On Differentially Private 3D Medical Image Synthesis with Controllable Latent Diffusion Models

TL;DR

This paper introduces a method for generating synthetic 3D cardiac MRI images using differentially private latent diffusion models, balancing privacy with image quality and controllability.

Contribution

It is the first to apply and quantify differential privacy in 3D medical image synthesis, demonstrating improved performance with pre-training and analyzing privacy-quality trade-offs.

Findings

Pre-training significantly improves image quality (FID 26.77 vs. 92.52).

Tighter privacy budgets can degrade image realism and controllability.

Proper privacy-aware training enhances synthetic cardiac MRI quality.

Abstract

Generally, the small size of public medical imaging datasets coupled with stringent privacy concerns, hampers the advancement of data-hungry deep learning models in medical imaging. This study addresses these challenges for 3D cardiac MRI images in the short-axis view. We propose Latent Diffusion Models that generate synthetic images conditioned on medical attributes, while ensuring patient privacy through differentially private model training. To our knowledge, this is the first work to apply and quantify differential privacy in 3D medical image generation. We pre-train our models on public data and finetune them with differential privacy on the UK Biobank dataset. Our experiments reveal that pre-training significantly improves model performance, achieving a Fr\'echet Inception Distance (FID) of 26.77 at $\epsilon=10$, compared to 92.52 for models without pre-training. Additionally, we explore the trade-off between privacy constraints and image quality, investigating how tighter privacy budgets affect output controllability and may lead to degraded performance. Our results demonstrate that proper consideration during training with differential privacy can substantially improve the quality of synthetic cardiac MRI images, but there are still notable challenges in achieving consistent medical realism.