Principal Diffusion Direction in Peritumoral Fiber Tracts: Color Map Patterns and Directional Statistics

The ability of diffusion tensor imaging (DTI) to probe the ultrastructural properties of biological tissues presents new possibilities for DTI‐based tissue characterization, with the potential for greater pathologic specificity than conventional imaging methods. This is urgently needed in the diagnosis and treatment of cerebral neoplasms, where clinical decisions depend on the ability to discriminate tumor‐involved from uninvolved tissue, a major shortcoming of conventional imaging. Several investigators have attempted to make this determination on the basis of the apparent diffusion coefficient (ADC) or the fractional anisotropy (FA), with mixed results. The directionally encoded color map, with hues reflecting tensor orientation and intensity weighted by FA, provides an aesthetic and informative summary of DTI features throughout the brain in an easily interpreted format. The use of these maps is becoming increasingly common in both basic and clinical research, as well as in purely clinical settings. These examples serve to demonstrate our approach to the quantitation of regional diffusion tensor distributions using directional statistical methods.

[1]  J K Smith,et al.  Apparent diffusion coefficients in the evaluation of high-grade cerebral gliomas. , 2001, AJNR. American journal of neuroradiology.

[2]  Mark E Bastin,et al.  Measurements of water diffusion and T1 values in peritumoural oedematous brain , 2002, Neuroreport.

[3]  Yu-Chien Wu,et al.  Quantitative analysis of diffusion tensor orientation: Theoretical framework , 2004, Magnetic resonance in medicine.

[4]  J G Pipe,et al.  In vivo MR determination of water diffusion coefficients and diffusion anisotropy: correlation with structural alteration in gliomas of the cerebral hemispheres. , 1995, AJNR. American journal of neuroradiology.

[5]  Peter McGraw,et al.  Peritumoral brain regions in gliomas and meningiomas: investigation with isotropic diffusion-weighted MR imaging and diffusion-tensor MR imaging. , 2004, Radiology.

[6]  K. Krabbe,et al.  MR diffusion imaging of human intracranial tumours , 1997, Neuroradiology.

[7]  D L Parker,et al.  Comparison of gradient encoding schemes for diffusion‐tensor MRI , 2001, Journal of magnetic resonance imaging : JMRI.

[8]  V. Jellús,et al.  Diffusion-weighted MR imaging of intracerebral masses: comparison with conventional MR imaging and histologic findings. , 2001, AJNR. American journal of neuroradiology.

[9]  R D Tien,et al.  MR imaging of high-grade cerebral gliomas: value of diffusion-weighted echoplanar pulse sequences. , 1994, AJR. American journal of roentgenology.

[10]  M. Bastin,et al.  Visualization and analysis of white matter structural asymmetry in diffusion tensor MRI data , 2004, Magnetic resonance in medicine.

[11]  Glyn Johnson,et al.  Diffusion-tensor MR imaging of intracranial neoplasia and associated peritumoral edema: introduction of the tumor infiltration index. , 2004, Radiology.

[12]  G. Johnson,et al.  Peritumoral diffusion tensor imaging of high-grade gliomas and metastatic brain tumors. , 2003, AJNR. American journal of neuroradiology.

[13]  S. Maier,et al.  Normal brain and brain tumor: multicomponent apparent diffusion coefficient line scan imaging. , 2001, Radiology.

[14]  Mark E Bastin,et al.  Diffusion tensor MR imaging of high-grade cerebral gliomas. , 2002, AJNR. American journal of neuroradiology.

[15]  K. Kono,et al.  The role of diffusion-weighted imaging in patients with brain tumors. , 2001, AJNR. American journal of neuroradiology.

[16]  A L Alexander,et al.  Analytical computation of the eigenvalues and eigenvectors in DT-MRI. , 2001, Journal of magnetic resonance.

[17]  Khader M Hasan,et al.  Diffusion tensor eigenvector directional color imaging patterns in the evaluation of cerebral white matter tracts altered by tumor , 2004, Journal of magnetic resonance imaging : JMRI.