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Abstract #1916

Towards Automated Modelling of Maxillofacial Musculature

Greg Daniel Parker1,2, Nicholas Drage3,4, Paul L. Rosin2, A. David Marshall2, Stephen Richmond4, John Evans1, Derek K. Jones1

1CUBRIC, School of Psychology, Cardiff University, Cardiff, United Kingdom; 2School of Computer Science, Cardiff University, Cardiff, United Kingdom; 3Cardiff Vale NHS Trust, United Kingdom; 4School of Dentistry, Cardiff University, United Kingdom

Accurate in vivo estimates of muscle fibre trajectory are desirable for evaluation of subject-specific maxillofacial surgical treatment options. While diffusion tensor MRI provides adequatly reconstructs larger muscles (e.g. calf), fibre crossing inherent to maxillofacial musculature exposes well-known limitations; necessitating alternative analysis methodologies. Constrained spherical harmonic deconvolution demonstrates potential, however current data-driven calibration (optimized for white matter) produce spurious peaks in the fibre orientation density, adversely affecting tractography. With clinical application in mind, we demonstrate an automated tissue-specific calibration which, for the first time, successfully reconstructs complex muscle tissue in vivo and include preliminary results of unsupervised tract segmentation.