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*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 (http://www.bic.mni.mcgill.ca/nihpd/info/index.html) 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. (http://dir2.nichd.nih.gov/nichd/stbb/Basser_estimation.pdf) 2. Chang, LC, Jones, DK, and Pierpaoli, C. (2005) RESTORE: Robust estimation of tensors by outlier rejection. Magn Reson Med. 53:1088-1095. (http://dir2.nichd.nih.gov/nichd/stbb/restore_rob_est05.pdf) 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. (http://stbb.nichd.nih.gov/pdf/Restore%2003558.pdf) *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 +1-703-993-9546 http://cs.gmu.edu/~jmlien