Probing the Limits and Capabilities of Diffusion Models for the Anatomic Editing of Digital Twins

TL;DR

This paper explores the use of Latent Diffusion Models to generate anatomically varied digital twins of cardiac anatomy, enabling enhanced virtual cohort creation for cardiovascular device simulation and assessment.

Contribution

It introduces methods for editing digital twins to create diverse anatomic variants, demonstrating the potential and limitations of diffusion models in medical image synthesis.

Findings

Diffusion models can generate anatomically varied digital twins.

Bias towards common anatomical features can be exploited for cohort augmentation.

The framework delineates the limits of diffusion models in anatomical editing.

Abstract

Numerical simulations can model the physical processes that govern cardiovascular device deployment. When such simulations incorporate digital twins; computational models of patient-specific anatomy, they can expedite and de-risk the device design process. Nonetheless, the exclusive use of patient-specific data constrains the anatomic variability which can be precisely or fully explored. In this study, we investigate the capacity of Latent Diffusion Models (LDMs) to edit digital twins to create anatomic variants, which we term digital siblings. Digital twins and their corresponding siblings can serve as the basis for comparative simulations, enabling the study of how subtle anatomic variations impact the simulated deployment of cardiovascular devices, as well as the augmentation of virtual cohorts for device assessment. However, while diffusion models have been characterized in their ability to edit natural images, their capacity to anatomically edit digital twins has yet to be studied. Using a case example centered on 3D digital twins of cardiac anatomy, we implement various methods for generating digital siblings and characterize them through morphological and topological analyses. We specifically edit digital twins to introduce anatomic variation at different spatial scales and within localized regions, demonstrating the existence of bias towards common anatomic features. We further show that such anatomic bias can be leveraged for virtual cohort augmentation through selective editing, partially alleviating issues related to dataset imbalance and lack of diversity. Our experimental framework thus delineates the limits and capabilities of using latent diffusion models in synthesizing anatomic variation for in silico trials.