Signal variability in diffusion weighted imaging (DWI) is influenced by both thermal noise and spatially and temporally varying artifacts such as subject motion and cardiac pulsation. In this paper, the effects of DWI artifacts on estimated tensor values, such as trace and fractional anisotropy, are analyzed using Monte Carlo simulations. A novel approach for robust diffusion tensor estimation, called RESTORE (for robust estimation of tensors by outlier rejection), is proposed. This method uses iteratively reweighted least-squares regression to identify potential outliers and subsequently exclude them. Diffusion tensor magnetic resonance imaging (DT-MRI) is used increasingly in clinical research for its ability to depict white matter tracts and for its sensitivity to microstructural and architectural features of brain tissue. Diffusion tensor maps are typically computed by fitting the signal intensities from diffusion weighted images as a function of their corresponding b-matrices (1) according to the multivariate least-squares regression model proposed by Basser et al. (2). The least-squares (LS) regression model takes into account the signal variability produced by thermal noise by including the assumed signal variance as a weighting factor in the tensor fitting. Signal variability in diffusion weighted imaging (DWI), however, 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. The multivariate leastsquares regression model assumes that the signal variability in the DWI is affected only by thermal noise and does not account for signal perturbations and potential outliers that originate from artifacts. While the signal variability produced by thermal noise is approximately Gaussian distributed (3), signal variability produced by physiologic noise and other artifacts does not have a known parametric distribution and currently cannot be modeled. Situations in which experimental errors do not follow a Gaussian distribution, or are unknown, are generally addressed statistically by using "robust" estimators, which are less sensitive to the presence of outliers.Surprisingly, the use of robust estimators has been largely neglected by the DT-MRI community. We are aware of only one robust tensor estimation approach recently proposed by Mangin et al. (4), which is based on the well-known Geman-McClure M-estimator (5) (we will refer to Mangin's approach as GMM in this paper). This approach uses an iteratively reweighted least-squares fitting in which the weight of each data point is set to a function of the residuals of the previous iteration. The GMM method ensures that potentially artifactual data points having large residuals are given lower weights in the estimation of the tensor parameters. Clearly, this approach is statistically more robust than the standard LS methods in the presence of outl...

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Using a population-based sampling strategy, the National Institutes of Health (NIH) Magnetic Resonance Imaging Study of Normal Brain Development compiled a longitudinal normative reference database of neuroimaging and correlated clinical/behavioral data from a demographically representative sample of healthy children and adolescents aged newborn through early adulthood. The present paper reports brain volume data for 325 children, ages 4.5-18 years, from the first cross-sectional time point. Measures included volumes of whole-brain gray matter (GM) and white matter (WM), left and right lateral ventricles, frontal, temporal, parietal and occipital lobe GM and WM, subcortical GM (thalamus, caudate, putamen, and globus pallidus), cerebellum, and brainstem. Associations with cross-sectional age, sex, family income, parental education, and body mass index (BMI) were evaluated. Key observations are: 1) age-related decreases in lobar GM most prominent in parietal and occipital cortex; 2) age-related increases in lobar WM, greatest in occipital, followed by the temporal lobe; 3) age-related trajectories predominantly curvilinear in females, but linear in males; and 4) small systematic associations of brain tissue volumes with BMI but not with IQ, family income, or parental education. These findings constitute a normative reference on regional brain volumes in children and adolescents.