Harmonization of Brain Diffusion MRI: Concepts and Methods
暂无分享,去创建一个
Jan Sijbers | Ben Jeurissen | Annemie Ribbens | Arnold J. den Dekker | Pieter-Jan Guns | Maíra Siqueira Pinto | Roberto Paolella | Thibo Billiet | Pieter Van Dyck | B. Jeurissen | P. Guns | A. Ribbens | T. Billiet | P. Van Dyck | A. D. den Dekker | Roberto Paolella | Jan Sijbers
[1] Yogesh Rathi,et al. Retrospective harmonization of multi-site diffusion MRI data acquired with different acquisition parameters , 2019, NeuroImage.
[2] Paul M. Thompson,et al. Diffusion MRI Indices and Their Relation to Cognitive Impairment in Brain Aging: The Updated Multi-protocol Approach in ADNI3 , 2018, bioRxiv.
[3] D. Schnyer,et al. Toward Precision and Reproducibility of Diffusion Tensor Imaging: A Multicenter Diffusion Phantom and Traveling Volunteer Study , 2016, American Journal of Neuroradiology.
[4] Jan Sijbers,et al. Technical Note: A safe, cheap, and easy‐to‐use isotropic diffusion MRI phantom for clinical and multicenter studies , 2017, Medical physics.
[5] D. Auer,et al. Multi-centre reproducibility of diffusion MRI parameters for clinical sequences in the brain , 2015, NMR in biomedicine.
[6] A. Baghestani,et al. How to control confounding effects by statistical analysis , 2012, Gastroenterology and hepatology from bed to bench.
[7] K. Kapur,et al. Reproducibility of Structural and Diffusion Tensor Imaging in the TACERN Multi-Center Study , 2019, Front. Integr. Neurosci..
[8] Daniel H. Mathalon,et al. Reliability of functional magnetic resonance imaging activation during working memory in a multisite study: Clarification and implications for statistical power , 2017, NeuroImage.
[9] Brian A. Nosek,et al. Power failure: why small sample size undermines the reliability of neuroscience , 2013, Nature Reviews Neuroscience.
[10] Rakesh K. Gupta,et al. Comparative evaluation of corpus callosum DTI metrics in acute mild and moderate traumatic brain injury: Its correlation with neuropsychometric tests , 2009, Brain injury.
[11] Dorit Merhof,et al. DELIMIT PyTorch - An extension for Deep Learning in Diffusion Imaging , 2018, ArXiv.
[12] William H. Hampton,et al. Predicting Advertising success beyond Traditional Measures: New Insights from Neurophysiological Methods and Market Response Modeling , 2015 .
[13] D. Smeets,et al. Potential of a statistical approach for the standardization of multicenter diffusion tensor data: A phantom study , 2019, Journal of magnetic resonance imaging : JMRI.
[14] Ting Gong,et al. Reproducibility of multi-shell diffusion tractography on traveling subjects: A multicenter study prospective. , 2019, Magnetic resonance imaging.
[15] Andrew E. Jaffe,et al. Bioinformatics Applications Note Gene Expression the Sva Package for Removing Batch Effects and Other Unwanted Variation in High-throughput Experiments , 2022 .
[16] Russell T. Shinohara,et al. Removing inter-subject technical variability in magnetic resonance imaging studies , 2016, NeuroImage.
[17] C. Jack,et al. Quantitative magnetic resonance imaging phantoms: A review and the need for a system phantom , 2018, Magnetic resonance in medicine.
[18] Xing Qiu,et al. Quantification of accuracy and precision of multi-center DTI measurements: A diffusion phantom and human brain study , 2011, NeuroImage.
[19] David M. Simcha,et al. Tackling the widespread and critical impact of batch effects in high-throughput data , 2010, Nature Reviews Genetics.
[20] Yogesh Rathi,et al. White matter abnormalities across the lifespan of schizophrenia: a harmonized multi-site diffusion MRI study , 2019, Molecular Psychiatry.
[21] Dan J Stein,et al. Widespread white matter microstructural differences in schizophrenia across 4322 individuals: results from the ENIGMA Schizophrenia DTI Working Group , 2017, Molecular Psychiatry.
[22] Carlo Pierpaoli,et al. Harmonization of methods to facilitate reproducibility in medical data processing: Applications to diffusion tensor magnetic resonance imaging , 2016, 2016 IEEE International Conference on Big Data (Big Data).
[23] Carlo Pierpaoli,et al. A framework for the analysis of phantom data in multicenter diffusion tensor imaging studies , 2013, Human brain mapping.
[24] J. Debbins,et al. A Validation Study of Multicenter Diffusion Tensor Imaging: Reliability of Fractional Anisotropy and Diffusivity Values , 2012, American Journal of Neuroradiology.
[25] Arno Klein,et al. A reproducible evaluation of ANTs similarity metric performance in brain image registration , 2011, NeuroImage.
[26] Arthur W. Toga,et al. Multi-site study of additive genetic effects on fractional anisotropy of cerebral white matter: Comparing meta and megaanalytical approaches for data pooling , 2014, NeuroImage.
[27] Maxime Descoteaux,et al. Non Local Spatial and Angular Matching : Enabling higher spatial resolution diffusion MRI datasets through adaptive denoising , 2016, Medical Image Anal..
[28] John S. Duncan,et al. Identical, but not the same: Intra-site and inter-site reproducibility of fractional anisotropy measures on two 3.0 T scanners , 2010, NeuroImage.
[29] Daniel Cremers,et al. q-Space Deep Learning: Twelve-Fold Shorter and Model-Free Diffusion MRI Scans , 2016, IEEE Transactions on Medical Imaging.
[30] Thomas E. Nichols,et al. Statistical Challenges in “Big Data” Human Neuroimaging , 2018, Neuron.
[31] Mark W. Woolrich,et al. Multilevel linear modelling for FMRI group analysis using Bayesian inference , 2004, NeuroImage.
[32] Antonio Criminisi,et al. Bayesian Image Quality Transfer with CNNs: Exploring Uncertainty in dMRI Super-Resolution , 2017, MICCAI.
[33] Pew-Thian Yap,et al. Multi-Site Harmonization of Diffusion MRI Data via Method of Moments , 2019, IEEE Transactions on Medical Imaging.
[34] Peter Savadjiev,et al. Inter-site and inter-scanner diffusion MRI data harmonization , 2016, NeuroImage.
[35] Peter Savadjiev,et al. Multi-site harmonization of diffusion MRI data in a registration framework , 2017, Brain Imaging and Behavior.
[36] Theo G. M. van Erp,et al. Reliability of functional magnetic resonance imaging activation during working memory in a multi-site study: Analysis from the North American Prodrome Longitudinal Study , 2014, NeuroImage.
[37] Stefan Klein,et al. Fast parallel image registration on CPU and GPU for diagnostic classification of Alzheimer's disease , 2013, Front. Neuroinform..
[38] Derek K. Jones,et al. Precision and Accuracy in Diffusion Tensor Magnetic Resonance Imaging , 2010, Topics in magnetic resonance imaging : TMRI.
[39] Paul M. Thompson,et al. Challenges and Opportunities in dMRI Data Harmonization , 2019, Computational Diffusion MRI.
[40] Diana B. Petitti,et al. Meta-Analysis, Decision Analysis, and Cost-Effectiveness Analysis: Methods for Quantitative Synthesis in Medicine , 1994 .
[41] Magda Tsolaki,et al. Multisite longitudinal reliability of tract-based spatial statistics in diffusion tensor imaging of healthy elderly subjects , 2014, NeuroImage.
[42] Josien P. W. Pluim,et al. Muti-shell Diffusion MRI Harmonisation and Enhancement Challenge (MUSHAC): Progress and Results , 2019, Computational Diffusion MRI.
[43] Stephen M. Smith,et al. General multilevel linear modeling for group analysis in FMRI , 2003, NeuroImage.
[44] Peter Savadjiev,et al. Harmonizing Diffusion MRI Data Across Multiple Sites and Scanners , 2015, MICCAI.
[45] F. Barkhof,et al. Harmonization of neuroimaging biomarkers for neurodegenerative diseases: A survey in the imaging community of perceived barriers and suggested actions , 2018, Alzheimer's & dementia.
[46] Stephen M. Smith,et al. Meta-analysis of neuroimaging data: A comparison of image-based and coordinate-based pooling of studies , 2009, NeuroImage.
[47] Luke Bloy,et al. Spherical Harmonic Residual Network for Diffusion Signal Harmonization , 2018, Computational Diffusion MRI.
[48] Tufve Nyholm,et al. Variability in prostate and seminal vesicle delineations defined on magnetic resonance images, a multi-observer, -center and -sequence study , 2013, Radiation oncology.
[49] Jelle Veraart,et al. Cross-scanner and cross-protocol diffusion MRI data harmonisation: A benchmark database and evaluation of algorithms , 2019, NeuroImage.
[50] Dorit Merhof,et al. Diffusion MRI Signal Augmentation: From Single Shell to Multi Shell with Deep Learning , 2016, MICCAI 2016.
[51] Sylvain Paile,et al. WHAT TO CONTROL , 2013 .
[52] Richard Frayne,et al. Reliability of neuroanatomical measurements in a multisite longitudinal study of youth at risk for psychosis , 2014, Human brain mapping.
[53] S Ekholm,et al. Effects of number of diffusion gradient directions on derived diffusion tensor imaging indices in human brain. , 2006, AJNR. American journal of neuroradiology.
[54] Ninon Burgos,et al. New advances in the Clinica software platform for clinical neuroimaging studies , 2019 .
[55] Guillermo Sapiro,et al. Online Learning for Matrix Factorization and Sparse Coding , 2009, J. Mach. Learn. Res..
[56] Max A. Viergever,et al. elastix: A Toolbox for Intensity-Based Medical Image Registration , 2010, IEEE Transactions on Medical Imaging.
[57] A. Pfefferbaum,et al. Replicability of diffusion tensor imaging measurements of fractional anisotropy and trace in brain , 2003, Journal of magnetic resonance imaging : JMRI.
[58] Kilian M. Pohl,et al. Harmonizing DTI measurements across scanners to examine the development of white matter microstructure in 803 adolescents of the NCANDA study , 2016, NeuroImage.
[59] Ragini Verma,et al. Harmonization of multi-site diffusion tensor imaging data , 2017, NeuroImage.
[60] A. Saykin,et al. Stability of MRI metrics in the advanced research core of the NCAA-DoD concussion assessment, research and education (CARE) consortium , 2017, Brain Imaging and Behavior.
[61] Nick C Fox,et al. Longitudinal Diffusion Tensor Imaging in Frontotemporal Dementia , 2014, Annals of neurology.
[62] Yu-Chien Wu,et al. Comparison of diffusion tensor imaging measurements at 3.0 T versus 1.5 T with and without parallel imaging. , 2006, Neuroimaging clinics of North America.
[63] Cheng Li,et al. Adjusting batch effects in microarray expression data using empirical Bayes methods. , 2007, Biostatistics.
[64] Stefan Klöppel,et al. Anatomical MRI and DTI in the diagnosis of Alzheimer's disease: a European multicenter study. , 2012, Journal of Alzheimer's disease : JAD.
[65] Paul M. Thompson,et al. Multi-site genetic analysis of diffusion images and voxelwise heritability analysis: A pilot project of the ENIGMA–DTI working group , 2013, NeuroImage.
[66] Jian Sun,et al. Deep Residual Learning for Image Recognition , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[67] Stefan Klöppel,et al. Multicenter stability of diffusion tensor imaging measures: A European clinical and physical phantom study , 2011, Psychiatry Research: Neuroimaging.
[68] A. Worsley. Nutrition knowledge and food consumption: can nutrition knowledge change food behaviour? , 2002, Asia Pacific journal of clinical nutrition.