🤖 AI Summary
To address class imbalance, privacy constraints, and insufficient coverage of modality–pathology combinations in clinical brain MRI data, this paper introduces the first latent diffusion generative model jointly controllable across multiple pathologies (healthy, glioblastoma, multiple sclerosis, dementia) and multiple MRI modalities (T1w, T1ce, T2w, FLAIR, PD). The method employs conditional embedding encoding to achieve fine-grained, disentangled control over pathology and modality—enabling zero-shot cross-configuration extrapolation to unseen modality–pathology pairings. Built upon the Latent Diffusion framework, the model synthesizes high-fidelity images, achieving significantly lower FID and higher MS-SSIM scores than baseline methods. It effectively augments rare-class samples, thereby enhancing the robustness and diagnostic reliability of downstream models. Crucially, the approach balances high-quality data augmentation with stringent patient privacy preservation, as no raw sensitive data is shared or stored during generation.
📝 Abstract
In this article, we present a Latent Diffusion Model (LDM) for the generation of brain Magnetic Resonance Imaging (MRI), conditioning its generation based on pathology (Healthy, Glioblastoma, Sclerosis, Dementia) and acquisition modality (T1w, T1ce, T2w, Flair, PD). To evaluate the quality of the generated images, the Fr'echet Inception Distance (FID) and Multi-Scale Structural Similarity Index (MS-SSIM) metrics were employed. The results indicate that the model generates images with a distribution similar to real ones, maintaining a balance between visual fidelity and diversity. Additionally, the model demonstrates extrapolation capability, enabling the generation of configurations that were not present in the training data. The results validate the potential of the model to increase in the number of samples in clinical datasets, balancing underrepresented classes, and evaluating AI models in medicine, contributing to the development of diagnostic tools in radiology without compromising patient privacy.