Abstract #4079

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

Francesco Sforazzini¹, Jakub Baran¹, Alexandra Carey^1,2, Nadim Jon Shah^1,3,4, Gary Egan^1,5,6, and Zhaolin Chen^1,4

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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