Generative 3D Cardiac Shape Modelling for In-Silico Trials
This work addresses the need for realistic synthetic cardiac shapes for in-silico trials in medical imaging, though it appears incremental as it applies existing neural field techniques to a specific domain.
The authors tackled the problem of modeling and generating synthetic aortic shapes for in-silico trials by proposing a deep learning method based on neural signed distance fields, achieving high fidelity in representing real patient anatomies.
We propose a deep learning method to model and generate synthetic aortic shapes based on representing shapes as the zero-level set of a neural signed distance field, conditioned by a family of trainable embedding vectors with encode the geometric features of each shape. The network is trained on a dataset of aortic root meshes reconstructed from CT images by making the neural field vanish on sampled surface points and enforcing its spatial gradient to have unit norm. Empirical results show that our model can represent aortic shapes with high fidelity. Moreover, by sampling from the learned embedding vectors, we can generate novel shapes that resemble real patient anatomies, which can be used for in-silico trials.