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医学成像在神经系统疾病中的数据融合

Golrokh Mirzaei¹, Aaditya Gupta¹, Hojjat Adeli²

¹Department of Computer Science and Engineering, The Ohio State University, Columbus, OH, 43302, USA.

Reviews in the neurosciences

|September 18, 2025

概括

数据融合整合了各种医学成像数据,提高了神经系统疾病等疾病的诊断准确度. 这种方法结合了多种观点和模式,以获得更全面的患者理解.

科学领域:

医学成像分析分析医学成像分析
计算神经科学是一种计算神经科学.
生物医学工程生物医学工程

背景情况:

医学成像对于诊断和预后至关重要,但没有一种单一的模式能够捕获所有必要的信息.
整合来自不同来源 (方法,观点,尺度) 的数据对于全面的临床分析至关重要.
数据融合提供了一个强大的解决方案,可以将不同成像技术的互补见解结合起来.

研究的目的:

提供医学成像中的数据融合方法的概述.
强调数据融合在诊断神经系统疾病中的应用.
突出自2016年以来数据融合的最新进展和关键研究.

主要方法:

对多视图,多模式和多规模数据融合策略的审查.
分析各种成像方式 (MRI,CT,PET,SPECT,EEG,MEG) 的应用.
专注于跨学科研究和该领域的最新进展.

主要成果:

数据融合显著提高了临床分析的精度和可靠性.
综合成像数据提供了对医疗条件的更全面的了解.
最近的进展证明了数据融合在诊断中的日益增长的影响和潜力.

结论:

数据融合对于克服个人医学成像模式的局限性至关重要.
多视图,多模式和多尺度数据的整合提高了诊断和预后能力.
数据融合领域的持续研究有望带来进一步的突破,特别是在理解神经系统疾病方面.

关键词:

数据融合数据融合深度学习是一种深度学习.机器学习是机器学习.医学成像医学成像神经系统疾病神经系统疾病

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