For risk analysis prior to interventional treatment of brain tumors it is important to identify the functional brain areas affected by the tumor and to estimate their connectivity. Fiber Tracking (FT) on Diffusion Tensor (DT) data has the potential to facilitate this task. Our work is organized in two parts. First, we derive a relationship between diffusion anisotropy and orientation uncertainty of the DT by considering image noise. In order to assess a given FT algorithm with respect to the reconstruction of locally disturbed fiber bundles, this relationship is used for the simulation of white mat-ter lesions in DT data. Then, a deflection based FT algorithm is assessed with our software phantom. The FT algorithm is modified and its parameters are adjusted in order to obtain a fiber bundle reconstruction, which is robust to local fiber disturbance. Thus, it is demonstrated how to evaluate and improve FT algorithms with respect to the reconstruction of locally disturbed fiber bundles on the basis of phantom data with known ground truth. This is expected to improve functional and structural risk analysis for the interventional treatment of brain tumors.