Effect of in‐painting on cortical thickness measurements in multiple sclerosis: A large cohort study

A comprehensive analysis of the effect of lesion in‐painting on the estimation of cortical thickness using magnetic resonance imaging was performed on a large cohort of 918 relapsing‐remitting multiple sclerosis patients who participated in a phase III multicenter clinical trial. An automatic lesion in‐painting algorithm was developed and implemented. Cortical thickness was measured using the FreeSurfer pipeline with and without in‐painting. The effect of in‐painting was evaluated using FreeSurfer's paired analysis pipeline. Multivariate regression analysis was also performed with field strength and lesion load as additional factors. Overall, the estimated cortical thickness was different with in‐painting than without. The effect of in‐painting was observed to be region dependent, more significant in the left hemisphere compared to the right, was more prominent at 1.5 T relative to 3 T, and was greater at higher lesion volumes. Our results show that even for data acquired at 1.5 T in patients with high lesion load, the mean cortical thickness difference with and without in‐painting is ∼2%. Based on these results, it appears that in‐painting has only a small effect on the estimated regional and global cortical thickness. Hum Brain Mapp 36:3749–3760, 2015. © 2015 Wiley Periodicals, Inc.

[1]  C. L. Olson.  On choosing a test statistic in multivariate analysis of variance. , 1976 .

[2]  Stephen M Smith,et al.  Fast robust automated brain extraction , 2002, Human brain mapping.

[3]  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.

[4]  Ponnada A. Narayana,et al.  Regional cortical thickness in relapsing remitting multiple sclerosis: A multi-center study☆ , 2012, NeuroImage: Clinical.

[5]  Hans-Jochen Heinze,et al.  Sailer, M. et al. Focal thinning of the cerebral cortex in multiple sclerosis. Brain 126, 1734-1744 , 2003 .

[6]  Anders M. Dale,et al.  Reliability of MRI-derived measurements of human cerebral cortical thickness: The effects of field strength, scanner upgrade and manufacturer , 2006, NeuroImage.

[7]  Alan C. Evans,et al.  Focal cortical atrophy in multiple sclerosis: Relation to lesion load and disability , 2007, NeuroImage.

[8]  M. Calabrese,et al.  Widespread cortical thinning characterizes patients with MS with mild cognitive impairment , 2010, Neurology.

[9]  Anders M. Dale,et al.  Automated manifold surgery: constructing geometrically accurate and topologically correct models of the human cerebral cortex , 2001, IEEE Transactions on Medical Imaging.

[10]  R. Turner,et al.  Optimizing T 1-weighted imaging of cortical myelin content at 3 . 0 T , 2012 .

[11]  Guillermo Sapiro,et al.  Filling-in by joint interpolation of vector fields and gray levels , 2001, IEEE Trans. Image Process..

[12]  David H. Miller,et al.  Reducing the impact of white matter lesions on automated measures of brain gray and white matter volumes , 2010, Journal of magnetic resonance imaging : JMRI.

[13]  T. Foster,et al.  A review of normal tissue hydrogen NMR relaxation times and relaxation mechanisms from 1-100 MHz: dependence on tissue type, NMR frequency, temperature, species, excision, and age. , 1984, Medical physics.

[14]  Ludwig Kappos,et al.  White matter lesion filling improves the accuracy of cortical thickness measurements in multiple sclerosis patients: a longitudinal study , 2014, BMC Neuroscience.

[15]  R Bakshi,et al.  The Impact of Lesion In-Painting and Registration Methods on Voxel-Based Morphometry in Detecting Regional Cerebral Gray Matter Atrophy in Multiple Sclerosis , 2012, American Journal of Neuroradiology.

[16]  André J. W. van der Kouwe,et al.  Reliability of MRI-derived cortical and subcortical morphometric measures: Effects of pulse sequence, voxel geometry, and parallel imaging , 2009, NeuroImage.

[17]  A. Dale,et al.  Focal thinning of the cerebral cortex in multiple sclerosis. , 2003, Brain : a journal of neurology.

[18]  Anders M. Dale,et al.  Cortical Surface-Based Analysis I. Segmentation and Surface Reconstruction , 1999, NeuroImage.

[19]  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.

[20]  Anders M. Dale,et al.  A hybrid approach to the Skull Stripping problem in MRI , 2001, NeuroImage.

[21]  D. Collins,et al.  Automatic 3D Intersubject Registration of MR Volumetric Data in Standardized Talairach Space , 1994, Journal of computer assisted tomography.

[22]  D. Ramasamy,et al.  Extent of cerebellum, subcortical and cortical atrophy in patients with MS A case-control study , 2009, Journal of the Neurological Sciences.

[23]  Ponnada A. Narayana,et al.  Segmentation and quantification of black holes in multiple sclerosis , 2006, NeuroImage.

[24]  Chester L. Olson,et al.  Comparative Robustness of Six Tests in Multivariate Analysis of Variance , 1974 .

[25]  A. Meyer-Lindenberg,et al.  Normal age-related brain morphometric changes: nonuniformity across cortical thickness, surface area and gray matter volume? , 2012, Neurobiology of Aging.

[26]  M. Sdika,et al.  Nonrigid registration of multiple sclerosis brain images using lesion inpainting for morphometry or lesion mapping , 2009, Human brain mapping.

[27]  R. He,et al.  Unified Approach for Multiple Sclerosis Lesion Segmentation on Brain MRI , 2006, Annals of Biomedical Engineering.

[28]  Ponnada A Narayana,et al.  Segmentation of gadolinium‐enhanced lesions on MRI in multiple sclerosis , 2007, Journal of magnetic resonance imaging : JMRI.

[29]  Jennifer L. Cuzzocreo,et al.  Reconstruction of the human cerebral cortex robust to white matter lesions: Method and validation , 2014, Human brain mapping.

[30]  James R. Schott,et al.  Principles of Multivariate Analysis: A User's Perspective , 2002 .

[31]  Xavier Golay,et al.  Routine clinical brain MRI sequences for use at 3.0 Tesla , 2005, Journal of magnetic resonance imaging : JMRI.

[32]  Frederik Barkhof,et al.  Intracortical lesions in multiple sclerosis: improved detection with 3D double inversion-recovery MR imaging. , 2005, Radiology.

[33]  M. Battaglini,et al.  Evaluating and reducing the impact of white matter lesions on brain volume measurements , 2012, Human brain mapping.

[34]  A. Dale,et al.  Cortical Surface-Based Analysis II: Inflation, Flattening, and a Surface-Based Coordinate System , 1999, NeuroImage.

[35]  Rohit Bakshi,et al.  The Relationship between Normal Cerebral Perfusion Patterns and White Matter Lesion Distribution in 1,249 Patients with Multiple Sclerosis , 2012, Journal of neuroimaging : official journal of the American Society of Neuroimaging.

[36]  Jerry S. Wolinsky,et al.  The CombiRx trial of combined therapy with interferon and glatiramer acetate in relapsing remitting MS: Design and baseline characteristics. , 2012, Multiple sclerosis and related disorders.

[37]  M. Calabrese,et al.  Cortical pathology in multiple sclerosis patients with epilepsy: a 3 year longitudinal study , 2011, Journal of Neurology, Neurosurgery & Psychiatry.

[38]  J. Morris,et al.  Differential effects of aging and Alzheimer's disease on medial temporal lobe cortical thickness and surface area , 2009, Neurobiology of Aging.

[39]  A. Toga,et al.  Mapping brain asymmetry , 2003, Nature Reviews Neuroscience.

[40]  B. Peterson,et al.  Sex, Age, and Cognitive Correlates of Asymmetries in Thickness of the Cortical Mantle Across the Life Span , 2014, The Journal of Neuroscience.

[41]  David J. Hand,et al.  Multivariate Analysis Of Variance And Repeated Measures , 1987 .

[42]  A. M. Dale,et al.  A hybrid approach to the skull stripping problem in MRI , 2004, NeuroImage.

[43]  Robert Turner,et al.  Optimizing T1-weighted imaging of cortical myelin content at 3.0T , 2013, NeuroImage.

[44]  Ponnada A Narayana,et al.  Intracortical lesions by 3T magnetic resonance imaging and correlation with cognitive impairment in multiple sclerosis , 2011, Multiple sclerosis.

[45]  Ponnada A. Narayana,et al.  Effect of Intrinsic and Extrinsic Factors on Global and Regional Cortical Thickness , 2014, PloS one.