对质子MR光谱工作流程的机器学习应用程序的审查
Dennis M J van de Sande1, Julian P Merkofer2, Sina Amirrajab1
1Department of Biomedical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands.
Magnetic resonance in medicine
|July 4, 2023
概括
质子磁共振光谱 (MRS) 中的机器学习 (ML) 正在迅速发展,主要集中在数据处理和分析上. 需要进一步的研究来解决临床应用的概括问题和风险.
科学领域:
- 医学成像和光谱学
- 医疗保健中的人工智能
- 生物医学数据分析
背景情况:
- 质子磁共振光谱 (MRS) 是一种强大的非侵入性技术,用于代谢分析.
- 机器学习 (ML) 在MRS中的集成是一个不断增长的领域,有可能增强数据分析和解释.
- 需要对MRS当前的ML应用进行结构化的概述,以指导未来的研发.
研究的目的:
- 为机器学习应用在质子MRS中提供全面的文献综述.
- 根据MRS工作流来对ML方法进行分类:数据采集,处理,分析和人工数据生成.
- 确定目前的趋势,挑战和MRS中的ML的未来方向.
主要方法:
- 在2017年至2023年期间在主要的MR期刊上发表的研究的系统文献搜索.
- 根据MRS工作流程阶段对审查的研究进行分类.
- 与ML模型架构,数据生成和性能相关的发现的定性综合.
主要成果:
- 在MRS中的ML应用主要是在处理和分析阶段,仅限于数据采集.
- 许多研究都使用了类似的ML模型架构,而没有探索其他替代方案.
- 人工数据生成缺乏标准化的方法,并且往往对体内数据的概括性很差.
- 在MRS中ML的临床应用需要解决风险,包括输出不确定性和模型偏差.
结论:
- 质子MRS中的机器学习是一个具有重大潜力的新兴领域,特别是在数据处理和分析方面.
- 人工数据生成的标准化和对体内数据的严格验证至关重要.
- 调查模型的不确定性和偏差对于基于ML的MRS工具的安全和可靠的临床翻译至关重要.
- 继续进行研究是有必要的,以充分实现ML在推进MRS应用中的好处.
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