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Diffusion tensor MR microscopy of tissues with low diffusional anisotropy Cover

Diffusion tensor MR microscopy of tissues with low diffusional anisotropy

Open Access
|Apr 2016

Abstract

Background

Diffusion tensor imaging exploits preferential diffusional motion of water molecules residing within tissue compartments for assessment of tissue structural anisotropy. However, instrumentation and post-processing errors play an important role in determination of diffusion tensor elements. In the study, several experimental factors affecting accuracy of diffusion tensor determination were analyzed.

Materials and methods

Effects of signal-to-noise ratio and configuration of the applied diffusion-sensitizing gradients on fractional anisotropy bias were analyzed by means of numerical simulations. In addition, diffusion tensor magnetic resonance microscopy experiments were performed on a tap water phantom and bovine articular cartilage-on-bone samples to verify the simulation results.

Results

In both, the simulations and the experiments, the multivariate linear regression of the diffusion-tensor analysis yielded overestimated fractional anisotropy with low SNRs and with low numbers of applied diffusion-sensitizing gradients.

Conclusions

An increase of the apparent fractional anisotropy due to unfavorable experimental conditions can be overcome by applying a larger number of diffusion sensitizing gradients with small values of the condition number of the transformation matrix. This is in particular relevant in magnetic resonance microscopy, where imaging gradients are high and the signal-to-noise ratio is low.

DOI: https://doi.org/10.1515/raon-2016-0018 | Journal eISSN: 1581-3207 | Journal ISSN: 1318-2099
Language: English
Page range: 175 - 187
Submitted on: Aug 27, 2015
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Accepted on: Feb 8, 2016
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Published on: Apr 3, 2016
In partnership with: Paradigm Publishing Services
Publication frequency: 4 issues per year

© 2016 Franci Bajd, Carlos Mattea, Siegfried Stapf, Igor Sersa, published by Association of Radiology and Oncology
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.