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Abstract #4207

Two-Tensor Residual Bootstrapping on Classified Tensor Morphologies

Nagulan Ratnarajah1, Andy Simmons Simmons2, Ali Hojjatoleslami1

1Neurosciences & Medical Image Computing, University of Kent, Canterbury, Kent, United Kingdom; 2Neuroimaging Department, Institute of Psychiatry, Kings College London., United Kingdom

A fast and clinically feasible two-tensor residual-bootstrapping algorithm is applied to classified tensor-morphologies for estimating uncertainty in fibre-orientation and probabilistic-tractography. The classification of tensor-morphologies allows information about the true tensor morphology to be considered when selecting the most appropriate bootstrap procedure to use. We use a constrained two-tensor model for the planar voxels, so MRI acquisition times can be greatly reduced when compared to other multi-tensor approaches which will enable widespread clinical use. Based on our experimental evaluations, unlike previous bootstrap methods with other multi-fibre approaches our algorithm shows improved computational efficiency and accurately reconstructs fibre paths and recovers complex fibre configurations.