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Neuroimage Clin. 2014 Aug 15;6:9-19. doi: 10.1016/j.nicl.2014.08.008. eCollection 2014.

Statistical normalization techniques for magnetic resonance imaging.

Author information

1
Department of Biostatistics and Epidemiology, University of Pennsylvania, Philadelphia, PA 19104, United States.
2
Translational Neurology Unit, Neuroimmunology Branch, National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, MD 20892, United States ; Department of Biostatistics, Johns Hopkins University, Baltimore, MD 21205, United States.
3
Department of Biostatistics, Columbia University, New York, NY 10032, United States.
4
Center for Neuroscience and Regenerative Medicine, Henry M. Jackson Foundation, Bethesda, MD 20892, United States.
5
Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, MA 02114, United States.
6
Department of Neurology, The Johns Hopkins University School of Medicine, Baltimore, MD 21287, United States.
7
Department of Radiology, The Johns Hopkins University School of Medicine, Baltimore, MD 21287, United States.
8
Translational Neurology Unit, Neuroimmunology Branch, National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, MD 20892, United States ; Department of Biostatistics, Johns Hopkins University, Baltimore, MD 21205, United States ; Department of Neurology, The Johns Hopkins University School of Medicine, Baltimore, MD 21287, United States ; Department of Radiology, The Johns Hopkins University School of Medicine, Baltimore, MD 21287, United States.

Abstract

While computed tomography and other imaging techniques are measured in absolute units with physical meaning, magnetic resonance images are expressed in arbitrary units that are difficult to interpret and differ between study visits and subjects. Much work in the image processing literature on intensity normalization has focused on histogram matching and other histogram mapping techniques, with little emphasis on normalizing images to have biologically interpretable units. Furthermore, there are no formalized principles or goals for the crucial comparability of image intensities within and across subjects. To address this, we propose a set of criteria necessary for the normalization of images. We further propose simple and robust biologically motivated normalization techniques for multisequence brain imaging that have the same interpretation across acquisitions and satisfy the proposed criteria. We compare the performance of different normalization methods in thousands of images of patients with Alzheimer's disease, hundreds of patients with multiple sclerosis, and hundreds of healthy subjects obtained in several different studies at dozens of imaging centers.

KEYWORDS:

Image analysis; Magnetic resonance imaging; Normalization; Statistics

PMID:
25379412
PMCID:
PMC4215426
DOI:
10.1016/j.nicl.2014.08.008
[Indexed for MEDLINE]
Free PMC Article

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