Authors

Amanda Mejia, Department of Biostatistics, Bloomberg School of Public Health, Johns Hopkins UniversityFollow
Elizabeth M. Sweeney, Department of Biostatistics, Bloomberg School of Public Health, Johns Hopkins University; Translational Neuroradiology Unit, Division of Neuroimmunology and Neurovirology, National Institute of Neurological Disorders and Stroke, National Institutes of HealthFollow
Blake Dewey, Translational Neuroradiology Unit, Division of Neuroimmunology and Neurovirology, National Institute of Neurological Disorders and Stroke, National Institutes of Health
Govind Nair, Translational Neuroradiology Unit, Division of Neuroimmunology and Neurovirology, National Institute of Neurological Disorders and Stroke, National Institutes of Health
Pascal Sati, Translational Neuroradiology Unit, Division of Neuroimmunology and Neurovirology, National Institute of Neurological Disorders and Stroke, National Institutes of Health
Colin Shea, Translational Neuroradiology Unit, Division of Neuroimmunology and Neurovirology, National Institute of Neurological Disorders and Stroke, National Institutes of Health
Daniel S. Reich, Translational Neuroradiology Unit, Division of Neuroimmunology and Neurovirology, National Institute of Neurological Disorders and Stroke, National Institutes of Health; Department of Biostatistics, Bloomberg School of Public Health, Johns Hopkins UniversityFollow
Russell T. Shinohara, Department of Biostatistics and Epidemiology, Perelman School of Medicine, University of PennsylvaniaFollow

Abstract

Quantitative T1 maps estimate T1 relaxation times and can be used to assess diffuse tissue abnormalities within normal-appearing tissue. T1 maps are popular for studying the progression and treatment of multiple sclerosis (MS). However, their inclusion in standard imaging protocols remains limited due to the additional scanning time and expert calibration required and susceptibility to bias and noise. Here, we propose a new method of estimating T1 maps using four conventional MR images, which are intensity- normalized using cerebellar gray matter as a reference tissue and related to T1 using a smooth regression model. Using leave-one-out cross-validation, we generate statistical T1 maps for 61 subjects with MS. The statistical maps are less noisy than the acquired maps and show similar accuracy. Tests of group differences in normal-appearing white matter across MS subtypes give similar results using both methods, but tests performed using statistical maps are more powerful.

Disciplines

Biostatistics

Previous Versions

Mar 10 2015

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