In radiotherapy, the internal movement of organs between treatment sessions
causes errors in the final radiation dose delivery. Motion models can be used
to simulate motion patterns and assess anatomical robustness before delivery.
Traditionally, such models are based on principal component analysis (PCA) and
are either patient-specific (requiring several scans per patient) or
population-based, applying the same deformations to all patients. We present a
hybrid approach which, based on population data, allows to predict
patient-specific inter-fraction variations for an individual patient. We
propose a deep learning probabilistic framework that generates deformation
vector fields (DVFs) warping a patient's planning computed tomography (CT) into
possible patient-specific anatomies. This daily anatomy model (DAM) uses few
random variables capturing groups of correlated movements. Given a new planning
CT, DAM estimates the joint distribution over the variables, with each sample
from the distribution corresponding to a different deformation. We train our
model using dataset of 312 CT pairs from 38 prostate cancer patients. For 2
additional patients (22 CTs), we compute the contour overlap between real and
generated images, and compare the sampled and ground truth distributions of
volume and center of mass changes. With a DICE score of 0.86 and a distance
between prostate contours of 1.09 mm, DAM matches and improves upon PCA-based
models. The distribution overlap further indicates that DAM's sampled movements
match the range and frequency of clinically observed daily changes on repeat
CTs. Conditioned only on a planning CT and contours of a new patient without
any pre-processing, DAM can accurately predict CTs seen during following
treatment sessions, which can be used for anatomically robust treatment
planning and robustness evaluation against inter-fraction anatomical changes