Spatio-temporal neural distance fields for conditional generative modeling of the heart

TL;DR

This paper introduces a novel spatio-temporal neural distance field model for the heart that captures shape and motion conditioned on clinical data, enabling realistic synthetic sequence generation and functional inference from static images.

Contribution

It presents a new implicit generative model using neural distance fields conditioned on clinical demography, outperforming existing methods in cardiac shape and motion modeling.

Findings

Outperforms state-of-the-art in anatomical sequence completion

Generates realistic synthetic cardiac sequences

Enables inference of functional measurements from static images

Abstract

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.