*GRAND Seminar* 12:00 noon, January 26, Tuesday, 2010, ENGR 4201

Diffusion Tensor Magnetic Resonance Imaging (DT-MRI) --- Robust
Diffusion Tensor Estimation by Outlier Rejection


Lin-Ching Chang,
Assistant Professor
Department of Electrical Engineering and Computer Science
The Catholic University of America



The presentation will begin by talking about the background
and basic concepts underlying diffusion tensor magnetic resonance
imaging (DT-MRI). Having explained the basic principle, we will then
consider how the diffusion tensor is actually estimated from data,
what quantitative parameters can be extracted from the tensor, and how
the tensor derived quantities can be used in clinical research and
applications. The NIH pediatric neuroimaging project
( will be used as
an example to demonstrate how DTI can be used to study normal human
brain development. The presentation will pose several problems in DTI
processing and analysis, particularly how the artifacts can affect the
tensor estimation. Enlightened solutions will be also presented in
detail when dealing with artifacts in DTI.


In addition to routine magnetic resonance (MR) imaging,
diffusion tensor imaging (DTI) is a well-established noninvasive
method. DT-MRI is increasingly used in clinical research and
applications for its ability to depict white matter tracts and for its
sensitivity to microstructural orientation and architectural features
of brain tissue in vivo. Despite its increasing prevalent clinical
use, DT-MRI suffers from generally poor image quality compared to
other established structural MRI acquisition.

Diffusion tensor maps are typically computed by fitting the signal
intensities from diffusion weighted images (DWI) to the multivariate
least-squares regression model proposed by Basser et al (1). The
least-squares regression model takes into account the signal
variability produced by thermal noise, however, signal variability in
diffusion weighted imaging is influenced not only by thermal noise but
also by spatially and temporally varying artifacts. Such artifacts
originate from the so called “physiologic noise” such as subject
motion and cardiac pulsation, as well as from acquisition-related
factors such as system instabilities. In this presentation, the
effects of DWI artifacts on estimated tensor values are analyzed using
Monte Carlo simulations. A novel approach for robust diffusion tensor
estimation, called RESTORE (2, 3), will be discussed.


1. Basser PJ, Mattiello J, LeBihan D. Estimation of the
effective selfdiffusion tensor from the NMR spin echo. J Magn Reson B

2. Chang, LC, Jones, DK, and Pierpaoli, C. (2005) RESTORE: Robust
estimation of tensors by outlier rejection. Magn Reson Med.

3. Chang, L.-C., Walker, L., and Pierpaoli, C. (2009) , Making the
Robust Tensor Estimation Approach: "RESTORE" more Robust. In
Proceeding ISMRM 17th Ann. Mtg: p. 5707.

*Short Bio*

Dr. Lin-Ching Chang is an assistant professor of electrical
engineering and computer science at the Catholic University of
America, Washington DC, USA. Her research over the past six years has
persistently emphasized in the area of magnetic resonance imaging
(MRI) processing and analysis. During her career at the National
Institutes of Health (NIH), she was working on quantitative image
analysis of diffusion tensor magnetic resonance imaging (DT-MRI) data
for human brain development. Prior to joining the NIH, Dr. Chang has
worked at 3Com Corporation, where she joined and led a number of
commercial software projects in telecommunication. Her research
interests include software development in medical image analysis,
pattern recognition, combinatorial design, information retrial, and
telecommunication applications.

*Jyh-Ming Lien*
Assistant Professor, George Mason University