Diffusion-tensor MRI: theory, experimental design and data analysis – a technical review
Corresponding Author
Peter J. Basser
Section on Tissue Biophysics and Biomimetics, NICHD, National Institutes of Health, Bethesda, MD, USA
Section on Tissue Biophysics and Biomimetics, NICHD, National Institutes of Health, Bethesda, Maryland, 20892, USASearch for more papers by this authorDerek K. Jones
Section of Old Psychiatry, Institute of Psychiatry, London, UK
Search for more papers by this authorCorresponding Author
Peter J. Basser
Section on Tissue Biophysics and Biomimetics, NICHD, National Institutes of Health, Bethesda, MD, USA
Section on Tissue Biophysics and Biomimetics, NICHD, National Institutes of Health, Bethesda, Maryland, 20892, USASearch for more papers by this authorDerek K. Jones
Section of Old Psychiatry, Institute of Psychiatry, London, UK
Search for more papers by this authorAbstract
This article treats the theoretical underpinnings of diffusion-tensor magnetic resonance imaging (DT-MRI), as well as experimental design and data analysis issues. We review the mathematical model underlying DT-MRI, discuss the quantitative parameters that are derived from the measured effective diffusion tensor, and describe artifacts thet arise in typical DT-MRI acquisitions. We also discuss difficulties in identifying appropriate models to describe water diffusion in heterogeneous tissues, as well as in interpreting experimental data obtained in such issues. Finally, we describe new statistical methods that have been developed to analyse DT-MRI data, and their potential uses in clinical and multi-site studies. Copyright © 2002 John Wiley & Sons, Ltd.
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Abbreviations used:
-
- ADC
-
apparent diffusion coefficient
-
- CSF
-
cerebrospinal fluid
-
- DT-MRI
-
diffusion tensor MRI
-
- DI
-
diffusion imaging
-
- DWI
-
diffusion-weighted imaging
-
- EPI
-
echo-planar imaging
-
- FA
-
fractional anisotropy
-
- RA
-
relative anisotropy
-
- RMS
-
root mean square
-
- SNR
-
signal to noise ratio
Citing Literature
Special Issue: Diffusion tensor imaging and axonal mapping ‐ state of the art
November ‐ December 2002
Pages 456-467