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Abstract #4079

Comprehensive head motion modelling and correction using simultaneously acquired MR and PET data

Francesco Sforazzini1, Jakub Baran1, Alexandra Carey1,2, Nadim Jon Shah1,3,4, Gary Egan1,5,6, and Zhaolin Chen1,4

1Monash Biomedical Imaging, Melbourne, Australia, 2Monash Health, Melbourne, Australia, 3Institute of Neuroscience and Medicine, Forschungszentrum Jülich GmbH, Jülich, Germany, 4Department of Electrical and Computer Systems Engineering, Monash University, Melbourne, Australia, 5Australian Research Council Centre of Excellence for Integrative Brain Function, Monash University, Melbourne, Australia, 6Monash Institute of Cognitive and Clinical Neuroscience, Monash University, Melbourne, Australia

Head motion is one of the major issues in neuroimaging. With the introduction of MR-PET scanners, motion parameters can now be estimated from two independent modalities acquired simultaneously. In this work, we propose a new data-driven method that combines MR image registration and PET data driven approach to model head motion during the complete course of MR-PET examination. Without changing the MR-PET acquisition protocol, the proposed method provides motion estimates with a temporal resolution of ~2 secs. Results on a phantom dataset show that the proposed method can significantly reduce motion artefact in brain PET images and improve image sharpness compared with the MR based methods.

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