Preuhs, A, Ravikumar, N, Manhart, M et al. (5 more authors) (2019) Maximum Likelihood Estimation of Head Motion Using Epipolar Consistency. In: Handels, H, Deserno, TM, Maier, A, Maier-Hein, KH, Palm, C and Tolxdorff, T, (eds.) Informatik aktuell. BVM 2019: Bildverarbeitung für die Medizin, 17-19 Mar 2019, Lübeck, Germany. Springer Vieweg , pp. 134-139. ISBN 978-3-658-25325-7
Abstract
Open gantry C-arm systems that are placed within the interventional room enable 3-D imaging and guidance for stroke therapy without patient transfer. This can profit in drastically reduced time-totherapy, however, due to the interventional setting, the data acquisition is comparatively slow. Thus, involuntary patient motion needs to be estimated and compensated to achieve high image quality. Patient motion results in a misalignment of the geometry and the acquired image data. Consistency measures can be used to restore the correct mapping to compensate the motion. They describe constraints on an idealized imaging process which makes them also sensitive to beam hardening, scatter, truncation or overexposure. We propose a probabilistic approach based on the Student’s t-distribution to model image artifacts that affect the consistency measure without sourcing from motion.
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2019, Springer Fachmedien Wiesbaden GmbH, ein Teil von Springer Nature. This is a post-peer-review, pre-copyedit version of an article published in Informatik aktuell. The final authenticated version is available online at: https://doi.org/10.1007/978-3-658-25326-4_29. Uploaded in accordance with the publisher's self-archiving policy. |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Computing (Leeds) |
Depositing User: | Symplectic Publications |
Date Deposited: | 05 Aug 2019 11:19 |
Last Modified: | 07 Feb 2020 01:39 |
Status: | Published |
Publisher: | Springer Vieweg |
Identification Number: | 10.1007/978-3-658-25326-4_29 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:149272 |