Hi All,

Due to some miscommunication between the speaker and I the talk
now is postponed to the later time in the semester.



Jyh-Ming Lien wrote:
> *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
> *Speaker*
> Lin-Ching Chang,
> Assistant Professor
> Department of Electrical Engineering and Computer Science
> The Catholic University of America
> *Abstract*
> Overview
> 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.
> Details
> 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.
> References
> 1. Basser PJ, Mattiello J, LeBihan D. Estimation of the
> effective selfdiffusion tensor from the NMR spin echo. J Magn Reson B
> 1994;103:247–254.
> (
> 2. Chang, LC, Jones, DK, and Pierpaoli, C. (2005) RESTORE: Robust
> estimation of tensors by outlier rejection. Magn Reson Med.
> 53:1088-1095.
> (
> 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