Data-driven Assessment of Structural Image Quality
暂无分享,去创建一个
Christos Davatzikos | Russell T. Shinohara | Mark A. Elliott | David R. Roalf | Kosha Ruparel | Ruben C. Gur | Theodore D. Satterthwaite | Adon F. G. Rosen | Jason Blake | Kevin Seelaus | Prayosha Villa | Phillip A. Cook | Angel Garcia de La Garza | Efstathios D. Gennatas | Megan Quarmley | J. Eric Schmitt | M. Dylan Tisdall | R. Cameron Craddock | Raquel E. Gur | R. Gur | R. Gur | C. Davatzikos | M. Elliott | D. Roalf | K. Ruparel | R. Shinohara | T. Satterthwaite | Megan Quarmley | J. Eric Schmitt | Angel Garcia de la Garza | M. Dylan Tisdall | J. Blake | Kevin Seelaus | P. Villa | R. Cameron Craddock | M. Quarmley
[1] Gary H. Glover,et al. Reducing inter-scanner variability of activation in a multicenter fMRI study: Role of smoothness equalization , 2006, NeuroImage.
[2] Daniel S. Margulies,et al. The Neuro Bureau ADHD-200 Preprocessed repository , 2016, NeuroImage.
[3] Yufeng Zang,et al. DPABI: Data Processing & Analysis for (Resting-State) Brain Imaging , 2016, Neuroinformatics.
[4] Christos Davatzikos,et al. Neuroimaging of the Philadelphia Neurodevelopmental Cohort , 2014, NeuroImage.
[5] Suzanne E. Welcome,et al. Longitudinal Mapping of Cortical Thickness and Brain Growth in Normal Children , 2022 .
[6] E M Haacke,et al. MR artifacts: a review. , 1986, AJR. American journal of roentgenology.
[7] Li Qingyang,et al. Towards Automated Analysis of Connectomes: The Configurable Pipeline for the Analysis of Connectomes (C-PAC) , 2013 .
[8] D. Mathalon,et al. A quantitative magnetic resonance imaging study of changes in brain morphology from infancy to late adulthood. , 1994, Archives of neurology.
[9] E. DeLong,et al. Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. , 1988, Biometrics.
[10] K. Saleh,et al. Comprehensive assessment. , 2012, Nursing older people.
[11] N. Ryan,et al. Schedule for Affective Disorders and Schizophrenia for School-Age Children-Present and Lifetime Version (K-SADS-PL): initial reliability and validity data. , 1997, Journal of the American Academy of Child and Adolescent Psychiatry.
[12] W. A. Shewhart,et al. Quality control , 1927 .
[13] Jonathan D. Power,et al. Recent progress and outstanding issues in motion correction in resting state fMRI , 2015, NeuroImage.
[14] M. Zaitsev,et al. Motion artifacts in MRI: A complex problem with many partial solutions , 2015, Journal of magnetic resonance imaging : JMRI.
[15] Melanie Bennett,et al. Initial development and preliminary validation of a new negative symptom measure: The Clinical Assessment Interview for Negative Symptoms (CAINS) , 2010, Schizophrenia Research.
[16] A. Toga,et al. Mapping Continued Brain Growth and Gray Matter Density Reduction in Dorsal Frontal Cortex: Inverse Relationships during Postadolescent Brain Maturation , 2001, The Journal of Neuroscience.
[17] NeuroData,et al. Towards Automated Analysis of Connectomes: The Configurable Pipeline for the Analysis of Connectomes , 2015 .
[18] Justin L. Vincent,et al. Distinct brain networks for adaptive and stable task control in humans , 2007, Proceedings of the National Academy of Sciences.
[19] J. Giedd. Structural Magnetic Resonance Imaging of the Adolescent Brain , 2004, Annals of the New York Academy of Sciences.
[20] Anders M. Dale,et al. An automated labeling system for subdividing the human cerebral cortex on MRI scans into gyral based regions of interest , 2006, NeuroImage.
[21] A M Dale,et al. Measuring the thickness of the human cerebral cortex from magnetic resonance images. , 2000, Proceedings of the National Academy of Sciences of the United States of America.
[22] M. Dylan Tisdall,et al. Head motion during MRI acquisition reduces gray matter volume and thickness estimates , 2015, NeuroImage.
[23] Karl J. Friston,et al. Movement‐Related effects in fMRI time‐series , 1996, Magnetic resonance in medicine.
[24] Abraham Z. Snyder,et al. Spurious but systematic correlations in functional connectivity MRI networks arise from subject motion , 2012, NeuroImage.
[25] D. Linszen,et al. Semantic fluency deficits and reduced grey matter before transition to psychosis: A voxelwise correlational analysis , 2011, Psychiatry Research: Neuroimaging.
[26] Abraham Z. Snyder,et al. Human Connectome Project informatics: Quality control, database services, and data visualization , 2013, NeuroImage.
[27] Paul A. Yushkevich,et al. Multi-Atlas Segmentation with Joint Label Fusion , 2013, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[28] Christos Davatzikos,et al. Benchmarking of participant-level confound regression strategies for the control of motion artifact in studies of functional connectivity , 2017, NeuroImage.
[29] J. Giedd,et al. Subtle in‐scanner motion biases automated measurement of brain anatomy from in vivo MRI , 2016, Human brain mapping.
[30] T. Jernigan,et al. Maturation of human cerebrum observed in vivo during adolescence. , 1991, Brain : a journal of neurology.
[31] J. Townsend,et al. Normal brain development and aging: quantitative analysis at in vivo MR imaging in healthy volunteers. , 2000, Radiology.
[32] Alan C. Evans,et al. Brain development during childhood and adolescence: a longitudinal MRI study , 1999, Nature Neuroscience.
[33] Guido Gerig,et al. Multisite validation of image analysis methods: assessing intra- and intersite variability , 2002, SPIE Medical Imaging.
[34] Heath R. Pardoe,et al. Motion and morphometry in clinical and nonclinical populations , 2016, NeuroImage.
[35] D. Selkoe. Alzheimer's disease. , 2011, Cold Spring Harbor perspectives in biology.
[36] Stephen M. Smith,et al. Segmentation of brain MR images through a hidden Markov random field model and the expectation-maximization algorithm , 2001, IEEE Transactions on Medical Imaging.
[37] Travis B. Smith,et al. MRI artifacts and correction strategies , 2010 .
[38] L. Westlye,et al. Brain maturation in adolescence and young adulthood: regional age-related changes in cortical thickness and white matter volume and microstructure. , 2010, Cerebral cortex.
[39] A. Reiss,et al. Brain development, gender and IQ in children. A volumetric imaging study. , 1996, Brain : a journal of neurology.
[40] Brian B. Avants,et al. An Open Source Multivariate Framework for n-Tissue Segmentation with Evaluation on Public Data , 2011, Neuroinformatics.
[41] Arno Klein,et al. Large-scale evaluation of ANTs and FreeSurfer cortical thickness measurements , 2014, NeuroImage.
[42] Arno Klein,et al. A reproducible evaluation of ANTs similarity metric performance in brain image registration , 2011, NeuroImage.
[43] Nancy Kanwisher,et al. Spurious group differences due to head motion in a diffusion MRI study , 2013, NeuroImage.
[44] Bennett A. Landman,et al. Non-local STAPLE: An Intensity-Driven Multi-atlas Rater Model , 2012, MICCAI.
[45] Justin L. Vincent,et al. Evidence for a frontoparietal control system revealed by intrinsic functional connectivity. , 2008, Journal of neurophysiology.
[46] Mert R. Sabuncu,et al. The influence of head motion on intrinsic functional connectivity MRI , 2012, NeuroImage.
[47] Max Kuhn,et al. caret: Classification and Regression Training , 2015 .
[48] Richard F. Betzel,et al. Human Connectomics across the Life Span , 2017, Trends in Cognitive Sciences.
[49] Timothy O. Laumann,et al. Generation and Evaluation of a Cortical Area Parcellation from Resting-State Correlations. , 2016, Cerebral cortex.
[50] Satrajit S. Ghosh,et al. Evaluation of volume-based and surface-based brain image registration methods , 2010, NeuroImage.
[51] Brian B. Avants,et al. Registration based cortical thickness measurement , 2009, NeuroImage.
[52] Mark A. Elliott,et al. Heritability of Subcortical and Limbic Brain Volume and Shape in Multiplex-Multigenerational Families with Schizophrenia , 2015, Biological Psychiatry.
[53] M. Sobel. Asymptotic Confidence Intervals for Indirect Effects in Structural Equation Models , 1982 .
[54] Ragini Verma,et al. The impact of quality assurance assessment on diffusion tensor imaging outcomes in a large-scale population-based cohort , 2016, NeuroImage.
[55] E. Feczko,et al. Motion‐related artifacts in structural brain images revealed with independent estimates of in‐scanner head motion , 2016, Human brain mapping.
[56] N. Makris,et al. Basic principles of MRI and morphometry studies of human brain development , 2002 .
[57] Thomas F. Nugent,et al. Dynamic mapping of human cortical development during childhood through early adulthood. , 2004, Proceedings of the National Academy of Sciences of the United States of America.
[58] Marisa O. Hollinshead,et al. The organization of the human cerebral cortex estimated by intrinsic functional connectivity. , 2011, Journal of neurophysiology.
[59] Michael Brady,et al. Improved Optimization for the Robust and Accurate Linear Registration and Motion Correction of Brain Images , 2002, NeuroImage.
[60] D. Joanes,et al. Comparing measures of sample skewness and kurtosis , 1998 .
[61] Lee Friedman,et al. Measurement of Signal-to-Noise and Contrast-to-Noise in the fBIRN Multicenter Imaging Study , 2006, Journal of Digital Imaging.
[62] R. Cameron Craddock,et al. A comprehensive assessment of regional variation in the impact of head micromovements on functional connectomics , 2013, NeuroImage.
[63] Suzanne E. Welcome,et al. Mapping cortical change across the human life span , 2003, Nature Neuroscience.
[64] John G. Csernansky,et al. Open Access Series of Imaging Studies (OASIS): Cross-sectional MRI Data in Young, Middle Aged, Nondemented, and Demented Older Adults , 2007, Journal of Cognitive Neuroscience.
[65] D. Bates,et al. Fitting Linear Mixed-Effects Models Using lme4 , 2014, 1406.5823.
[66] Anders M. Dale,et al. Cortical Surface-Based Analysis I. Segmentation and Surface Reconstruction , 1999, NeuroImage.
[67] Jeffrey P. Woodard,et al. No-Reference image quality metrics for structural MRI , 2007, Neuroinformatics.
[68] Bruce Fischl,et al. Volumetric navigators for prospective motion correction and selective reacquisition in neuroanatomical MRI , 2012, Magnetic resonance in medicine.
[69] Qian Luo,et al. Automated Quality Assessment of Structural Magnetic Resonance Brain Images Based on a Supervised Machine Learning Algorithm , 2016, Front. Neuroinform..
[70] Xi-Nian Zuo,et al. A Connectome Computation System for discovery science of brain , 2015 .
[71] Timothy O. Laumann,et al. Data Quality Influences Observed Links Between Functional Connectivity and Behavior , 2017, Cerebral cortex.
[72] Mark A. Elliott,et al. The Philadelphia Neurodevelopmental Cohort: A publicly available resource for the study of normal and abnormal brain development in youth , 2016, NeuroImage.
[73] Jessica A. Turner,et al. Exploration of scanning effects in multi-site structural MRI studies , 2014, Journal of Neuroscience Methods.
[74] Brian B. Avants,et al. N4ITK: Improved N3 Bias Correction , 2010, IEEE Transactions on Medical Imaging.
[75] Efstathios D. Gennatas,et al. Age-Related Effects and Sex Differences in Gray Matter Density, Volume, Mass, and Cortical Thickness from Childhood to Young Adulthood , 2017, The Journal of Neuroscience.
[76] David Atkinson,et al. Automatic correction of motion artifacts in magnetic resonance images using an entropy focus criterion , 1997, IEEE Transactions on Medical Imaging.
[77] M. Dylan Tisdall,et al. Prospective motion correction with volumetric navigators (vNavs) reduces the bias and variance in brain morphometry induced by subject motion , 2016, NeuroImage.
[78] J. Giedd,et al. Brain development in children and adolescents: Insights from anatomical magnetic resonance imaging , 2006, Neuroscience & Biobehavioral Reviews.
[79] Mark A. Elliott,et al. Impact of in-scanner head motion on multiple measures of functional connectivity: Relevance for studies of neurodevelopment in youth , 2012, NeuroImage.
[80] Jean-Philippe Thiran,et al. Automatic quality assessment in structural brain magnetic resonance imaging , 2009, Magnetic resonance in medicine.