Many scalar measures have been proposed to quantify magnetic resonance diffusion tensor imaging (MR DTI) data in the brain. However, only two parameters are commonly used in the literature: mean diffusion (D) and fractional anisotropy (FA). We introduce a visualization technique which permits the simultaneous analysis of an additional five scalar measures. This enhanced diversity is important, as it is not known a priori which of these measures best describes pathological changes for brain tissue. The proposed technique is based on a tensor transformation, which decomposes the diffusion tensor into its isotropic (p) and anisotropic (q) components. To illustrate the use of this technique, diffusion tensor imaging was performed on a healthy volunteer, a sequential study in a patient with recent stroke, a patient with hydrocephalus and a patient with an intracranial tumour. Our results demonstrate a clear distinction between different anatomical regions in the normal volunteer and the evolution of the pathology in the patients. In the normal volunteer, the brain parenchyma values for p and q fell into a narrow band with 0.976
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