Tumor invasion margin from diffusion weighted imaging

• Author / Creator

  Mosayebi, Parisa

• Glioma is one of the most challenging types of brain tumors to be treated or controlled locally. One
  of the main problems is to determine which areas of the apparently normal brain contain glioma
  cells, as gliomas are known to infiltrate several centimetres beyond the clinically apparent lesion
  that is visualized on standard CT or MRI. To ensure that radiation treatment encompasses the whole
  tumor, including the cancerous cells not revealed by MRI, doctors treat the volume of brain that
  extends 2cm out from the margin of the visible tumor. This approach does not consider varying
  tumor-growth dynamics in different brain tissues, thus it may result in killing some healthy cells
  while leaving cancerous cells alive in other areas. These cells may cause recurrence of the tumor
  later in time which limits the effectiveness of the therapy.
  In this thesis, we propose two models to define the tumor invasion margin based on the fact that
  glioma cells preferentially spread along nerve fibers. The first model is an anisotropic reaction-diffusion
  type tumor growth model that prioritizes diffusion along nerve fibers, as given by DW-MRI
  data. The second proposed approach computes the tumor invasion margin using a geodesic
  distance defined on the Riemannian manifold of brain fibers. Both mathematical models result
  in Partial Differential Equations (PDEs) that have to be numerically solved. Numerical methods
  used for solving differential equations should be chosen with great care. A part of this thesis is
  dedicated to discuss in detail, the numerical aspects such as stability and consistency of different
  finite difference methods used to solve these PDEs. We review the stability issues of several 2D
  methods that discretize the anisotropic diffusion equation and we propose an extension of one 2D
  stable method to 3D. We also analyze the stability issues of the geodesic model. In comparison, the
  geodesic model is numerically more stable than the anisotropic diffusion model since it results in a first-order PDE. Finally, we evaluate both models on actual DTI data from patients with glioma by
  comparing our predicted growth with follow-up MRI scans. Results show improvement in predicting
  the invasion margin when using the geodesic distance model as opposed to the 2cm conventional
  Euclidean distance.

• Subjects / Keywords

  • Tumor invasion margin

• Graduation date

  Spring 2010

• Type of Item

  Thesis

• Degree

  Master of Science

• DOI

  https://doi.org/10.7939/R3Q13V

• License

  This thesis is made available by the University of Alberta Libraries with permission of the copyright owner solely for non-commercial purposes. This thesis, or any portion thereof, may not otherwise be copied or reproduced without the written consent of the copyright owner, except to the extent permitted by Canadian copyright law.