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Abstract #4713

Statistic Model of Respiratory Motion by Using Dynamic MRI

Gang Gao1, Jamie McClelland1, Dave Hawkes1

1Centre for Medical Image Computing, University College London, London, England, UK

The accurate delivery of medical treatments to internal organs (e.g. MR guided intervention) that are subject to considerable respiratory motion has proved to be difficult. A motion model that can predict the internal motion from respiratory surrogate signals (e.g. the displacement of points on the skin surface) can help improve this situation. we are presenting a method of building respiratory motion models using the statistical technique called CCA and dynamic MRI. In this abstract, we have demonstrated that the MR-based CCA models can accurately predict the internal organ motion from respiratory surrogates measured from the skin surface. Additionally, we have compared the CCA model with a previously-reported PCA-based modelling technique by using the same MR data. The experimental results show the CCA model is superior to the PCA model in 3 out of 4 cases and appears to be more stable than the PCA model.