This research aims to develop a methodological framework based on a data driven approach known as particle filters,
often found in computer vision methods, to correct the effect of respiratory motion on Nuclear Medicine imaging data.
Particles filters are a popular class of numerical methods for solving optimal estimation problems and we wish to use
their flexibility to make an adaptive framework. In this work we use the particle filter for estimating the deformation of
the internal organs of the human torso, represented by X, over a discrete time index k. The particle filter approximates
the distribution of the deformation of internal organs by generating many propositions, called particles. The posterior
estimate is inferred from an observation Zk of the external torso surface. We demonstrate two preliminary approaches in
tracking organ deformation. In the first approach, Xk represent a small set of organ surface points. In the second
approach, Xk represent a set of affine organ registration parameters to a reference time index r. Both approaches are
contrasted to a comparable technique using direct mapping to infer Xk from the observation Zk. Simulations of both
approaches using the XCAT phantom suggest that the particle filter-based approaches, on average performs, better.