Assessing DT-MRI tractography results via sampling the fiber tract space

Abstract

Complex neural processes in human brain are realized through a huge number of connections between neural cells. White matter tractography is the only available tool to reconstruct these anatomical connectivities non-invasively and in vivo. Following the emergence of di usion imaging, several tractography algorithms have been proposed, where the local direction of white matter ber bundles is estimated from measurements of water di usion in human brain. The goal of this thesis is to introduce a generic tractography assessment and improvement method for di usion tensor imaging (DTI) data. The proposed method takes a set of ber tracts that are generated with any tractography algorithm as the input, and allow the user to interactively assess tractography results by identifying the erroneous or inde nite regions in the DTI data along input tracts and highlighting possible branching patterns of ber bundles. By introducing alternative pathways that might have been missed by the initial tractography, given tractography results can also be improved. The technique relies on splitting the input tracts into shorter segments to prevent error accumulation, followed by sampling from the space of short tract clusters to estimate the connectivities between these short ber segments. After the connectivity values are computed, given a set of seed tracts and a connectivity threshold, the method displays the short tracts that are connected to the seed tracts with a probability higher than the given threshold in an interactive environment. Thus, the possible pathways can be investigated as a function of the connectivity threshold, highlighting the uncertainty in DTI data.