title: Numerical tissue characterization in MS via standardization of the MR image intensity scale.
abstract: Image intensity standardization is a recently developed postprocessing method that is capable of correcting the signal intensity variations in MR images. We evaluated signal intensity of healthy and diseased tissues in 10 multiple sclerosis (MS) patients based on standardized dual fast spin-echo MR images using a numerical postprocessing technique. The main idea of this technique is to deform the volume image histogram of each study to match a standard histogram and to utilize the resulting transformation to map the image intensities into standard scale. Upon standardization, the coefficients of variation of signal intensities for each segmented tissue (gray matter, white matter, lesion plaques, and diffuse abnormal white matter) in all patients were significantly smaller (2.3-9.2 times) than in the original images, and the same tissues from different patients looked alike, with similar intensity characteristics. Numerical tissue characterizability of different tissues in MS achieved by standardization offers a fixed tissue-specific meaning for the numerical values and can significantly facilitate image segmentation and analysis.
authors.affiliation: Department of Radiology, Hospital of the University of Pennsylvania, Philadelphia, Pennsylvania 19104-6021, USA.
authors.forename: Y
authors.initials: Y
authors.lastname: Ge
authors.forename: J K
authors.initials: JK
authors.lastname: Udupa
authors.forename: L G
authors.initials: LG
authors.lastname: Nyúl
authors.forename: L
authors.initials: L
authors.lastname: Wei
authors.forename: R I
authors.initials: RI
authors.lastname: Grossman
mesh_terms: D000328:Adult; D005260:Female; D006801:Humans; D007089:Image Enhancement / Q000379:methods*; D008279:Magnetic Resonance Imaging / Q000379:methods / Q000592:standards*; D008297:Male; D009103:Multiple Sclerosis / Q000175:diagnosis*; D012016:Reference Values; D012680:Sensitivity and Specificity; D012720:Severity of Illness Index