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Computed Tomography01:10

Computed Tomography

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Tomography refers to imaging by sections. Computed tomography (CT) is a non-invasive imaging technique that uses computers to analyze several cross-sectional X-rays to reveal minute details about structures in the body.
The technique was invented in the 1970s and is based on the principle that as X-rays pass through the body, they are absorbed or reflected at different levels. In the technique, a patient lies on a motorized platform while a computerized axial tomography (CAT) scanner rotates...
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在基于深度学习的CT降噪中使用虚拟成像试验方法进行图像质量评估:对比度依赖的空间分辨率.

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  • 1Department of Radiology, Mayo Clinic, Rochester, Minnesota, USA.

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概括

深度学习图像重建方法在较低的CT对比度和剂量水平下降空间分辨率. 这种基于患者数据的框架准确地评估了临床环境中的深度卷积神经网络 (DCNN) 性能.

关键词:
计算机断层扫描 (CT) 是一种计算机断层扫描.深度卷积神经网络 (DCNN) 是一个深度卷积神经网络.深度学习图像重建和降噪 (DLIR)图像质量 图像质量的质量虚拟成像试验试验 虚拟成像试验

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科学领域:

  • 医学成像物理 医学成像物理
  • 计算成像技术的成像
  • 放射学 放射学是一门学科.

背景情况:

  • 基于深度学习的图像重建和降噪 (DLIR) 方法越来越多地用于临床CT.
  • 评估DLIR性能具有挑战性,因为幻影研究可能不反映现实患者数据的结果.
  • DLIR模型主要是通过患者图像进行训练,因此需要以患者数据为导向的评估.

研究的目的:

  • 开发基于患者数据的虚拟成像试验框架,用于评估DLIR方法.
  • 应用这个框架来测量特定DLIR技术的空间分辨率特性.

主要方法:

  • 在各种对比度和剂量水平下,模拟的病变和噪音被插入到患者投影数据中.
  • 深度卷积神经网络 (DCNN) 和其他重建方法处理了数据.
  • 用多个噪声实现的集成平均计算来计算平均损伤信号.
  • 调制转移函数 (MTF) 的计算用于评估平面内和z轴的空间分辨率.

主要成果:

  • DCNN的空间分辨率降低,对比度降低,辐射剂量降低,光强度增加.
  • 在25%的剂量和-10 HU对比度下,DCNN与FBP相比显示了59.5%的平面和4.1%的z轴MTF减少.
  • 空间分辨率排名通常遵循:FBP>DCNN-弱>IR>DCNN-中>DCNN-强,特别是在较低的对比度/剂量时.

结论:

  • 一个基于患者数据的新型虚拟成像试验框架被成功开发和应用.
  • 与其他非线性技术一样,DCNN方法在低对比度,低剂量和高无色化条件下降低了空间分辨率.
  • 该框架提供了一种可靠的方法,用于使用患者特定数据评估DLIR性能.