  Two key questions in cardiac image analysis are to assess the anatomy and
motion of the heart from images; and to understand how they are associated with
non-imaging clinical factors such as gender, age and diseases. While the first
question can often be addressed by image segmentation and motion tracking
algorithms, our capability to model and to answer the second question is still
limited. In this work, we propose a novel conditional generative model to
describe the 4D spatio-temporal anatomy of the heart and its interaction with
non-imaging clinical factors. The clinical factors are integrated as the
conditions of the generative modelling, which allows us to investigate how
these factors influence the cardiac anatomy. We evaluate the model performance
in mainly two tasks, anatomical sequence completion and sequence generation.
The model achieves a high performance in anatomical sequence completion,
comparable to or outperforming other state-of-the-art generative models. In
terms of sequence generation, given clinical conditions, the model can generate
realistic synthetic 4D sequential anatomies that share similar distributions
with the real data.
