Abstract:

Atrial shape has been linked to atrial fibrillation (AF), yet many aspects remain poorly understood. Recent evidence suggests that the right atrium (RA) affects AF vulnerability in both chambers, highlighting the need for bi-atrial analysis. However, open bi-atrial cohorts are scarce. Statistical shape models (SSMs) address this gap, but model variability only linearly and require surface correspondence. Recently, deep learning (DL)-based generative models have emerged as an alternative. We present a DL-based bi-atrial generative model, extending an existing signed distance function based auto-decoder framework for the left atrium (LA) to a dual-head network with shared trunk, trained on 344 patients from a public CT dataset. We investigated latent dimensionality effects on reconstruction and generation performance. The model produced anatomically realistic geometries with separated atria, variable shapes, and diverse LA appendage morphologies. Volumes and volume ratios aligned with clinically observed ranges. Reconstruction and generation improved with latent dimension, with dimension 64 best balancing complexity and quality where mean Chamfer distances were 1.2 mm for reconstruction and 2.5 mm (LA) and 2.2 mm (RA) for generation. LA reconstruction distances exceeded those in the RA, likely due to greater topological complexity and the comparatively lower RA data fidelity. Compared to the existing bi-atrial SSM, the DL model required higher dimension, possibly confounded by dataset differences. Overall, loss plateaued within 400 epochs for all models, suggesting early stopping for future work. In conclusion, the model can support virtual cohort generation for AF in silico studies; allowing conditioning on clinical metadata in future work.