  The rhythmic pumping motion of the heart stands as a cornerstone in life, as
it circulates blood to the entire human body through a series of carefully
timed contractions of the individual chambers. Changes in the size, shape and
movement of the chambers can be important markers for cardiac disease and
modeling this in relation to clinical demography or disease is therefore of
interest. Existing methods for spatio-temporal modeling of the human heart
require shape correspondence over time or suffer from large memory
requirements, making it difficult to use for complex anatomies. We introduce a
novel conditional generative model, where the shape and movement is modeled
implicitly in the form of a spatio-temporal neural distance field and
conditioned on clinical demography. The model is based on an auto-decoder
architecture and aims to disentangle the individual variations from that
related to the clinical demography. It is tested on the left atrium (including
the left atrial appendage), where it outperforms current state-of-the-art
methods for anatomical sequence completion and generates synthetic sequences
that realistically mimics the shape and motion of the real left atrium. In
practice, this means we can infer functional measurements from a static image,
generate synthetic populations with specified demography or disease and
investigate how non-imaging clinical data effect the shape and motion of
cardiac anatomies.
