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基于深度学习的时间缩短纠正在脑电脑断层扫描输液中.

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  • 1Department of Radiological Technology, School of Health Sciences, Faculty of Medicine, Niigata University, 2-746 Asahimachi-Dori, Chuo-ku, Niigata, 951-8518, Japan. ichikawa@clg.niigata-u.ac.jp.

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一种新的卷积神经网络 (CNN) 方法准确地预测了脑电脑断层扫描 (CTP) 输液成像中缺失的. 一次性预测可以最大限度地减少时间截断的错误,从而提高缺血性中风的量化.

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3D U-Net 是一个 3D U-Net.通过CT perfusion进行CT perfusion,使得人体内产生更多的光线.深度学习是一种深度学习.时间切断时间切断.时间序列预测预测.

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科学领域:

  • 医疗成像医学成像
  • 人工智能在医学中的应用
  • 神经学 神经学

背景情况:

  • 大脑计算机断层扫描输液 (CTP) 成像对于诊断缺血性中风至关重要.
  • 临床CTP获取往往遭受时间截断,导致不完整的对比玻尿酸数据.
  • 不完整的数据可能会损害 perfusion 参数量化的准确性.

研究的目的:

  • 开发和评估一种深度学习方法,用于预测缺失的CTP图像.
  • 为了比较不同的预测策略来处理CTP中的时间截断.
  • 评估预测方法对图像质量和临床参数的影响.

主要方法:

  • 一个三维卷积神经网络 (CNN) 被训练来预测CTP系列的最后10.
  • 72个CTP扫描被用于训练和测试CNN模型.
  • 评估了三个预测策略:一次性,递归多步骤和直接递归混合预测.

主要成果:

  • 单拍预测,同时预测所有缺失的,与递归方法相比,产生了优越的图像质量指标.
  • 单一射击方法显示了最大的相关性 (r=0.990) 和 bolus 形状分析中最小的差异.
  • 来自单一射击预测的 perfusion 参数显示了与基本真相最小的绝对差异.

结论:

  • 拟议的基于CNN的方法有效地预测了缺失的CTP,减轻了时间截断问题.
  • 一次性预测是准确的CTP数据重建和分析的最佳策略.
  • 这种方法有可能最大限度地减少错误并提高缺血性中风的量化.