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 significant BOLD vs. diffusion-weighted signal differences and inadequate integration of disease-related neuroanatomical patterns during generation. To address these challenges, the paper proposes PDS, introducing a pattern-aware dual-modal 3D diffusion framework for cross-modality learning and a tissue refinement network integrated with efficient microstructure refinement to maintain structural fidelity and fine details. Evaluated on OASIS-3, ADNI, and in-house datasets, the method reports state-of-the-art synthesis and clinical validation results.

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 significant BOLD vs. diffusion-weighted signal differences and inadequate integration of disease-related neuroanatomical patterns during generation. To address these challenges, the paper proposes PDS, introducing a pattern-aware dual-modal 3D diffusion framework for cross-modality learning and a tissue refinement network integrated with efficient microstructure refinement to maintain structural fidelity and fine details. Evaluated on OASIS-3, ADNI, and in-house datasets, the method reports state-of-the-art synthesis and clinical validation results.

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 significant BOLD vs. diffusion-weighted signal differences and inadequate integration of disease-related neuroanatomical patterns during generation. To address these challenges, the paper proposes PDS, introducing a pattern-aware dual-modal 3D diffusion framework for cross-modality learning and a tissue refinement network integrated with efficient microstructure refinement to maintain structural fidelity and fine details. Evaluated on OASIS-3, ADNI, and in-house datasets, the method reports state-of-the-art synthesis and clinical validation results.

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 significant BOLD vs. diffusion-weighted signal differences and inadequate integration of disease-related neuroanatomical patterns during generation. To address these challenges, the paper proposes PDS, introducing a pattern-aware dual-modal 3D diffusion framework for cross-modality learning and a tissue refinement network integrated with efficient microstructure refinement to maintain structural fidelity and fine details. Evaluated on OASIS-3, ADNI, and in-house datasets, the method reports state-of-the-art synthesis and clinical validation results.