Challenges and Opportunities of Diffusion-Based AI in MRI: A Critical Evaluation for Brain Age Prediction

Diffusion-based generative models have emerged as powerful tools for synthesizing high-fidelity images across diverse domains, yet their application to neuroimaging remains underexplored. We systematically evaluated two representative approaches, namely denoising diffusion probabilistic models (DDPM) and latent diffusion models (LDM), for generating synthetic brain MRI data using the Cambridge Centre for Ageing and Neuroscience (Cam-CAN) dataset. Both approaches were validated through brain age prediction tasks, with performance assessed using mean absolute error (MAE) and Pearson's correlation coefficient (R). Models trained on real MRI achieved MAE of 6.26-6.80 years (R = 0.890-0.910), whereas DDPM-generated data degraded performance to MAE of 11.50-13.57 years (R = 0.866-0.871), and LDM-generated data performed worse with MAE of 15.87-18.19 years (R = 0.792-0.827). Both DDPM and LDM exhibited significant limitations in preserving brain morphological structures critical for accurate brain age prediction. These findings highlight fundamental challenges in adapting conventional diffusion models to neuroimaging applications and underscore the need for specialized architectures and training strategies tailored to the unique characteristics of brain MRI data.