Super-resolved Estimation of White Matter Microstructure via 3D Conditional Latent Diffusion Model

As a powerful microstructural imaging technique, neurite orientation dispersion and density imaging (NODDI) provides detailed insights into brain microstructures. Its clinical application is often restricted by the necessity for high-quality scanning, which can be challenging to achieve in practical settings. To overcome this limitation, we propose an innovative 3D conditional latent diffusion model (3D-CLDM) to generate high-quality NODDI index maps from low-resolution diffusion magnetic resonance imaging data. The 3D-CLDM is a two-stage super-resolved microstructure estimation model that includes training a vector quantized generative adversarial network and a diffusion model. It leverages the sophisticated high-dimensional data modeling capabilities of the conditional latent diffusion model to effectively capture and represent intricate microstructural features that are difficult to detect with conventional techniques. We conducted comprehensive experiments using data from the human connectome project to rigorously assess our model's performance. The results reveal that our approach not only significantly improves the quality of super-resolved microstructural estimation but also surpasses current state-of-the-art models in both qualitative and quantitative evaluations. This highlights the potential of 3D-CLDM to advance brain microstructure imaging, making it more feasible and effective for clinical applications.