Spatio-temporal neural distance fields for conditional generative modeling of the heart
This work addresses cardiac disease modeling by enabling functional inference from static images and synthetic population generation, though it is incremental as it builds on existing generative and implicit representation methods.
The paper tackled the problem of spatio-temporal modeling of the human heart, which is challenging due to shape correspondence and memory issues, by introducing a conditional generative model based on spatio-temporal neural distance fields; it outperformed state-of-the-art methods in anatomical sequence completion and generated realistic synthetic sequences for the left atrium.
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.