GM-LDM: Latent Diffusion Model for Brain Biomarker Identification through Functional Data-Driven Gray Matter Synthesis
Journal:
arXiv
Published Date:
Jun 15, 2025
Abstract
Generative models based on deep learning have shown significant potential in
medical imaging, particularly for modality transformation and multimodal fusion
in MRI-based brain imaging. This study introduces GM-LDM, a novel framework
that leverages the latent diffusion model (LDM) to enhance the efficiency and
precision of MRI generation tasks. GM-LDM integrates a 3D autoencoder,
pre-trained on the large-scale ABCD MRI dataset, achieving statistical
consistency through KL divergence loss. We employ a Vision Transformer
(ViT)-based encoder-decoder as the denoising network to optimize generation
quality. The framework flexibly incorporates conditional data, such as
functional network connectivity (FNC) data, enabling personalized brain
imaging, biomarker identification, and functional-to-structural information
translation for brain diseases like schizophrenia.
