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Abstract #1786

Automatic detection of volumes affected by subvolume movement

Kerstin Pannek1, Jurgen Fripp1, Joanne George2, Roslyn Boyd2, Paul Colditz2, and Stephen Rose1

1The Australian E-Health Research Centre, CSIRO, Brisbane, Australia, 2The University of Queensland, Brisbane, Australia

Diffusion-weighted MRI is prone to a number of artefacts, including movement between subvolumes in an interleaved acquisition. Affected volumes need to be identified and dealt with before further processing. We use a registration based approach to identify volumes affected by subvolume motion, and demonstrate that a single metric, calculated from all subjects acquired using the same acquisition protocol, is sufficient to reliably identify such volumes. Importantly, the detection threshold is determined from the data itself, and can be applied to multi-shell data.

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