A Structural Causal Model for MR Images of Multiple Sclerosis
This work addresses the need for causal inference tools in medical imaging to support personalized treatment decisions for patients with multiple sclerosis, though it appears incremental by applying existing SCM methods to a specific domain.
The authors tackled the problem of answering counterfactual questions in precision medicine for multiple sclerosis by developing a structural causal model that generates counterfactual MR brain images based on changes in demographic or disease covariates, enabling applications in disease progression modeling and image processing.
Precision medicine involves answering counterfactual questions such as "Would this patient respond better to treatment A or treatment B?" These types of questions are causal in nature and require the tools of causal inference to be answered, e.g., with a structural causal model (SCM). In this work, we develop an SCM that models the interaction between demographic information, disease covariates, and magnetic resonance (MR) images of the brain for people with multiple sclerosis. Inference in the SCM generates counterfactual images that show what an MR image of the brain would look like if demographic or disease covariates are changed. These images can be used for modeling disease progression or used for image processing tasks where controlling for confounders is necessary.