在扫描仪独立的MRI序列中,人工智能驱动和自动化的MRI序列优化,该序列由特定领域的语言制定
Daniel Christopher Hoinkiss1, Jörn Huber1, Christina Plump2
1Fraunhofer Institute for Digital Medicine MEVIS, Imaging Physics, Bremen, Germany.
Frontiers in neuroimaging
|August 9, 2023
概括
机器学习 (ML) 自动化了磁共振成像 (MRI) 序列的优化. 特定领域的语言和ML模型创建最佳的MRI协议,减少扫描时间和改善临床结果.
科学领域:
- 医疗成像医学成像
- 机器学习 机器学习
- 计算科学 计算科学
背景情况:
- 磁共振成像 (MRI) 序列选择需要专门的知识.
- 复杂的MRI协议可能会导致低于最佳的成像和延长扫描时间.
研究的目的:
- 使用机器学习自动选择和优化MRI序列和协议.
- 为定义MRI序列和临床要求开发特定领域的语言 (DSL).
主要方法:
- 定义了MRI序列描述和优化需求的DSL.
- 使用了一个独立于供应商的MRI框架 (gammaSTAR) 和Pulseq格式.
- 通过模拟 (JEMRIS) 来生成训练数据,将序列与指标联系起来.
- 应用ML技术来学习顺序与指标之间的关系以进行优化.
主要成果:
- ML有效地捕捉了MRI序列输入和模拟输出之间的关系.
- 进化算法在基于训练数据的MRI序列优化方面取得了成功.
- 模拟的MRI数据与获得的数据和初始要求密切匹配.
结论:
- 这种方法为各种临床需求提供了最佳MRI解决方案的途径.
- 通过避免低效的MRI协议,有可能减少患者扫描时间.
- 未来的工作包括提高DSL灵活性和优化模拟/优化流程.
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