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Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data
Published on: June 26, 2013
Differentiation of normal and pathologic brain structures in MRI using exact T1 and T2 values followed by a
J Bernarding1, J Reul, T Tolxdorff
1Institute for Medical Statistics and Information Processing, Department of Medical Informatics, Benjamin Franklin University Clinic, Free University of Berlin, Hindenburgdamm 30, 12200 Berlin, Germany.
Abstract:
A new method to differentiate and classify normal and pathologic brain tissue in Magnetic Resonance Imaging (MRI) is introduced. With a special pulse sequence, exact T1 and T2 values are simultaneously acquired, where the T2 values may show a biexponential decay behavior. The automatic segmentation of the tissue is based on a histogram analysis of the multidimensional parameters. The tissue-characterizing parameters are stored in a database, which serves as an objective classification of different tissues. A pilot study with 15 healthy volunteers and 100 patients showed a good differentiation between normal tissue like white and gray matter, cerebrospinal fluid, muscle, and fat, thereby revealing a biexponential decay behavior of fat. Pathologic tissue-like edema and meningioma could be classified with high accuracy, while glioblastoma or astrocytoma were difficult to classify because of their non-homogeneous structures.

