  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.
