Differentiation of normal and pathologic brain structures in MRI using exact T1 and T2 values followed by a multidimensional cluster analysis.
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.
Bernarding
J
J
Institute for Medical Statistics and Information Processing, Department of Medical Informatics, Benjamin Franklin University Clinic, Free University of Berlin, Hindenburgdamm 30, 12200 Berlin, Germany.
Reul
J
J
Tolxdorff
T
T
D000273:Adipose Tissue / Q000033:anatomy & histology; D001921:Brain / Q000033:anatomy & histology* / Q000473:pathology; D001927:Brain Diseases / Q000175:diagnosis*; D001932:Brain Neoplasms / Q000175:diagnosis; D016000:Cluster Analysis; D003937:Diagnosis, Differential; D006801:Humans; D007089:Image Enhancement / Q000379:methods*; D008279:Magnetic Resonance Imaging / Q000379:methods*; D018482:Muscle, Skeletal / Q000033:anatomy & histology; D017711:Nonlinear Dynamics; D010865:Pilot Projects; D012016:Reference Values; D012044:Regression Analysis; D012984:Software