Diffusion tensor and tensor metrics imaging in intracranial epidermoid cysts

J Magn Reson Imaging. 2009 Apr;29(4):967-70. doi: 10.1002/jmri.21686.

Abstract

Purpose: To explore the utility of diffusion tensor imaging (DTI) and diffusion tensor metrics (DTM) in characterizing the structural pathology of epidermoid cysts. DTI gives information about the tissue structure; a high fractional anisotropy (FA) indicates a highly structured orientation of the tissue, fibers, or white matter tracts. Based on the tensor rank, a set of three metrics has been described that can be used to measure the directional dependence of diffusion: linear anisotropy (CL), planar anisotropy (CP), and spherical anisotropy (CS). DTM takes into account the shape of diffusion anisotropy and hence may provide better insight into the orientation of structures than FA.

Materials and methods: DTI was performed in three patients with epidermoid cysts. FA, directionally-averaged mean diffusivity (Dav), exponential apparent diffusion coefficient (eADC), and DTM, such as CL, CP, and CS, were measured from the tumor core as well as from the normal-appearing white matter. Histopathological correlation was obtained.

Results: Epidermoid cysts showed high FA with Dav values similar to that of normal white matter. eADC maps did not show any restriction of diffusion. FA values were high, but not as high as that for the white matter. CP values were higher and CL values were lower than those obtained for the white matter in various regions.

Conclusion: High CP values suggest preferential diffusion of water molecules along a two-dimensional geometry, which could be attributed to the well-structured orientation of keratin filaments and flakes within the tumor as demonstrated by histopathology. Advanced imaging modalities like DTI with DTM can provide information regarding the microstructural anatomy of the epidermoid cysts.

MeSH terms

  • Brain Diseases / pathology*
  • Diffusion Magnetic Resonance Imaging / methods*
  • Epidermal Cyst / pathology*
  • Humans
  • Image Processing, Computer-Assisted