Close-range feature-based head motion tracking for MRI and PET-MRI
Field | Value | Language |
dc.contributor.author | Henry, David | |
dc.contributor.author | Fulton, Roger R. | |
dc.contributor.author | Maclaren, Julian | |
dc.contributor.author | Aksoy, Murat | |
dc.contributor.author | Bammer, Roland | |
dc.contributor.author | Kyme, Andre | |
dc.date.accessioned | 2021-01-08T00:13:07Z | |
dc.date.available | 2021-01-08T00:13:07Z | |
dc.date.issued | 2018 | en_AU |
dc.identifier.uri | https://hdl.handle.net/2123/24254 | |
dc.description.abstract | Optical motion tracking systems are effective in measuring head motion during MRI and PET scans. However, most systems rely on tracking attached markers which can slip or move relative to the head. In this study, we aimed to validate a methodology which uses a stereo-optical camera system to track small feature patches on the forehead. This approach has the advantage that tracked features `native' to the skin can be tracked at very close range (<;5cm), making it ideal for use inside an MR scanner bore. 15 volunteers were instructed to perform 6 degree of freedom head motions while simultaneously being tracked by two systems - our feature-based tracking system, and a ground-truth multi-view optical system relying on passive IR-reflective markers attached to the back of the head. Sub-millimeter agreement between the two systems was achieved when head motion was purely rigid. In the case of non-rigid movement of the skin with respect to the head, large spikes in the motion traces derived from the feature-based algorithm were observed. This experiment provides a valuable dataset to benchmark future improvements and optimizations of the feature-based tracking algorithm, such as techniques to handle non-rigid motion. | en_AU |
dc.language.iso | en | en_AU |
dc.publisher | IEEE | en_AU |
dc.relation.ispartof | 2018 IEEE Nuclear Science Symposium and Medical Imaging Conference Proceedings | en_AU |
dc.rights | Copyright All Rights Reserved | en_AU |
dc.title | Close-range feature-based head motion tracking for MRI and PET-MRI | en_AU |
dc.type | Conference paper | en_AU |
dc.subject.asrc | 0299 Other Physical Sciences | en_AU |
dc.subject.asrc | 0801 Artificial Intelligence and Image Processing | en_AU |
dc.subject.asrc | 0903 Biomedical Engineering | en_AU |
dc.identifier.doi | 10.1109/NSSMIC.2018.8824682 | |
dc.relation.arc | DE160100745 | |
dc.rights.other | © 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. | |
usyd.faculty | SeS faculties schools::Faculty of Medicine and Health::Brain and Mind Centre | en_AU |
usyd.faculty | SeS faculties schools::Faculty of Engineering::School of Biomedical Engineering | en_AU |
workflow.metadata.only | No | en_AU |
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