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.

Diffusion weighted images (DWIs) are commonly acquired with Echo-planar imaging (EPI). B0 inhomogeneities affect EPI by producing spatially nonlinear image distortions. Several strategies have been proposed to correct EPI distortions including B 0 field mapping (B 0 M) and image registration. In this study, an experimental framework is proposed to evaluation the performance of different EPI distortion correction methods in improving DTderived quantities. A deformable registration based method with mutual information metric and cubic B-spline modeled constrained deformation field (BSP) is proposed as an alternative when B 0 mapping data are not available. BSP method is qualitatively and quantitatively compared to B 0 M method using the framework. Both methods can successful reduce EPI distortions and significantly improve the quality of DT-derived quantities. Overall, B 0 M was clearly superior in infratentorial regions including brainstem and cerebellum, as well as in the ventral areas of the temporal lobes while BSP was better in all rostral brain regions.

Background Wrist-worn accelerometry provides objective monitoring of upper-extremity functional use, such as reaching tasks, but also detects nonfunctional movements, leading to ambiguity in monitoring results. Objective Compare machine learning algorithms with standard methods (counts ratio) to improve accuracy in detecting functional activity. Methods Healthy controls and individuals with stroke performed unstructured tasks in a simulated community environment (Test duration = 26 ± 8 minutes) while accelerometry and video were synchronously recorded. Human annotators scored each frame of the video as being functional or nonfunctional activity, providing ground truth. Several machine learning algorithms were developed to separate functional from nonfunctional activity in the accelerometer data. We also calculated the counts ratio, which uses a thresholding scheme to calculate the duration of activity in the paretic limb normalized by the less-affected limb. Results The counts ratio was not significantly correlated with ground truth and had large errors ( r = 0.48; P = .16; average error = 52.7%) because of high levels of nonfunctional movement in the paretic limb. Counts did not increase with increased functional movement. The best-performing intrasubject machine learning algorithm had an accuracy of 92.6% in the paretic limb of stroke patients, and the correlation with ground truth was r = 0.99 ( P < .001; average error = 3.9%). The best intersubject model had an accuracy of 74.2% and a correlation of r =0.81 ( P = .005; average error = 5.2%) with ground truth. Conclusions In our sample, the counts ratio did not accurately reflect functional activity. Machine learning algorithms were more accurate, and future work should focus on the development of a clinical tool.

Physiological noise artifacts, especially those originating from cardiac pulsation and subject motion, are common in clinical DT-MRI acquisitions. Previous works show that signal perturbations produced by artifacts can be severe and neglecting to account for their contribution can result in erroneous diffusion tensor values. The Robust Estimation of Tensors by Outlier Rejection (RESTORE) method has been shown to be an effective strategy for improving tensor estimation on a voxel-by-voxel basis in the presence of artifactual data points in diffusion weighted images (DWIs). In this paper, we address potential instabilities that may arise when using RESTORE and propose practical constraints to improve its usability. Moreover, we introduce a method, called informed RESTORE (iRESTORE) designed to remove physiological noise artifacts in datasets acquired with low redundancy (less than 30~40 DWI volumes)—a condition in which the original RESTORE algorithm may converge to an incorrect solution. This new method is based on the notion that physiological noise is more likely to result in signal dropouts than signal increases. Results from both Monte Carlo simulation and clinical diffusion data indicate that iRESTORE performs very well in removing physiological noise artifacts for low redundancy DWI datasets.

The longitudinal relaxation time, T1, can be estimated from two or more spoiled gradient recalled echo images (SPGR) acquired with different flip angles and/or repetition times. The function relating signal intensity to flip angle and TR is non-linear; however, a linear form proposed 30 years ago is currently widely used. Here, we show that this linear method provides T1 estimates that have similar precision but lower accuracy than those obtained with a nonlinear method. We also show that T1 estimated by the linear method is biased due to improper accounting for noise in the fitting. This bias can be significant for clinical SPGR images, for example, T1 estimated in brain tissue (800ms

The immense volume of data generated by the suite of instruments on the Solar Dynamics Observatory (SDO) requires new tools for efficient identifying and accessing data that is most relevant for research. We have developed the Heliophysics Events Knowledgebase (HEK) to fill this need. The HEK system combines automated data mining using feature-detection methods and high-performance visualization systems for data markup. In addition, web services and clients are provided for searching the resulting metadata, reviewing results, and efficiently accessing the data. We review these components and present examples of their use with SDO data.