Anonymization is a crucial de-identification technique that protects data privacy while ensuring its utility for model building. Current generative models such as generative adversarial networks and variational auto-encoders (VAEs) have been applied to medical image anonymization but mainly focus on general image features, lacking specificity in regions of interest such as lesions. This study proposes a novel framework for brain magnetic resonance imaging anonymization, enabling the handling of lesion region prediction while preserving patient privacy. The framework consists of three stages: pre-training VAEs to represent lesion and non-lesion regions in latent space; fine-tuning these latent representations using a diffusion model conditioned on spatial and temporal features; and generating medical image substitutions through joint decoding of lesion and non-lesion latent representations. The comparative investigation has highlighted the benefits of our proposed methods, achieving a promising privacy-utility balance. In a small number of real sample scenarios, using synthetic samples with an 86% anonymity rate still enhanced the downstream segmentation task by 4.60% and the classification task by 8.75%. Our proposed framework offers significant improvements over existing methods in preserving privacy and maintaining data utility for lesion prediction tasks, which holds potential implications for enhanced privacy practices in medical imaging.
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