{"ID":2844787,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2511.04963","arxiv_id":"2511.04963","title":"Pattern-Aware Diffusion Synthesis of fMRI/dMRI with Tissue and Microstructural Refinement","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}","short_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, th...","url_abs":"https://arxiv.org/abs/2511.04963","url_pdf":"https://arxiv.org/pdf/2511.04963v1","authors":"[\"Xiongri Shen\",\"Jiaqi Wang\",\"Yi Zhong\",\"Zhenxi Song\",\"Leilei Zhao\",\"Yichen Wei\",\"Lingyan Liang\",\"Shuqiang Wang\",\"Baiying Lei\",\"Demao Deng\",\"Zhiguo Zhang\"]","published":"2025-11-07T03:51:00Z","proceeding":"cs.CV","tasks":"[\"cs.CV\",\"cs.AI\"]","methods":"[\"Diffusion Model\",\"Generative Adversarial Network\"]","has_code":false,"code_links":[{"ID":607323,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_id":2844787,"paper_url":"https://arxiv.org/abs/2511.04963","paper_title":"Pattern-Aware Diffusion Synthesis of fMRI/dMRI with Tissue and Microstructural Refinement","repo_url":"https://github.com/SXR3015/PDS","is_official":false,"mentioned_in_paper":false,"mentioned_in_github":true,"github_stars":0}]}