  Objective: Organ deformation models have the potential to improve delivery
and reduce toxicity of radiotherapy, but existing data-driven motion models are
based on either patient-specific or population data. We propose to combine
population and patient-specific data using a Bayesian framework. Our goal is to
accurately predict individual motion patterns while using fewer scans than
previous models.
  Approach: We have derived and evaluated two Bayesian deformation models. The
models were applied retrospectively to the rectal wall from a cohort of
prostate cancer patients. These patients had repeat CT scans evenly acquired
throughout radiotherapy. Each model was used to create coverage probability
matrices (CPMs). The spatial correlations between these CPMs and ``true'' CPMs,
derived from independent scans of the same patient, were calculated.
  Main results: Spatial correlation with ground truth were significantly higher
for the Bayesian deformation models than both patient-specific and
population-derived models with 1, 2 or 3 patient-specific scans as input.
Statistical motion simulations indicate that this result will also hold for
more than 3 scans.
  Significance: The improvement over known models means that fewer scans per
patient are needed to achieve accurate deformation predictions. The models have
applications in robust radiotherapy planning and evaluation, among others.
