Pattern-Aware Diffusion Synthesis of fMRI/dMRI with Tissue and Microstructural Refinement

TL;DR

This paper introduces PDS, a novel pattern-aware diffusion framework with tissue and microstructural refinement for synthesizing missing fMRI and dMRI modalities, improving structural fidelity and clinical diagnostic accuracy.

Contribution

The paper presents a dual-modal 3D diffusion model with tissue refinement to enhance cross-modality MRI synthesis, addressing previous limitations in neuroimaging data generation.

Findings

Achieves state-of-the-art PSNR/SSIM scores for fMRI and dMRI synthesis.

Synthesized data maintains structural fidelity and microstructural details.

Clinical validation shows high diagnostic accuracy with synthesized modalities.

Abstract

Magnetic resonance imaging (MRI), especially functional MRI (fMRI) and diffusion MRI (dMRI), is essential for studying neurodegenerative diseases. However, missing modalities pose a major barrier to their clinical use. Although GAN- and diffusion model-based approaches have shown some promise in modality completion, they remain limited in fMRI-dMRI synthesis due to (1) significant BOLD vs. diffusion-weighted signal differences between fMRI and dMRI in time/gradient axis, and (2) inadequate integration of disease-related neuroanatomical patterns during generation. To address these challenges, we propose PDS, introducing two key innovations: (1) a pattern-aware dual-modal 3D diffusion framework for cross-modality learning, and (2) a tissue refinement network integrated with a efficient microstructure refinement to maintain structural fidelity and fine details. Evaluated on OASIS-3, ADNI, and in-house datasets, our method achieves state-of-the-art results, with PSNR/SSIM scores of 29.83 dB/90.84\% for fMRI synthesis (+1.54 dB/+4.12\% over baselines) and 30.00 dB/77.55\% for dMRI synthesis (+1.02 dB/+2.2\%). In clinical validation, the synthesized data show strong diagnostic performance, achieving 67.92\%/66.02\%/64.15\% accuracy (NC vs. MCI vs. AD) in hybrid real-synthetic experiments. Code is available in \href{https://github.com/SXR3015/PDS}{PDS GitHub Repository}