A bilinear model for temporally coherent respiratory motion

We propose a bilinear model of respiratory organ motion. The advantages of classical statistical shape modelling are combined with a preconditioned trajectory basis for separately modelling the shape and motion components of the data. The separation of a linear basis into bilinear form leads to a more compact representation of the underlying physical process and the resulting model respects the temporal regularity within the training data, which is an important property for modelling quasi-periodic data. Bilinear modelling is combined with a Bayesian reconstruction algorithm for sparse data under observation noise. By applying the model to liver motion data, we show that our bilinear formulation of respiratory motion is significantly more parsimonious and can even outperform linear PCA-based models.