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Statistical normalization techniques for magnetic resonance imaging

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

NeuroImage clinical, 2014-01, Vol.6 (C), p.9-19 [Periódico revisado por pares]

Netherlands: Elsevier

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  • Título:
    Statistical normalization techniques for magnetic resonance imaging
  • Autor: 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
  • Assuntos: 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 ; Statistics
  • É parte de: NeuroImage clinical, 2014-01, Vol.6 (C), p.9-19
  • Notas: ObjectType-Article-1
    SourceType-Scholarly Journals-1
    ObjectType-Feature-2
    content type line 23
    Data used in the preparation of this article was obtained from the Australian Imaging Biomarkers and Lifestyle Flagship Study of Ageing (AIBL) funded by the Commonwealth Scientific and Industrial Research Organisation (CSIRO) which was made available at the ADNI database (www.loni.ucla.edu/ADNI). The AIBL researchers contributed data but did not participate in the analysis or writing of this report. AIBL researchers are listed at www.aibl.csiro.au.
    Data used in preparation of this article were obtained from the Alzheimers Disease Neuroimaging Initiative (ADNI) database (adni.loni.ucla.edu). As such, the investigators within the ADNI contributed to the design and implementation of ADNI and/or provided data but did not participate in the analysis or writing of this report. A complete listing of ADNI investigators can be found at: http://adni.loni.ucla.edu/wpcontent/uploads/how_to_apply/ADNI_Acknowledgement_List.pdf.
  • Descrição: 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.
  • Editor: Netherlands: Elsevier
  • Idioma: Inglês
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  • Realização: ABCD-USP USP   Logos de Redes Sociais: Freepik

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