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基于DL的超分辨率重建的心脏MR功能分析:在临床环境中应用.

Franziska Adomat1, Christof Schaub2,3, Tobias Hoh4

  • 1Department of Radiology and Nuclear Medicine, Cantonal Hospital Winterthur, Winterthur, Switzerland. franziska.adomat@ksw.ch.

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概括

基于深度学习的超分辨率重建 (CS-SR) 显著减少心脏MRI获取时间,而不会影响左心室 (LV) 体积. 这种先进的技术有望简化心脏成像程序.

关键词:
心脏磁力共振成像 (MRI)电影影像成像技术压缩的感觉 压缩的感觉深度学习是一种深度学习.重建算法的算法重建算法超级分辨率的超级分辨率

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科学领域:

  • 心血管成像 - 心血管成像
  • 医学物理 医学物理
  • 人工智能在医学中的应用

背景情况:

  • 心脏磁共振成像 (MRI) 对于诊断心肌病症至关重要.
  • 标准化灵敏度编码 (SENSE) 和压缩的SENSE (C-SENSE) 用于加速MRI采集.
  • 深度学习为提高图像质量和减少扫描时间提供了潜力.

研究的目的:

  • 为了比较心脏MRI的体积,图像质量和采集时间,使用SENSE与基于深度学习的超分辨率 (CS-SR) 从C-SENSE数据的重建.
  • 评估CS-SR用于常规心脏成像的可行性.

主要方法:

  • 对31次心脏MRI检查 (1.5T Philips Ingenia) 的回顾性分析.
  • 使用SENSE (R=2) 和C-SENSE (R=4) 加速技术进行获取.
  • 用基于深度学习的破解和超分辨率算法 (CS-SR) 重建C-SENSE数据.
  • 手动左心室 (LV) 细分和体积分析,由盲阅读器执行.
  • 图像质量通过三个独立读者使用利克特尺度进行评估.

主要成果:

  • 对于LV体积,SENSE和CS-SR之间的高相关性 (r=0.98-1.00),在终端透气或终端静脉体积上没有显著差异.
  • 可比的整体主观图像质量 (p=0.061),CS-SR显示了提高的清晰度,但增加了文物 (p<0.001).
  • 与SENSE (411.1s) 相比,使用C-SENSE (165.6s) 显著减少了获取时间 (p<0.001).

结论:

  • 基于深度学习的超分辨率重建 (CS-SR) 有效地缩短了心脏MRI采集时间.
  • CS-SR保留了LV体积分析的准确性,这对于评估心脏功能至关重要.
  • CS-SR展示了高效和高质量的心脏MRI的潜力,简化了临床工作流程.