Latent Diffusion Models Allow Generation of Synthetic Breast MRI DCE-MIPs

The training of neural networks for classification or segmentation of medical images requires large amounts of training data. Sharing of these datasets is commonly difficult due to legislation and privacy constraints of medical data. In this work, we demonstrate the utility of latent diffusion models that allow the generation of synthetic samples of dynamic contrast-enhanced breast MRI-derived maximum intensity projections of subtraction series. Whilst the image quality of the generated data is high as demonstrated by a radiologist evaluation, further steps are envisioned to derive specific compounds of data, e.g., BI-RADS, FGT, or BPE classes.