基于超vise辅助的多模态融合扩散模型用于 PET 图像恢复

正电子断层扫描（PET）提供强大的功能成像，但涉及辐射暴露。通过降低放射性示踪剂剂量或扫描时间来减少这种暴露的努力可能会降低图像质量。虽然使用磁共振（MR）图像与更清晰的解剖信息来恢复标准剂量 PET（SPET） from low-dose PET（LPET）是一种有前景的 approach, but it faces challenges with the inconsistencies in the structure and texture of multi-modality fusion, as well as the mismatch in out-of-distribution (OOD) data. In this paper, we propose a supervise-assisted multi-modality fusion diffusion model (MFdiff) for addressing these challenges for high-quality PET restoration. Firstly, to fully utilize auxiliary MR images without introducing extraneous details in the restored image, a multi-modality feature fusion module is designed to learn an optimized fusion feature. Secondly, using the fusion feature as an additional condition, high-quality SPET images are iteratively generated based on the diffusion model. Furthermore, we introduce a two-stage supervise-assisted learning strategy that harnesses both generalized priors from simulated in-distribution datasets and specific priors tailored to in-vivo OOD data. Experiments demonstrate that the proposed MFdiff effectively restores high-quality SPET images from multi-modality inputs and outperforms state-of-the-art methods both qualitatively and quantitatively.