In this preliminary work, a variety of MRI techniques, including conventional high-resolution T1-weighted, T2-weighted, and T2-FLAIR, as well as quantitative techniques comprising of T2-relaxometry, DWI, DTI, DSC-MRI, and IVIM derived features were acquired from patients with gliomas. The features extracted from the mentioned images were explored for their potential in stratification of histopathologically-approved samples, labelled as active tumor, infiltrative glioma (edema) and normal brain tissue. Furthermore, the most accurate combination of the features for discrimination of tissue subregions was generated through a machine learning technique.
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