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相关概念视频

Reconstruction of Signal using Interpolation01:10

Reconstruction of Signal using Interpolation

254
Signal processing techniques are essential for accurately converting continuous signals to digital formats and vice versa. When a continuous signal is sampled with a period T, the resulting sampled signal exhibits replicas of the original spectrum in the frequency domain, spaced at intervals equal to the sampling frequency. To handle this sampled signal, a zero-order hold method can be applied, which creates a piecewise constant signal by retaining each sample's value until the next...
254

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相关实验视频

Updated: Jul 27, 2025

Protocol for the Evaluation of MRI Artifacts Caused by Metal Implants to Assess the Suitability of Implants and the Vulnerability of Pulse Sequences
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神经网络指导的sinogram-domain代算法用于人工物减少.

Gengsheng L Zeng1,2

  • 1Department of Computer Science, Utah Valley University, Salt Lake City, Utah, USA.

Medical physics
|June 6, 2023
PubMed
概括
此摘要是机器生成的。

这项研究引入了一种新的神经网络方法,用于在X射线CT扫描中减少金属工件,当底层物理不清楚时. 该方法有效地减少了文物,提高了计算机断层扫描任务中的图像质量.

关键词:
文物 文物 文物计算机断层扫描 (CT) 是一种计算机断层扫描.图像重建 图像重建代算法是一种代算法.神经网络的神经网络的神经网络

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科学领域:

  • 医疗成像医学成像
  • 人工智能的人工智能
  • 计算科学 计算科学

背景情况:

  • 在X射线CT中,金属工件由于复杂,未建模的物理,特别是未知的材料和广泛的X射线光谱带来了挑战.
  • 在许多现实世界的场景中,对文物创造的准确数学建模是很困难的.

研究的目的:

  • 开发和评估一种基于神经网络的客观函数,用于计算机断层扫描 (CT) 中的代工件减少.
  • 解决文物减少问题,当文物创建模型未知或难以数学定义时.

主要方法:

  • 一个卷积神经网络被训练,使用一个假设的,不可预测的投影数据扭曲模型来识别文物.
  • 训练有素的神经网络作为CT中的代工件减少算法的目标函数.
  • 使用梯度下降优化,通过链条规则计算梯度,评估图像域中的目标函数.

主要成果:

  • 学习曲线显示,随着代的增加,目标函数的下降,表明了趋同.
  • 处理后的图像显示金属文物显著减少.
  • 使用总平方差 (SSD) 度量的定量分析证实了该方法的有效性.

结论:

  • 利用神经网络作为客观函数为复杂的场景中,在物理建模具有挑战性时,为人工物减少提供了一个有希望的解决方案.
  • 这种方法具有在医学成像和其他需要人工物减轻的领域的真实应用的潜力.