Subject-specific physiological motion modeling combined with low-dimensional real-time sensing can provide effective prediction of acyclic tissue deformation particularly due to respiration. However, real-time sensing signals used for predictive motion modeling can be strongly coupled with each other but poorly correlated with respiratory induced cardiac deformation. This paper explores a systematic framework based on sequential feature selection for optimal sensor placement so as to achieve maximal model sensitivity and prediction accuracy in response to the entire range of tissue deformation. The proposed framework effectively resolves the problem encountered by traditional regression methods in that the latent variables from both the input and output of the regression model are used to establish their inner relationships. Detailed numerical analysis and in vivo results are provided, which demonstrate the potential clinical value of the technique.
pubs.doc.ic.ac.uk: built & maintained by Ashok Argent-Katwala.