arXiv 2602.082492026-02-09eess.IVcs.CV

基于去噪扩散模型的多模态图像重建与合成统一框架

A Unified Framework for Multimodal Image Reconstruction and Synthesis using Denoising Diffusion Models

Department of Computer Science and Engineering, Washington University in St. Louis（华盛顿大学圣路易斯分校计算机科学与工程系）；Mallinckrodt Institute of Radiology, Washington University in St. Louis（华盛顿大学圣路易斯分校马林克罗德特放射医学研究所）；Department of Electrical and Systems Engineering, Washington University in St. Louis（华盛顿大学圣路易斯分校电气与系统工程系）；Department of Neurology, Washington University in St. Louis（华盛顿大学圣路易斯分校神经病学系）；Department of Biomedical Engineering, Washington University in St. Louis（华盛顿大学圣路易斯分校生物医学工程系）；Division of Biology and Biomedical Sciences, Washington University in St. Louis（华盛顿大学圣路易斯分校生物学与生物医学科学 division）；Department of Electrical and Computer Engineering, University of Wisconsin–Madison（威斯康星大学麦迪逊分校电气与计算机工程系）

Weijie Gan, Xucheng Wang, Tongyao Wang, Wenshang Wang, Chunwei Ying, Yuyang Hu, Yasheng Chen, Hongyu An, Ulugbek S. Kamilov

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AI总结：

Any2all通过统一框架实现多模态图像重建与合成，利用去噪扩散模型提升性能与质量。

摘要：

图像重建和图像合成对于处理不完整的多模态成像数据至关重要，但现有方法需要各种任务特定的模型，这使训练和部署流程复杂化。我们引入Any2all，一种统一的框架，通过将这些不同的任务视为一个单一的虚拟修复问题来解决这一限制。我们在一个完整的多模态数据堆上训练一个单一的无条件扩散模型。然后在推理时间将该模型适应以“修复”所有目标模态从任何组合的输入中获取的干净图像或噪声测量。我们在PET/MR/CT脑数据集上验证了Any2all。我们的结果表明，Any2all在多模态重建和合成任务中都能取得优异表现，持续产生具有竞争性失真性能和优于专门方法的高质量图像。

英文摘要：

Image reconstruction and image synthesis are important for handling incomplete multimodal imaging data, but existing methods require various task-specific models, complicating training and deployment workflows. We introduce Any2all, a unified framework that addresses this limitation by formulating these disparate tasks as a single virtual inpainting problem. We train a single, unconditional diffusion model on the complete multimodal data stack. This model is then adapted at inference time to ``inpaint'' all target modalities from any combination of inputs of available clean images or noisy measurements. We validated Any2all on a PET/MR/CT brain dataset. Our results show that Any2all can achieve excellent performance on both multimodal reconstruction and synthesis tasks, consistently yielding images with competitive distortion-based performance and superior perceptual quality over specialized methods.

机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。