The Impact of the Siemens Trio to Prisma Upgrade and Volumetric Navigators on MRI Indices: A Reliability Study with Implications for Longitudinal Study Designs
暂无分享,去创建一个
Christine L. Tardif | Jamie Near | Gabriel A. Devenyi | M. Natasha Rajah | Raihaan Patel | Stephanie Tullo | M. Mallar Chakravarty | Eric Plitman | Aurelie Bussy | Vanessa Valiquette | Alyssa Salaciak | Marie-Lise Béland | Christine Tardif | M. Chakravarty | C. Tardif | M. Rajah | J. Near | Aurélie Bussy | Raihaan Patel | E. Plitman | S. Tullo | Alyssa Salaciak | Marie-Lise Béland | Vanessa Valiquette
[1] S. Provencher. Automatic quantitation of localized in vivo 1H spectra with LCModel , 2001, NMR in biomedicine.
[2] M. Mallar Chakravarty,et al. Manual segmentation of the fornix, fimbria, and alveus on high-resolution 3T MRI: Application via fully-automated mapping of the human memory circuit white and grey matter in healthy and pathological aging , 2016, NeuroImage.
[3] R. Gruetter,et al. In vivo 1H NMR spectroscopy of rat brain at 1 ms echo time , 1999, Magnetic resonance in medicine.
[4] M. Mallar Chakravarty,et al. Evaluating accuracy of striatal, pallidal, and thalamic segmentation methods: Comparing automated approaches to manual delineation , 2017, NeuroImage.
[5] Rolf Gruetter,et al. MR spectroscopy of the human brain with enhanced signal intensity at ultrashort echo times on a clinical platform at 3T and 7T , 2009, Magnetic resonance in medicine.
[6] M. Mallar Chakravarty,et al. Multi-atlas segmentation of the whole hippocampus and subfields using multiple automatically generated templates , 2014, NeuroImage.
[7] C. Jack,et al. Tracking pathophysiological processes in Alzheimer's disease: an updated hypothetical model of dynamic biomarkers , 2013, The Lancet Neurology.
[8] Brian R. Tietz,et al. Deciding Which Way to Go: How Do Insects Alter Movements to Negotiate Barriers? , 2012, Front. Neurosci..
[9] D Louis Collins,et al. Warping an atlas derived from serial histology to 5 high-resolution MRIs , 2018, Scientific Data.
[10] Tyrone D. Cannon,et al. Progressive Reduction in Cortical Thickness as Psychosis Develops: A Multisite Longitudinal Neuroimaging Study of Youth at Elevated Clinical Risk , 2015, Biological Psychiatry.
[11] D. Louis Collins,et al. An Object-Based Method for Rician Noise Estimation in MR Images , 2009, MICCAI.
[12] Alan C. Evans,et al. Cortical thickness analysis examined through power analysis and a population simulation , 2005, NeuroImage.
[13] M. Mallar Chakravarty,et al. Normative brain size variation and brain shape diversity in humans , 2018, Science.
[14] Anders M. Dale,et al. The Adolescent Brain Cognitive Development (ABCD) study: Imaging acquisition across 21 sites , 2018, Developmental Cognitive Neuroscience.
[15] Brian B. Avants,et al. N4ITK: Improved N3 Bias Correction , 2010, IEEE Transactions on Medical Imaging.
[16] Jon Pipitone,et al. Hippocampal (subfield) volume and shape in relation to cognitive performance across the adult lifespan , 2015, Human brain mapping.
[17] J. Giedd,et al. Subtle in‐scanner motion biases automated measurement of brain anatomy from in vivo MRI , 2016, Human brain mapping.
[18] D. Selkoe. Alzheimer's disease. , 2011, Cold Spring Harbor perspectives in biology.
[19] D. Collins,et al. Automatic 3D Intersubject Registration of MR Volumetric Data in Standardized Talairach Space , 1994, Journal of computer assisted tomography.
[20] Robin A. de Graaf,et al. In Vivo NMR Spectroscopy , 2019 .
[21] Esben Thade Petersen,et al. Improvement in diagnostic quality of structural and angiographic MRI of the brain using motion correction with interleaved, volumetric navigators , 2019, PloS one.
[22] M. Mallar Chakravarty,et al. Illness Progression, Recent Stress, and Morphometry of Hippocampal Subfields and Medial Prefrontal Cortex in Major Depression , 2014, Biological Psychiatry.
[23] S. A. Wijtenburg,et al. In vivo assessment of neurotransmitters and modulators with magnetic resonance spectroscopy: Application to schizophrenia , 2015, Neuroscience & Biobehavioral Reviews.
[24] Arno Klein,et al. 101 Labeled Brain Images and a Consistent Human Cortical Labeling Protocol , 2012, Front. Neurosci..
[25] Hyunwoo Lee,et al. Estimating and accounting for the effect of MRI scanner changes on longitudinal whole-brain volume change measurements , 2019, NeuroImage.
[26] Heath R. Pardoe,et al. Motion and morphometry in clinical and nonclinical populations , 2016, NeuroImage.
[27] D. Collins,et al. The creation of a brain atlas for image guided neurosurgery using serial histological data , 2003, NeuroImage.
[28] Margot J. Taylor,et al. Large-scale analyses of the relationship between sex, age and intelligence quotient heterogeneity and cortical morphometry in autism spectrum disorder , 2019, Molecular Psychiatry.
[29] R. Hoge,et al. Measurement Variability Following MRI System Upgrade , 2019, Front. Neurol..
[30] A. Dale,et al. Cortical Surface-Based Analysis II: Inflation, Flattening, and a Surface-Based Coordinate System , 1999, NeuroImage.
[31] Alan C. Evans,et al. Automated 3-D extraction and evaluation of the inner and outer cortical surfaces using a Laplacian map and partial volume effect classification , 2005, NeuroImage.
[32] Alan C. Evans,et al. Automatic "pipeline" analysis of 3-D MRI data for clinical trials: application to multiple sclerosis , 2002, IEEE Transactions on Medical Imaging.
[33] D. Collins,et al. Performing label‐fusion‐based segmentation using multiple automatically generated templates , 2013, Human brain mapping.
[34] R Gruetter,et al. Field mapping without reference scan using asymmetric echo‐planar techniques , 2000, Magnetic resonance in medicine.
[35] Naoto Hayashi,et al. Effects of the use of multiple scanners and of scanner upgrade in longitudinal voxel‐based morphometry studies , 2013, Journal of magnetic resonance imaging : JMRI.
[36] Brian B. Avants,et al. Symmetric diffeomorphic image registration with cross-correlation: Evaluating automated labeling of elderly and neurodegenerative brain , 2008, Medical Image Anal..
[37] Lei Ai,et al. Is it time to switch your T1W sequence? Assessing the impact of prospective motion correction on the reliability and quality of structural imaging , 2019, NeuroImage.
[38] D. Cicchetti. Guidelines, Criteria, and Rules of Thumb for Evaluating Normed and Standardized Assessment Instruments in Psychology. , 1994 .
[39] Stine K. Krogsrud,et al. Development and aging of cortical thickness correspond to genetic organization patterns , 2015, Proceedings of the National Academy of Sciences.
[40] R. Woods,et al. Sex differences in cortical thickness mapped in 176 healthy individuals between 7 and 87 years of age. , 2007, Cerebral cortex.
[41] R. Berman,et al. Longitudinal four-dimensional mapping of subcortical anatomy in human development , 2014, Proceedings of the National Academy of Sciences.
[42] Sabina Sonia Tangaro,et al. Integrating longitudinal information in hippocampal volume measurements for the early detection of Alzheimer's disease , 2016, NeuroImage.
[43] Borys Shuter,et al. Reproducibility of brain tissue volumes in longitudinal studies: Effects of changes in signal-to-noise ratio and scanner software , 2008, NeuroImage.
[44] M. Dylan Tisdall,et al. Head motion during MRI acquisition reduces gray matter volume and thickness estimates , 2015, NeuroImage.
[45] 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.
[46] Jennifer Fedor,et al. Cortical and subcortical brain morphometry differences between patients with autism spectrum disorders (ASD) and healthy individuals across the lifespan: results from the ENIGMA-ASD working group , 2017 .
[47] Peter Jezzard,et al. Advanced processing and simulation of MRS data using the FID appliance (FID‐A)—An open source, MATLAB‐based toolkit , 2017, Magnetic resonance in medicine.
[48] Bruce Fischl,et al. Volumetric navigators for prospective motion correction and selective reacquisition in neuroanatomical MRI , 2012, Magnetic resonance in medicine.
[49] Karolinska Schizophrenia. Cortical brain abnormalities in 4474 individuals with schizophrenia and 5098 control subjects via the Enhancing Neuro Imaging Genetics Through Meta Analysis (ENIGMA) Consortium , 2018 .
[50] Anders M. Dale,et al. Cortical Surface-Based Analysis I. Segmentation and Surface Reconstruction , 1999, NeuroImage.
[51] A. Dale,et al. Whole Brain Segmentation Automated Labeling of Neuroanatomical Structures in the Human Brain , 2002, Neuron.
[52] Adrian Preda,et al. Cortical Brain Abnormalities in 4474 Individuals With Schizophrenia and 5098 Control Subjects via the Enhancing Neuro Imaging Genetics Through Meta Analysis (ENIGMA) Consortium , 2018, Biological Psychiatry.
[53] Lachlan T. Strike,et al. Subcortical brain alterations in major depressive disorder: findings from the ENIGMA Major Depressive Disorder working group , 2015, Molecular Psychiatry.
[54] M. Mallar Chakravarty,et al. A novel in vivo atlas of human hippocampal subfields using high-resolution 3T magnetic resonance imaging , 2013, NeuroImage.
[55] Christine L. Tardif,et al. MR‐based age‐related effects on the striatum, globus pallidus, and thalamus in healthy individuals across the adult lifespan , 2019, Human brain mapping.
[56] I. Melle,et al. Erratum: Subcortical brain volume abnormalities in 2028 individuals with schizophrenia and 2540 healthy controls via the ENIGMA consortium (Molecular Psychiatry (2015) DOI:10.1038/mp.2015.63) , 2016 .
[57] Alan C. Evans,et al. Changes in cortical thickness during the course of illness in schizophrenia. , 2011, Archives of general psychiatry.