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Abstract #3581

Outlier Detection for High B-Value Diffusion Data

Kerstin Pannek1, David Raffelt2, Christopher Bell1, Jane Mathias3, Stephen Rose1

1The University of Queensland, Brisbane, Queensland, Australia; 2Brain Research Institute, Australia; 3University of Adelaide, Australia

Diffusion weighted images are prone to artefacts caused by physiological noise. Existing model based approaches for voxel-wise identification of such artefacts rely on the diffusion tensor model, which is problematic in crossing fibre areas and at higher b-values required for high angular resolution diffusion imaging. We developed a voxel-wise identification method based on a higher order model of diffusion, and compared outlier probability maps obtained using the tensor model with those obtained using a higher order model in a cohort of 103 healthy participants.