使用Primal-Dual UNet进行下采样CT和放射性MRI重建的sinogram上采样,用于下采样CT和放射性MRI重建
Philipp Ernst1, Soumick Chatterjee2, Georg Rose3
1Data and Knowledge Engineering Group, Faculty of Computer Science, Otto von Guericke University Magdeburg, Germany; Research Campus STIMULATE, Otto von Guericke University Magdeburg, Germany.
概括
这项研究引入了一种统一的深度学习方法,Primal-Dual UNet,用于重建低样本计算机断层扫描 (CT) 和磁共振成像 (MRI) 数据. 这种新的方法显著提高了两种模式的图像质量和重建速度.
科学领域:
- 医疗成像医学成像
- 人工智能的人工智能
- 图像重建 图像的重建
背景情况:
- 计算机断层扫描 (CT) 使用电离辐射,而磁共振成像 (MRI) 采集速度较慢.
- 在CT和MRI中低采样可以减轻这些问题,但通常会导致更低的分辨率和文物.
- 现有的重建方法通常分别处理CT和MRI.
研究的目的:
- 开发一个统一的深度学习解决方案,用于重建稀疏采样CT和低采样辐射MRI数据.
- 在准确性和速度方面改进现有的图像重建深度学习方法.
- 评估拟议方法在CT和MRI数据集上的性能,包括特定感兴趣的区域.
主要方法:
- 采用基于富里埃变换的预处理进行辐射核磁共振和以过后投影为两种模式的阴影图上采样的统一方法.
- 实施Primal-Dual UNet,这是一个基于Primal-Dual网络的增强深度学习模型.
- 验证使用风扇光束CT数据的稀疏度水平为16和低样本的大脑和腹部MRI数据的加速度因子为16.
主要成果:
- 原始-双元UNet在结构相似性指数测量 (SSIM) 中实现了统计学上显著的改善,用于稀疏的CT重建 (0.932±0.021对比0.919±0.016).
- 对于低样本MRI,该模型为大脑 (0.903±0.019对比0.867±0.025) 和腹部数据 (0.957±0.023对比0.949±0.025) 提供了改善的平均SSIM得分.
- 该网络在感兴趣的区域 (肝脏,脏,脏) 显示出更好的图像质量,并且在存在诸如针头之类的工件的情况下,可以更好地概括.
结论:
- 拟议的Primal-Dual UNet提供了一个统一且有效的解决方案,用于重建低采样CT和MRI数据.
- 与以前的模型相比,该方法显著提高了图像质量和重建速度.
- 这种统一的方法有望通过解决当前模式的局限性来推进非侵入性诊断成像.
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