Statistic Model of Respiratory Motion by Using Dynamic MRI

Gang Gao¹, Jamie McClelland¹, Dave Hawkes¹

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.