深度学习管道用于MRSI频谱的质量过
Mladen Rakić1,2, Federico Turco3, Guodong Weng3
1Research and Development, Icometrix, Leuven, Belgium.
NMR in biomedicine
|July 31, 2023
概括
这项研究引入了一种人工智能驱动的管道,自动预处理大脑磁共振光谱成像 (MRSI) 数据,过噪音并识别可用于代谢物量化的可用光谱.
科学领域:
- 神经成像是一种神经成像.
- 医疗人工智能 医疗人工智能
- 频谱学是一种光谱学.
背景情况:
- 3D磁共振光谱成像 (MRSI) 技术的进步使得能够捕捉到许多大脑光谱.
- 临床MRSI需要自动化预处理来管理大型数据集并确保数据质量.
- 在代谢物量化之前,过质量差和识别可挽救的光谱至关重要.
研究的目的:
- 为MRSI光谱质量评估开发基于深度学习的自动化管道.
- 过噪音和受脂污染的光谱,同时保留临床相关的代谢信息.
- 为了减少MRSI数据分析中的手动干预和计算时间.
主要方法:
- 开发了一组深度学习分类器,包括卷积自编码器和多层感知子.
- 该模型是从健康和脑瘤患者的数据中训练了36338个光谱,这些数据是使用SLOW编辑的7T MRSI获得的.
- 使用多数投票方案,Spectra被分为四个质量类别:噪音,脂质主导,轻度脂质污染和优质.
主要成果:
- 人工智能管道在光谱分类方面获得了高F1分数:0.96 (噪音),0.93 (脂质主导),0.82 (轻度脂质污染) 和0.90 (质量良好).
- 该模型通过对真实,想象和组合的光谱表示进行训练来证明其稳定性.
- 管道有效地减少了用户交互,并减少了代谢物量化的计算时间.
结论:
- 拟议的深度学习管道为MRSI光谱质量控制提供了一个高效和自动化的解决方案.
- 该方法通过专注于临床相关的光谱数据,提高了代谢物量化的可靠性.
- 自动化方法通过确保数据完整性,支持先进的MRSI技术的临床翻译.
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