Statistical normalization techniques for magnetic resonance imaging

• Published in: NeuroImage clinical Vol. 6; no. C; pp. 9 - 19
• Main Authors: Shinohara, Russell T., Sweeney, Elizabeth M., Goldsmith, Jeff, Shiee, Navid, Mateen, Farrah J., Calabresi, Peter A., Jarso, Samson, Pham, Dzung L., Reich, Daniel S., Crainiceanu, Ciprian M.
• Format: Journal Article
• Language: English
• Published: Netherlands Elsevier Inc 01.01.2014; Elsevier
• Subjects: Aged; Aged, 80 and over; Alzheimer Disease - diagnosis; Female; Humans; Image analysis; Image Processing, Computer-Assisted - standards; Image Processing, Computer-Assisted - statistics & numerical data; Magnetic resonance imaging; Magnetic Resonance Imaging - standards; Magnetic Resonance Imaging - statistics & numerical data; Male; Middle Aged; Multiple Sclerosis - diagnosis; Normalization; Radiology; Statistics; Image analysis; Normalization; Magnetic resonance imaging; Statistics
• ISSN: 2213-1582; 2213-1582
• DOI: 10.1016/j.nicl.2014.08.008

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. •Formalize the necessity and goals of statistical intensity normalization•Novel approach for intensity normalization of brain MRI without prior segmentation•Extend the novel approach for multimodality imaging•Propose new quantitative metric for intensity normalization in a population•Evaluate normalization techniques in large multicenter studies