arXiv 2602.115452026-02-12cs.CV

监督辅助多模态融合扩散模型用于PET修复

Supervise-assisted Multi-modality Fusion Diffusion Model for PET Restoration

Beijing Institute of Technology（北京理工大学）；Research and Development Center of Agricultural Bank of China（中国农业银行研发中心）；Peking University（北京大学）；Beijing Normal University（北京师范大学）

Yingkai Zhang, Shuang Chen, Ye Tian, Yunyi Gao, Jianyong Jiang, Ying Fu

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AI总结：

本文提出监督辅助多模态融合扩散模型，用于从低剂量PET和MR图像中恢复高质量SPET图像，通过优化特征融合和两阶段监督学习策略提升修复效果。

摘要：

正电子发射断层扫描（PET）提供了强大的功能成像，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暴露的努力可能会降低图像质量。虽然使用磁共振（MR）图像来从低剂量PET（LPET）中恢复标准剂量PET（SPET）是一个有前途的方法，但多模态融合中结构和纹理的一致性以及分布外（OOD）数据的不匹配带来了挑战。在本文中，我们提出了一种监督辅助多模态融合扩散模型（MFdiff），以解决这些挑战，实现高质量的PET修复。首先，为了充分利用辅助MR图像而不引入恢复图像中的多余细节，设计了一个多模态特征融合模块，以学习优化的融合特征。其次，利用融合特征作为额外条件，基于扩散模型迭代生成高质量的SPET图像。此外，我们引入了一种两阶段监督辅助学习策略，利用来自模拟分布内数据集的通用先验和针对体内OOD数据定制的特定先验。实验表明，所提出的MFdiff能够从多模态输入中高效恢复高质量的SPET图像，并在定性和定量上均优于最先进的方法。

英文摘要：

Positron emission tomography (PET) offers powerful functional imaging but involves radiation exposure. Efforts to reduce this exposure by lowering the radiotracer dose or scan time can degrade image quality. While using magnetic resonance (MR) images with clearer anatomical information to restore standard-dose PET (SPET) from low-dose PET (LPET) is a promising approach, 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.

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