title: Tissue-based MRI intensity standardization: application to multicentric datasets.
abstract: Intensity standardization in MRI aims at correcting scanner-dependent intensity variations. Existing simple and robust techniques aim at matching the input image histogram onto a standard, while we think that standardization should aim at matching spatially corresponding tissue intensities. In this study, we present a novel automatic technique, called STI for STandardization of Intensities, which not only shares the simplicity and robustness of histogram-matching techniques, but also incorporates tissue spatial intensity information. STI uses joint intensity histograms to determine intensity correspondence in each tissue between the input and standard images. We compared STI to an existing histogram-matching technique on two multicentric datasets, Pilot E-ADNI and ADNI, by measuring the intensity error with respect to the standard image after performing nonlinear registration. The Pilot E-ADNI dataset consisted in 3 subjects each scanned in 7 different sites. The ADNI dataset consisted in 795 subjects scanned in more than 50 different sites. STI was superior to the histogram-matching technique, showing significantly better intensity matching for the brain white matter with respect to the standard image.
authors.affiliation: Centre de Recherche de l'Institut Universitaire en Santé Mentale de Québec, 2601 Chemin de la Canardière, Québec, QC, Canada G1J 2G3.
authors.forename: Nicolas
authors.initials: N
authors.lastname: Robitaille
authors.forename: Abderazzak
authors.initials: A
authors.lastname: Mouiha
authors.forename: Burt
authors.initials: B
authors.lastname: Crépeault
authors.forename: Fernando
authors.initials: F
authors.lastname: Valdivia
authors.forename: Simon
authors.initials: S
authors.lastname: Duchesne