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Robust Intensity Standardization in Brain Magnetic Resonance Images

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Abstract

The paper is focused on a tiSsue-Based Standardization Technique (SBST) of magnetic resonance (MR) brain images. Magnetic Resonance Imaging intensities have no fixed tissue-specific numeric meaning, even within the same MRI protocol, for the same body region, or even for images of the same patient obtained on the same scanner in different moments. This affects postprocessing tasks such as automatic segmentation or unsupervised/supervised classification methods, which strictly depend on the observed image intensities, compromising the accuracy and efficiency of many image analyses algorithms. A large number of MR images from public databases, belonging to healthy people and to patients with different degrees of neurodegenerative pathology, were employed together with synthetic MRIs. Combining both histogram and tissue-specific intensity information, a correspondence is obtained for each tissue across images. The novelty consists of computing three standardizing transformations for the three main brain tissues, for each tissue class separately. In order to create a continuous intensity mapping, spline smoothing of the overall slightly discontinuous piecewise-linear intensity transformation is performed. The robustness of the technique is assessed in a post hoc manner, by verifying that automatic segmentation of images before and after standardization gives a high overlapping (Dice index >0.9) for each tissue class, even across images coming from different sources. Furthermore, SBST efficacy is tested by evaluating if and how much it increases intertissue discrimination and by assessing gaussianity of tissue gray-level distributions before and after standardization. Some quantitative comparisons to already existing different approaches available in the literature are performed.

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Acknowledgments

First of all, we warmly thank our anonymous reviewers for their pertinent comments and useful suggestions. We thank the Alzheimer’s Disease Neuroimaging Initiative. Data collection and sharing for this project was funded by the Alzheimer’s Disease Neuroimaging Initiative (ADNI) (National Institutes of Health Grant U01 AG024904). ADNI is funded by the National Institute on Aging, the National Institute of Biomedical Imaging and Bioengineering, and through generous contributions from the following: Alzheimer’s Association; Alzheimer’s Drug Discovery Foundation; BioClinica, Inc.; Biogen Idec Inc.; Bristol-Myers Squibb Company; Eisai Inc.; Elan Pharmaceuticals, Inc.; Eli Lilly and Company; F. Hoffmann-La Roche Ltd and its affiliated company Genentech, Inc.; GE Healthcare; Innogenetics, N.V.; IXICO Ltd.; Janssen Alzheimer Immunotherapy Research & Development, LLC.; Johnson & Johnson Pharmaceutical Research & Development LLC.; Medpace, Inc.; Merck & Co., Inc.; Meso Scale Diagnostics, LLC.; NeuroRx Research; Novartis Pharmaceuticals Corporation; Pfizer Inc.; Piramal Imaging; Servier; Synarc Inc.; and Takeda Pharmaceutical Company. The Canadian Institutes of Health Research is providing funds to support ADNI clinical sites in Canada. Private sector contributions are facilitated by the Foundation for the National Institutes of Health (www.fnih.org). The grantee organization is the Northern California Institute for Research and Education, and the study is coordinated by the Alzheimer’s Disease Cooperative Study at the University of California, San Diego. ADNI data are disseminated by the Laboratory for Neuro Imaging at the University of California, Los Angeles. This research was also supported by NIH grants P30 AG010129 and K01 AG030514. We thank the Open Access Structural Imaging Series (OASIS), a project dedicated to making brain imaging data openly available to the public (grant numbers P50 G05681, P01 AG03991, R01 AG021910, P20 MH071616, and U24 RR021382). This work is inserted in the framework of the “Programma Operativo Nazionale (PON) 254/Ric- Ricerca e competitività 2007-2013” of the Italian Ministry of Education, University, and Research (upgrading of the “Centro ricerche per la salute dell'uomo e dell'ambiente” PONa3_00334). It is also supported by the Italian “Istituto Nazionale di Fisica Nucleare” (INFN).

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Authors and Affiliations

  1. Dipartimento di Matematica e Fisica “Ennio De Giorgi”, Università del Salento, Ecotekne, via per Monteroni, Corpo M, 73100, Lecce, Italy

    Giorgio De Nunzio, Rosella Cataldo & Alessandra Carlà

  2. Istituto Nazionale di Fisica Nucleare, sez di Lecce, Lecce, Italy

    Giorgio De Nunzio, Rosella Cataldo & Alessandra Carlà

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  1. Giorgio De Nunzio
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  2. Rosella Cataldo
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  3. Alessandra Carlà
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Correspondence to Giorgio De Nunzio.

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De Nunzio, G., Cataldo, R. & Carlà, A. Robust Intensity Standardization in Brain Magnetic Resonance Images. J Digit Imaging 28, 727–737 (2015). https://doi.org/10.1007/s10278-015-9782-8

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  • DOI: https://doi.org/10.1007/s10278-015-9782-8

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