Implementation of determinisic and probabilisic fiber tracking algorihms for abnormal brain tissues analysis using dMRI

Abstract

Diffusion magnetic resonance imaging (DMRI) is a technique that allows to probe the microstructure of materials. In our case we use it for the White Matter (WM) while tractography is a computational reconstruction method based on diffusion-weighted mag- netic resonance imaging (DWI)that attempts to reveal the trajectories of white matter pathways in vivo and to infer the underlying structural connectome of the human brain. The aim of our study is to reach the best reconstruction of the WM in the presence of abnormal tissues such as Astrocytoma type II and III, Glioblastoma Multiform, Menin- gioma and Oligodendrocytoma type II. For that purpose, nine data about the mentioned diseases aquired from the the UK data archive are utilised, the procedure is to apply both deterministic and probabilistic methods with two stopping criteria for each to the dataset. The analysis of the four outputs is conducted for each patient to assess the results in the region of interest (ROI). Besides the comparison between the tracts generated with the probabilistic and the deter- ministic algorithms, another comparison is performed for FA=0.2 and FA=0.4 as stopping criteria and their effect on the generated fibers. The main contribution of this work is the implementation of the probabilistic tracking algorithm. While searching for information concerning tractography .It is found that de- terministic tractography is widely used because of its ease and simplicity. In this repport advantages of using the probabilistic method for better results demonstrated therefore both methods were applied on the same dataset in addition to analising the effect of stopping criterion on the results in the ROI and the whole brain.