RESTORE: Robust estimation of tensors by outlier rejection

By Lin-Ching Chang, Derek K. Jones, Carlo Pierpaoli. Magnetic Resonance in Medicine, 2005

666 citations recorded by the source corpus.

Subfields: Radiology, Nuclear Medicine and Imaging

View Paper: https://www.semanticscholar.org/paper/1dd8ab89eab463c776936d7269534c7536df803e

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In Radiology, Nuclear Medicine and Imaging

Citations in the corpus

Clinical DT-MRI estimation, smoothing and fiber tracking with Log-Euclidean metrics (2006, background): “This gives the following criterion [7]:”
Effects of diffusion weighting schemes on the reproducibility of DTI-derived fractional anisotropy, mean diffusivity, and principal eigenvector measurements at 1.5T (2007, methodology): “Recently, other fitting methods such as robust tensor estimation methods (Chang et al. 2005; Cox, Glen 2006; Koay et al. 2006) have been introduced.”
Functionally linked resting‐state networks reflect the underlying structural connectivity architecture of the human brain (2009, methodology): “Within each voxel, the diffusion profile was fitted a tensor using a robust tensor fit method based on M-estimators [Chang et al., 2005].”
Dipy, a library for the analysis of diffusion MRI data (2014, methodology): “In addition, there is ongoing research on methods to robustly ﬁt the tensor model, in the face of noisy data (Chang et al., 2005, 2012).”
Tract Profiles of White Matter Properties: Automating Fiber-Tract Quantification (2012, methodology): “The tensors were then fit using a robust least-squares algorithm designed to remove outliers from the tensor estimation step [58].”
Anatomical Properties of the Arcuate Fasciculus Predict Phonological and Reading Skills in Children (2011, neutral): “We also collected high-resolution T1-weighted anatomical images for each subject using an 8-min sagittal 3-”
The hubs of the human connectome are generally implicated in the anatomy of brain disorders (2014, methodology): “Image preprocessing included correction for eddy currents and subject motion, normalization into standardized space using an appropriate B-matrix rotation on the diffusion gradients (Leemans and Jones, 2009), before robust estimation of the tensor model (RESTORE) (Chang et al., 2005).”
Diffusion Tensor Imaging and Beyond (2011, neutral): “Several diffusion tensor estimation procedures have been developed that take advantage of the diffusion tensor residuals to detect signal outliers (66-68).”
The NIH MRI study of normal brain development (2006, neutral): “…the midplane of the basal ganglia. late gyri and lateral white matter without touching extra-brain tissue (lipid selected voxel (blue). tensor values, so a robust non-linear tensor fitting algorithm based on iterative re-weighting was employed to identify and remove outliers (Chang et al., 2004).”
Incorporating outlier detection and replacement into a non-parametric framework for movement and distortion correction of diffusion MR images (2016, methodology): “2 Comparison to RESTORE RESTORE is a previously published method (Chang et al. (2005), Chang et al.”
A hitchhiker's guide to diffusion tensor imaging (2012, methodology): “RESTORE is a commonly used tool to estimate tensors robustly, excluding potential outliers prior to tensor estimation (Chang et al., 2005).”
Characterization of Cerebral White Matter Properties Using Quantitative MRI Stains (2011, background): “In the case where artifactual noise may be present, robust estimators may be used to identify and either remove or minimize the impact of outlier measurements (RESTORE) (Chang et al., 2005).”
Spurious group differences due to head motion in a diffusion MRI study (2013, methodology): “This can range from a procedure as simple as discarding an entire scan that exceeds acceptable levels of motion, either by visual inspection or based on a hard threshold on motion parameters, to statistical methods for detecting and discarding subsets of the data in each scan as outliers (Chang et a”
Denoising of diffusion MRI using random matrix theory (2016, background): “The collection of spatially and temporally varying image distortions, such as cardiac pulsation and motion, often referred to as physiological noise (Chang et al., 2005) are imaging artifacts.”