Whole Brain Volume Measured from 1.5T versus 3T MRI in Healthy Subjects and Patients with Multiple Sclerosis
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Rohit Bakshi | Brian C. Healy | Shahamat Tauhid | Renxin Chu | Fariha Khalid | Mohit Neema | R. Chu | R. Bakshi | B. Healy | M. Neema | B. Glanz | S. Tauhid | Gloria Kim | F. Khalid | Vinit V. Oommen | Gloria Kim | Bonnie I. Glanz | Mohit Neema
[1] Jeffrey A. Cohen,et al. Diagnostic criteria for multiple sclerosis: 2010 Revisions to the McDonald criteria , 2011, Annals of neurology.
[2] R Bakshi,et al. Normal Findings on Brain Fluid-Attenuated Inversion Recovery MR Images at 3T , 2009, American Journal of Neuroradiology.
[3] Rohit Bakshi,et al. Brain MRI Lesion Load at 1.5T and 3T versus Clinical Status in Multiple Sclerosis , 2011, Journal of neuroimaging : official journal of the American Society of Neuroimaging.
[4] R. Benedict,et al. Validity of the minimal assessment of cognitive function in multiple sclerosis (MACFIMS) , 2006, Journal of the International Neuropsychological Society.
[5] P. Matthews,et al. Normalized Accurate Measurement of Longitudinal Brain Change , 2001, Journal of computer assisted tomography.
[6] D. Alsop,et al. Spinal cord lesions and clinical status in multiple sclerosis: A 1.5 T and 3 T MRI study , 2009, Journal of the Neurological Sciences.
[7] Jacob Cohen. Statistical Power Analysis for the Behavioral Sciences , 1969, The SAGE Encyclopedia of Research Design.
[8] C P Langlotz,et al. MR identification of white matter abnormalities in multiple sclerosis: a comparison between 1.5 T and 4 T. , 1998, AJNR. American journal of neuroradiology.
[9] Mike P. Wattjes,et al. Higher sensitivity in the detection of inflammatory brain lesions in patients with clinically isolated syndromes suggestive of multiple sclerosis using high field MRI: an intraindividual comparison of 1.5 T with 3.0 T , 2006, European Radiology.
[10] Stephen M. Smith,et al. A global optimisation method for robust affine registration of brain images , 2001, Medical Image Anal..
[11] A. Goldszal,et al. Estimating cerebral atrophy in multiple sclerosis patients from various MR pulse sequences , 2002, Multiple sclerosis.
[12] A. Thompson,et al. Method for simultaneous voxel-based morphometry of the brain and cervical spinal cord area measurements using 3D-MDEFT , 2010, Journal of magnetic resonance imaging : JMRI.
[13] J. Kurtzke. Rating neurologic impairment in multiple sclerosis , 1983, Neurology.
[14] Anders M. Dale,et al. MRI-derived measurements of human subcortical, ventricular and intracranial brain volumes: Reliability effects of scan sessions, acquisition sequences, data analyses, scanner upgrade, scanner vendors and field strengths , 2009, NeuroImage.
[15] C. Poser,et al. Diagnostic criteria for multiple sclerosis , 2001, Clinical Neurology and Neurosurgery.
[16] Ralph H B Benedict,et al. Minimal Neuropsychological Assessment of MS Patients: A Consensus Approach , 2002, The Clinical neuropsychologist.
[17] 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.
[18] Stephen M. Smith,et al. Accurate, Robust, and Automated Longitudinal and Cross-Sectional Brain Change Analysis , 2002, NeuroImage.
[19] Seth E. Bouvier,et al. Comparison of Multiple Sclerosis Lesions at 1.5 and 3.0 Tesla , 2003, Investigative radiology.
[20] P. Lachenbruch. Statistical Power Analysis for the Behavioral Sciences (2nd ed.) , 1989 .
[21] R Turner,et al. Optimisation of the 3D MDEFT sequence for anatomical brain imaging: technical implications at 1.5 and 3 T , 2004, NeuroImage.
[22] D Louis Collins,et al. Regional impact of field strength on voxel‐based morphometry results , 2009, Human brain mapping.
[23] S. Reingold,et al. The Multiple Sclerosis Functional Composite measure (MSFC): an integrated approach to MS clinical outcome assessment , 1999, Multiple sclerosis.
[24] C. Crainiceanu,et al. Quantification of multiple-sclerosis-related brain atrophy in two heterogeneous MRI datasets using mixed-effects modeling☆ , 2013, NeuroImage: Clinical.
[25] L. Radloff. The CES-D Scale , 1977 .
[26] Michael Brady,et al. Improved Optimization for the Robust and Accurate Linear Registration and Motion Correction of Brain Images , 2002, NeuroImage.
[27] S. Reingold,et al. Diagnostic criteria for multiple sclerosis: 2005 revisions to the “McDonald Criteria” , 2005, Annals of neurology.
[28] C. Jack,et al. Comparing 3T and 1.5T MRI for Mapping Hippocampal Atrophy in the Alzheimer's Disease Neuroimaging Initiative , 2015, American Journal of Neuroradiology.
[29] Stephen M Smith,et al. Fast robust automated brain extraction , 2002, Human brain mapping.
[30] C. Kuhl,et al. Does high-field MR imaging have an influence on the classification of patients with clinically isolated syndromes according to current diagnostic mr imaging criteria for multiple sclerosis? , 2006, AJNR. American journal of neuroradiology.
[31] R. Bakshi,et al. Measurement of Brain and Spinal Cord Atrophy by Magnetic Resonance Imaging as a Tool to Monitor Multiple Sclerosis , 2005, Journal of neuroimaging : official journal of the American Society of Neuroimaging.
[32] Richard Nicholas,et al. Effect of high-dose simvastatin on brain atrophy and disability in secondary progressive multiple sclerosis (MS-STAT): a randomised, placebo-controlled, phase 2 trial , 2014, The Lancet.
[33] D. Louis Collins,et al. Sensitivity of voxel-based morphometry analysis to choice of imaging protocol at 3 T , 2009, NeuroImage.
[34] Mark W. Woolrich,et al. Advances in functional and structural MR image analysis and implementation as FSL , 2004, NeuroImage.
[35] Rohit Bakshi,et al. Quantification of Global Cerebral Atrophy in Multiple Sclerosis from 3T MRI Using SPM: The Role of Misclassification Errors , 2014, Journal of neuroimaging : official journal of the American Society of Neuroimaging.