Autoadaptive motion modelling

C. F. Baumgartner, C. Kolbitsch, J. R. McClelland, D. Rueckert, A. P. King

Research output: Chapter in Book/Report/Conference proceedingConference paperpeer-review

1 Citation (Scopus)

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.

Original languageEnglish
Title of host publication2014 IEEE 11th International Symposium on Biomedical Imaging, ISBI 2014
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages457-460
Number of pages4
ISBN (Print)9781467319591
Publication statusPublished - 29 Jul 2014
Event2014 IEEE 11th International Symposium on Biomedical Imaging, ISBI 2014 - Beijing, China
Duration: 29 Apr 20142 May 2014

Conference

Conference2014 IEEE 11th International Symposium on Biomedical Imaging, ISBI 2014
Country/TerritoryChina
CityBeijing
Period29/04/20142/05/2014

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