Accurate, Robust, and Automated Longitudinal and Cross-Sectional Brain Change Analysis

By Stephen M. Smith, Yongyue Zhang, Mark Jenkinson, Jacqueline Chen, Paul M. Matthews, A. Federico, Nicola De Stefano. NeuroImage, 2002

2,140 citations recorded by the source corpus.

Subfields: Biomedical Engineering, Computer Vision and Pattern Recognition, Radiology, Nuclear Medicine and Imaging

View Paper: https://www.semanticscholar.org/paper/d295252063c96ecad04165a7471700a5fc55cf3f

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In Biomedical Engineering

In Computer Vision and Pattern Recognition

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Citations in the corpus

Probabilistic diffusion tractography with multiple fibre orientations: What can we gain? (2006, methodology): “The right hand-side is a 90° rotation of the left. extraction derived from this reference scan was then applied to each volume (Smith et al., 2002).”
Improving automated multiple sclerosis lesion segmentation with a cascaded 3D convolutional neural network approach (2017, methodology): “All images were first skull-stripped using BET (Smith et al., 2002) and intensity normalized using N3 (Sled et al.”
Image processing and Quality Control for the first 10,000 brain imaging datasets from UK Biobank (2017, methodology): “Tools used to achieve this include BET (Brain Extraction Tool Smith (2002)) and FLIRT (FMRIB’s Linear Image Registration Tool Jenkinson and Smith (2001); Jenkinson et al. (2002)), in conjunction with the MNI152 “nonlinear 6th generation” standard-space T1 template 15 .”
Advances in functional and structural MR image analysis and implementation as FSL. (2004, methodology): “SIENA [34, 35] (Structural Image Evaluation, using Normalisation, of Atrophy) is a fully automated method for ﬁnding temporal brain change, taking as input two MR images taken at different points in time, and giving as output a “change” image, along with a global estimate of percentage brain volume…”
Fast robust automated brain extraction (2002, background): “…after brain/non-brain segmentation, brain volume is measured at a single time point with respect to some normalizing volume such as skull or head size; alternatively, images from two or more time points are compared, to estimate how the brain has changed over time [Smith et al., 2001, 2002].”
A unified approach for morphometric and functional data analysis in young, old, and demented adults using automated atlas-based head size normalization: reliability and validation against manual measurement of total intracranial volume (2004, positive): “While this error term is small, some applications benefit from even more precise estimates, such as longitudinal estimates of brain atrophy (Fox and Freeborough, 1997; Smith et al., 2001, 2002).”
Increased sensitivity in neuroimaging analyses using robust regression (2005, methodology): “The techniques we describe here are explicitly designed to deal with outliers, and may complement other techniques, such as data filtering and incorporation of Bayesian priors, designed to increase robustness to artifacts (Ciuciu et al., 2003; Smith et al., 2002; Woolrich et al., 2004).”
Assessing the effects of age on long white matter tracts using diffusion tensor tractography (2009, methodology): “The resulting images were skull-stripped in order to remove non-brain and ambient noise using the Brain Extraction Tool (Smith et al., 2002), and eddycorrected to account for drifts in scanner acquisition.”
Aerobic exercise training increases brain volume in aging humans. (2006, background): “and robust longitudinal registration approach to perform the initial coregistration between participants’ time 1 and time 2 images (17).”
Homocysteine-Lowering by B Vitamins Slows the Rate of Accelerated Brain Atrophy in Mild Cognitive Impairment: A Randomized Controlled Trial (2010, positive): “A cross-sectional method (SIENAX) was used to estimate normalized brain volume from a single image, using the skull to normalize spatially, with respect to a standard image [31].”
Ocrelizumab versus Placebo in Primary Progressive Multiple Sclerosis (2017, methodology): “†† The analysis was performed with the use of SIENA/X.(22) Data were not available for 6 patients in the ocrelizumab group and 1 patient in the placebo group.”
ICA-based artifact removal diminishes scan site differences in multi-center resting-state fMRI (2015, methodology): “Individual pre-processing included motion correction (Jenkinson et al., 2002), brain-extraction (Smith et al., 2002), and spatial smoothing using a Gaussian kernel of 6mm fullwidth-at-half-maximum (FWHM).”