发表机构
Aeronautics Institute of Technology; Science and Technology Institute, Federal University of São Paulo; LASIGE, Faculdade de Ciências, Universidade de Lisboa; University of Coimbra, CISUC/LASI – Centre for Informatics and Systems of the University of Coimbra; Instituto Superior Técnico, Universidade de Lisboa, Instituto de Telecomunicações（航空技术学院; 圣保罗联邦大学科学与技术研究所; 里斯本大学理学院LASIGE实验室; 科英布拉大学CISUC/LASI - 科英布拉大学信息与系统中心; 里斯本大学高等技术学院、电信研究所）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该研究对基于扩散的医学图像修复方法进行系统回顾，涵盖多方面内容并提出分类法。分析显示相关研究兴趣快速增长，扩散模型在生成合理重建结果及辅助临床任务上表现出色，但也面临缺乏标准化基准等挑战。
AI 中文摘要
图像修复旨在重建图像中缺失或损坏的区域，同时尽可能保持视觉和语义一致性。在医学成像中，此任务尤为重要，因为伪影、缺失信息和病理改变会影响诊断可靠性及下游临床应用。近期，扩散模型因其能生成解剖学上一致的重建结果，成为医学图像修复的先进生成方法。本综述对基于扩散的医学图像修复方法进行系统回顾，涵盖60项研究中的主要架构、应用、数据集及评估策略。此外，还提出基于扩散方法的分类法。分析显示，基于扩散的医学图像修复研究兴趣迅速增长，去噪扩散概率模型和潜在扩散模型成为主要架构。综述研究主要集中在伪影去除、数据增强、伪健康组织重建和异常检测，特别是在磁共振成像和计算机断层扫描成像中。总体而言，扩散模型在生成解剖学上合理的重建结果及辅助下游临床任务方面表现出色。然而，综述也强调了重要挑战，包括缺乏标准化基准、数据集多样性有限以及不同临床应用和成像场景下验证程序受限。
英文摘要
Image inpainting aims to reconstruct missing or corrupted regions of an image while preserving as much as possible, visual and semantic consistency. In medical imaging, this task is particularly important because artifacts, missing information, and pathological alterations can compromise diagnostic reliability and downstream clinical applications. Recently, diffusion models have emerged as state-of-the-art generative approaches for medical image inpainting due to their ability to generate anatomically consistent reconstructions. This survey presents a systematic review of diffusion-based methods for medical image inpainting, covering the main architectures, applications, datasets, and evaluation strategies reported across 60 studies. In addition, we propose a taxonomy for diffusion-based approaches. The analysis reveals a rapid growth of research interest in diffusion-based medical image inpainting, with denoising diffusion probabilistic models and latent diffusion models emerging as the dominant architectures. The reviewed studies mainly focus on artifact removal, data augmentation, pseudo-healthy tissue reconstruction, and anomaly detection, particularly in magnetic resonance imaging and computed tomography imaging. Overall, diffusion models demonstrate strong performance in producing anatomically plausible reconstructions and aiding downstream clinical tasks. However, the review also highlights important challenges, including the lack of standardized benchmarks, limited dataset diversity, and restricted validation procedures across diverse clinical applications and imaging scenarios.
