NDLI: Assessment of input signal positioning for cardiac respiratory motion models during different breathing patterns

Content Provider	IEEE Xplore Digital Library
Author	Savill, F. Schaeffter, T. King, A.P.
Copyright Year	2011
Description	Author affiliation: Division of Imaging Sciences and Biomedical Engineering, King's College London, U.K. (Savill, F.; Schaeffter, T.; King, A.P.)
Abstract	Motion models have been applied as a solution to the problem of respiratory motion in a range of applications. Such models predict motion fields based on 1-D signals or signal combinations. These signals often measure the motion of a region of the subject's anatomy, such as the chest surface or diaphragm. The hypotheses we investigate in this paper are that the predictive accuracy of motion models will vary depending on the choice of input signal(s) used by the model, and furthermore that the optimal choice of signal(s) will vary depending on the breathing pattern of the subject (e.g. normal breathing, deep breathing, fast breathing). We test these hypotheses by forming cardiac respiratory motion models from dynamic MRI data acquired from 9 volunteers. For input signals we produce post-processed 'virtual navigators' from the dynamic MRI images, enabling us to test arbitrary navigator positions and orientations. Our results support both of our hypotheses. We show that the optimal choice of input signal over all breathing patterns was a combination of signals including one positioned on the diaphragm and either one on the abdominal surface or one on the lateral wall of the heart. In addition, the best combination changed as the subject altered their breathing pattern.
Starting Page	1698
Ending Page	1701
File Size	631595
Page Count	4
File Format	PDF
ISBN	9781424441273
ISSN	19457928
DOI	10.1109/ISBI.2011.5872731
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-03-30
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Navigation Magnetic resonance imaging Dynamics Predictive models Computational modeling Image resolution Heart navigators Respiratory motion modelling cardiac MRI
Content Type	Text
Resource Type	Article

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