AI-based motion correction for PET brain studies

Summary

Supervisor

Research location

Synopsis

The goal of this project is develop a complete pipeline for neural network-based motion correction of brain PET studies in the image domain. A solution will require novel data simulation approaches, neural network architectures and clever use of multi-modal data. It will facilitate much easier clinical translation of motion correction than hardware-based approaches, and enable massive archives of legacy PET data to be reprocessed.

Collaborator: Prof Arman Rahmim, University of British Columbia, Canada

Note:

Projects are available to scholarship holders only
$5k-10k /annum top-up is available to eligible scholarship holders
6-12 month visits to collaborating partner institutions is possible for relevant projects

For more information contact andre.kyme@sydney.edu.au

How to apply:

To apply, please email andre.kyme@sydney.edu.au the following:

CV
transcripts

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Opportunity ID

The opportunity ID for this research opportunity is 3489