Causal Spatio-Temporal Neural Distance Fields for Counterfactual Cardiac Anatomy

Abstract

Counterfactual cardiac anatomy can support risk modelling and intervention planning, by predicting how a patient's heart would change under altered clinical conditions. However, current shape generative models are conditional on clinical variables and disregard causal links between these variables. Thus, they cannot answer counterfactual questions about how a given subject's anatomy would change under a different clinical profile. Modelling the heart's continuous movement over the cardiac cycle requires a representation that is itself continuous in space and time. Spatio-temporal neural distance fields provide this while separating clinical conditioning from subject-specific shape. We place a structural causal model (SCM) over the set of clinical variables (age, sex, smoking, BMI, heart rate, systolic blood pressure), before the condition encoding of the spatio-temporal neural distance field which represents the left atrial complex over a cardiac cycle by signed distance fields. An intervention of the clinical variables, propagated through a SCM before the 4D counterfactual, is constructed from the neural distance field. The generated counterfactuals follow dose-response under age interventions and transforms the male distribution to closely match the female distribution under the intervention $\mathrm{do(\mathrm{female}{=}1)}$. By doing inverse interventions the model also approximates the original anatomy with sub millimeter precision. To our knowledge, this is the first counterfactual generation of 4D cardiac anatomy and the first integration of an SCM with an implicit neural representation as its imaging mechanism.