用于提升诊断效果的多模态医学图像融合的技术、算法、进展、挑战与临床应用综合综述
A Comprehensive Review of Techniques, Algorithms, Advancements, Challenges, and Clinical Applications of Multi-modal Medical Image Fusion for Improved Diagnosis
- Interdisciplinary Research Center for Finance and Digital Economy, King Fahd University of Petroleum and Minerals（金融与数字经济交叉研究中心，国王法赫德石油和矿物大学）
- Department of Software Engineering, Faculty of Information Technology, Al-Ahliyya Amman University（软件工程系，信息科技学院，阿尔阿赫利亚大学）
- Department of Information Systems and Operations Management, King Fahd University of Petroleum and Minerals（信息系统与运营管理系，国王法赫德石油和矿物大学）
- ADAPT Research Centre, School of Computer Science, University of Galway（ADAPT研究中心，计算机科学学院，Galway大学）
- Department of Mechanical and Nuclear Engineering, Khalifa University（机械与核工程系，哈利法大学）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
本文是多模态医学图像融合（MMIF）的综合综述，梳理了传统与深度学习类融合方法的差异，介绍了其在多科室的临床应用，分析了落地挑战并指明未来研究方向。
AI中文摘要：
多模态医学图像融合（MMIF）作为计算机辅助诊断系统中提升诊断精度、助力有效临床决策的关键技术，正日益受到认可。MMIF 融合 X 射线、MRI、CT、PET、SPECT 及超声的成像数据，生成展示患者解剖结构与病理特征的精细化、具备临床实用价值的图像。这类整合后的表征可显著提升诊断准确率、病灶检测与分割效果。\n本综述全面梳理了 MMIF 的发展历程、方法论、算法体系、最新进展及临床应用。我们对像素级、特征级、决策级等传统融合方法进行了批判性对比分析，并深入探讨了由深度学习、生成式模型及 Transformer 架构推动的最新研究进展。文章对传统方法与前沿技术展开关键对比，明确了二者在鲁棒性、计算效率及可解释性方面的差异。\n本文梳理了 MMIF 在肿瘤学、神经学、心脏病学领域的广泛临床应用，证明其通过改善个体化治疗效果，在精准医疗中发挥着重要作用。此外，综述深入分析了制约 MMIF 大规模落地的现存挑战，包括数据隐私、数据异质性、计算复杂度、AI 驱动算法的可解释性，以及与临床工作流的整合问题。文章还指出了未来重要的研究方向，例如融合可解释 AI、采用隐私保护型联邦学习框架、研发实时融合系统，以及推进符合监管要求的标准化工作。
英文摘要：
Multi-modal medical image fusion (MMIF) is increasingly recognized as an essential technique for enhancing diagnostic precision and facilitating effective clinical decision-making within computer-aided diagnosis systems. MMIF combines data from X-ray, MRI, CT, PET, SPECT, and ultrasound to create detailed, clinically useful images of patient anatomy and pathology. These integrated representations significantly advance diagnostic accuracy, lesion detection, and segmentation. This comprehensive review meticulously surveys the evolution, methodologies, algorithms, current advancements, and clinical applications of MMIF. We present a critical comparative analysis of traditional fusion approaches, including pixel-, feature-, and decision-level methods, and delves into recent advancements driven by deep learning, generative models, and transformer-based architectures. A critical comparative analysis is presented between these conventional methods and contemporary techniques, highlighting differences in robustness, computational efficiency, and interpretability. The article addresses extensive clinical applications across oncology, neurology, and cardiology, demonstrating MMIF's vital role in precision medicine through improved patient-specific therapeutic outcomes. Moreover, the review thoroughly investigates the persistent challenges affecting MMIF's broad adoption, including issues related to data privacy, heterogeneity, computational complexity, interpretability of AI-driven algorithms, and integration within clinical workflows. It also identifies significant future research avenues, such as the integration of explainable AI, adoption of privacy-preserving federated learning frameworks, development of real-time fusion systems, and standardization efforts for regulatory compliance.
