\begin{abstract}
In the medical domain, the use of Machine Learning (ML) techniques for diagnosis, treatment planning, and medical imaging interpretation is becoming increasingly important. However, these approaches require a large amount of data, which is challenging to access due to its sensitive nature and related privacy concerns. Synthetic data generation, enabled by advances in generative techniques, provides a solution to create large anonymized datasets for training models without compromising patient privacy.
Recently, Flow Matching with Optimal Transport (OTFM) has proven to be an effective technique for generating realistic 2D natural images, surpassing existing methods, but its usage for 3D medical data generation is limited.
In this work we generate craniofacial skeletal data using OTFM and test the validity of the results in two clinical downstream tasks: skull alignment and shape completion. Moreover, we compare the quality of synthetic data generated with OTFM with the ones generated using Denoising Diffusion Probabilistic Models (DDPMs). We show that Flow Matching with Optimal Transport is an effective technique for generating synthetic data and that, in this context, it outperforms DDPMs both in quality and robustness. 
% Improvements in 3D medical image generation will enable easier and more effective training of ML models, supporting clinicians in performing automatable medical tasks.




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\end{abstract}