Relating multi-sequence longitudinal intensity profiles and clinical covariates in incident multiple sclerosis lesions

[1]  D. Arnold,et al.  Quantitative Measurement of tissue damage and recovery within new T2w lesions in pediatric- and adult-onset multiple sclerosis , 2015, Multiple sclerosis.

[2]  D. Reich,et al.  Sample-size calculations for short-term proof-of-concept studies of tissue protection and repair in multiple sclerosis lesions via conventional clinical imaging , 2015, Multiple sclerosis.

[3]  C. Crainiceanu,et al.  Statistical normalization techniques for magnetic resonance imaging , 2014, NeuroImage: Clinical.

[4]  D. Barr,et al.  Random effects structure for confirmatory hypothesis testing: Keep it maximal. , 2013, Journal of memory and language.

[5]  Peter A. Calabresi,et al.  OASIS is Automated Statistical Inference for Segmentation, with applications to multiple sclerosis lesion segmentation in MRI☆ , 2013, NeuroImage: Clinical.

[6]  C M Crainiceanu,et al.  Automatic Lesion Incidence Estimation and Detection in Multiple Sclerosis Using Multisequence Longitudinal MRI , 2013, American Journal of Neuroradiology.

[7]  Volker Schmid,et al.  Working with the DICOM and NIfTI Data Standards in R , 2011 .

[8]  Volker Schmid,et al.  Quantitative Analysis of Dynamic Contrast-Enhanced and Diffusion-Weighted Magnetic Resonance Imaging for Oncology in R , 2011 .

[9]  Russell T. Shinohara,et al.  Population-wide principal component-based quantification of blood–brain-barrier dynamics in multiple sclerosis , 2011, NeuroImage.

[10]  Jeffrey A. Cohen,et al.  Diagnostic criteria for multiple sclerosis: 2010 Revisions to the McDonald criteria , 2011, Annals of neurology.

[11]  Aaron Carass,et al.  Erratum to: The Java Image Science Toolkit (JIST) for Rapid Prototyping and Publishing of Neuroimaging Software , 2010, Neuroinformatics.

[12]  Peter A. Calabresi,et al.  A topology-preserving approach to the segmentation of brain images with multiple sclerosis lesions , 2010, NeuroImage.

[13]  C. Almli,et al.  Unbiased nonlinear average age-appropriate brain templates from birth to adulthood , 2009, NeuroImage.

[14]  Dominik S. Meier,et al.  Time-series modeling of multiple sclerosis disease activity: A promising window on disease progression and repair potential? , 2007, Neurotherapeutics.

[15]  Aaron Carass,et al.  A JOINT REGISTRATION AND SEGMENTATION APPROACH TO SKULL STRIPPING , 2007, 2007 4th IEEE International Symposium on Biomedical Imaging: From Nano to Macro.

[16]  Dominik S. Meier,et al.  MRI time series modeling of MS lesion development , 2006, NeuroImage.

[17]  Simon K Warfield,et al.  Diffusion Tensor Magnetic Resonance Imaging in Multiple Sclerosis , 2005, Journal of neuroimaging : official journal of the American Society of Neuroimaging.

[18]  Charles E. McCulloch,et al.  Generalized Linear Mixed Models , 2005 .

[19]  Dominik S. Meier,et al.  Time-series analysis of MRI intensity patterns in multiple sclerosis , 2003, NeuroImage.

[20]  F. Barkhof.  The clinico‐radiological paradox in multiple sclerosis revisited , 2002, Current opinion in neurology.

[21]  F Barkhof,et al.  The role of MRI as a surrogate outcome measure in multiple sclerosis , 2002, Multiple sclerosis.

[22]  M Cercignani,et al.  A quantitative study of water diffusion in multiple sclerosis lesions and normal-appearing white matter using echo-planar imaging. , 2000, Archives of neurology.

[23]  F. Barkhof,et al.  Axonal loss in multiple sclerosis lesions: Magnetic resonance imaging insights into substrates of disability , 1999, Annals of neurology.

[24]  Jianqing Fan,et al.  Two‐step estimation of functional linear models with applications to longitudinal data , 1999 .

[25]  R. Tibshirani,et al.  An Introduction to the Bootstrap , 1995 .

[26]  R Core Team,et al.  R: A language and environment for statistical computing. , 2014 .

[27]  Ciprian M. Crainiceanu,et al.  refund: Regression with Functional Data , 2013 .

[28]  M. Kenward,et al.  An Introduction to the Bootstrap , 2007 .

[29]  NeuroImage: Clinical , 2022 .