Autoadaptive motion modelling

Baumgartner CF, Kolbitsch C, McClelland JR, Rueckert D, King AP (2014)

Publication Type: Conference contribution

Publication year: 2014

Publisher: Institute of Electrical and Electronics Engineers Inc.

Pages Range: 457-460

Conference Proceedings Title: 2014 IEEE 11th International Symposium on Biomedical Imaging, ISBI 2014

Event location: Beijing, CHN

ISBN: 9781467319591

DOI: 10.1109/isbi.2014.6867907

Abstract

Respiratory motion is a complicating factor for many applications in medical imaging. Respiration is an approximately periodic, but very complex motion that may undergo significant changes within the duration of a treatment or imaging session. Motion models are a possible solution to the problem of respiratory motion. However, in the current state-of-the-art, the model is formed preprocedure and may lose validity during the procedure. We propose a novel autoadaptive motion model which can automatically adapt to changing breathing patterns and thus maintain its validity. We quantitatively evaluated the method on synthetic data generated from MR images acquired from 4 healthy volunteers and found that motion estimation errors after a change in breathing pattern were significantly reduced using the proposed method. Furthermore, we demonstrated the method on real MR data acquired from one healthy volunteer.

Involved external institutions

King’s College London GB United Kingdom (GB) University College London (UCL) GB United Kingdom (GB) Imperial College London / The Imperial College of Science, Technology and Medicine GB United Kingdom (GB)

How to cite

APA:

Baumgartner, C.F., Kolbitsch, C., McClelland, J.R., Rueckert, D., & King, A.P. (2014). Autoadaptive motion modelling. In 2014 IEEE 11th International Symposium on Biomedical Imaging, ISBI 2014 (pp. 457-460). Beijing, CHN: Institute of Electrical and Electronics Engineers Inc..

MLA:

Baumgartner, C. F., et al. "Autoadaptive motion modelling." Proceedings of the 2014 IEEE 11th International Symposium on Biomedical Imaging, ISBI 2014, Beijing, CHN Institute of Electrical and Electronics Engineers Inc., 2014. 457-460.

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