denoising of MR images with spatially varying noise levels.

Diffusion anisotropy measurement of brain white matter is affected by voxel size: underestimation occurs in areas with crossing fibers.

A unifying theoretical and algorithmic framework for least squares methods of estimation in diffusion tensor imaging.

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