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Abstract #0852

Graph-Based Tractography for Robust Propagation Through Complex Fibre Configurations

Stamatios N. Sotiropoulos1, Li Bai2, Paul S. Morgan3,4, Christopher R. Tench1

1Division of Clinical Neurology, University of Nottingham, Nottingham, UK; 2School of Computer Science, University of Nottingham, Nottingham, UK; 3Division of Academic Radiology, University of Nottingham, Nottingham, UK; 4Radiology & Radiological Sciences, Medical University of South Carolina, Charleston, SC, USA

Graph-based distributed tractography provides an alternative to streamline approaches. However, graph-based tracking through complex fibre configurations has not been extensively studied and existing methods have inherent limitations. In this study, we discuss these limitations and present a new approach for robustly propagating through fibre crossings, as these are depicted by the Q-ball orientation distribution functions (ODFs).Complex ODFs are decomposed to components representative of single-fibre populations and an appropriate image graph is created. Path strengths are calculated using a modified version of Dijkstras shortest path algorithm. A comparison with existing methods is performed on simulated and on human Q-ball imaging data.