LS-MAT: Lifespan structural magnetic resonance imaging synthesis for microstructural covariance profile analysis toolbox

Abstract

Human brain structure changes dynamically across the lifespan, closely linked to cognitive development and decline. T1-weighted and T2-weighted magnetic resonance imaging (MRI) are widely used to assess anatomical and microstructural properties. However, acquiring longitudinal multimodal MRI is resource-intensive, limiting characterization of age-related trajectories. Here, we propose LS-MAT, a generative framework for synthesizing personalized, multimodal, and age-conditioned structural MRIs to support lifespan neuroimaging research. The framework integrates: (i) a variational autoencoder with a generative adversarial network for efficient latent encoding, (ii) a latent diffusion model for high-resolution conditional synthesis, and (iii) a ControlNet for modality-guided structural consistency. We trained and evaluated the model on large-scale, publicly available datasets spanning ages 5–100 years. LS-MAT achieved strong performance in modality conversion, measured by peak signal-to-noise ratio, structural similarity index measure, and mean squared error. The generated images captured established developmental and aging trends, including ventricular enlargement, cortical thinning, and age-related trajectories of T1/T2-weighted ratio-based microstructural profiles. Compared with existing methods, our model outperformed previous approaches in both modality conversion and age-conditioned synthesis tasks. These findings highlight the potential of generative modeling to overcome data scarcity in lifespan neuroimaging and provide a powerful tool for studying structural brain changes. The pipeline not only supports longitudinal analyses but also enables the derivation of microstructural profile features and is openly available at https://github.com/hobacteria/LS-MAT