Multivariate image analysis of magnetic resonance images with the direct exponential curve resolution algorithm (DECRA) - Part 2: Application to human brain images
被引:32
作者:
Antalek, B
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Eastman Kodak Co, Imaging Res & Adv Dev, Rochester, NY 14650 USAEastman Kodak Co, Imaging Res & Adv Dev, Rochester, NY 14650 USA
Antalek, B
[1
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Hornak, JP
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机构:Eastman Kodak Co, Imaging Res & Adv Dev, Rochester, NY 14650 USA
Hornak, JP
Windig, W
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机构:Eastman Kodak Co, Imaging Res & Adv Dev, Rochester, NY 14650 USA
Windig, W
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[1] Eastman Kodak Co, Imaging Res & Adv Dev, Rochester, NY 14650 USA
[2] Rochester Inst Technol, Dept Chem, Rochester, NY 14623 USA
[3] Rochester Inst Technol, Ctr Imaging Sci, Rochester, NY 14623 USA
Owing to the heterogeneity of living tissues, it is challenging to quantify tissue properties using magnetic resonance imaging. Within a single voxel, contributions to the signal may result from several types of H-1 nuclei with varied chemical (e.g., -CH2-, -OH) and physical environments (e.g., tissue density, compartmentalization). Therefore, mixtures of H-1 environments are prevalent. Furthermore, each unique type of H-1 environment may possess a unique and characteristic spin-lattice relaxation time (T-1) and spin-spin relaxation time (T-2) A method for resolving these unique exponentials is introduced in a separate paper (Part 1. Algorithm and Model System) and uses the direct exponential curve resolution algorithm(DECRA). We present results from an analysis of images of the human head comprising brain tissues. (C) 1998 Academic Press.