On the Accuracy of Fiber Tractography
Year: 2013
Type: dissertation
Abstract: Invasive neurosurgical interventions bear the risk of damaging indispensable fiber pathways. Current fiber reconstruction techniques based on diffusion tensor imaging (DTI) do not determine the spatial extent of a fiber bundle in an accurate and reliable manner, due to errors and imprecisions in both the imaging and the algorithmic pipeline. In this thesis, we start by quantifying the errors of current fiber tracking algorithms by means of novel software phantoms which provide a realistic model of neural fiber bundles. This knowledge is used to locally analyze the quality of a patient's diffusion tensor dataset and to construct individual confidence hulls around the tracked fibers. In the following chapter, these ideas are developed further and we suggest an accurate approach to the segmentation of a patient's diffusion tensor image which combines connectivity and tensor clustering information. We then focus our attention on the sensitivity of streamline tractography to user-defined regions of interest. In order to reduce this sensitivity, we demonstrate the feasibility of mapping a fiber bundle from a fiber atlas onto the DTI dataset of a patient. In the last chapter of this thesis, we consider several alternatives to visualize fiber-tracking related uncertainties by means of color-coded streamlines and confidence hulls.
Author: Sebastiano Barbieri
Language: English
Subfield: Radiology, Nuclear Medicine and Imaging
Field: Medicine
Domain: Health Sciences
Open Access status: green
OpenAlex ID: W2231720207