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基于深度学习的数字放射学降噪算法的定量评估,使用噪声功率频谱分析

Sho Maruyama1, Hiroki Saitou2

  • 1Department of Radiological Technology, Gunma Prefectural College of Health Sciences, Maebashi, Gunma, Japan.

Journal of applied clinical medical physics
|February 25, 2026
PubMed
概括

数字放射学 (DR) 中的深度学习降噪 (DLNR) 在低剂量下表现优于传统方法. 详细的频率分析揭示了明显的抑制噪声行为,突出了需要剂量依赖评估的必要性.

关键词:
基于深度学习的降噪方法数字辐射学数字辐射学剂量依赖的性能表现根据频率依赖的评估.噪声功率频谱 噪声功率频谱

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相关概念视频

Downsampling01:20

Downsampling

When considering a sampled sequence with zero values between sampling instants, one can replace it by taking every N-th value of the sequence. At these integer multiples of N, the original and sampled sequences coincide. This process, known as decimation, involves extracting every N-th sample from a sequence, thereby creating a more efficient sequence.
The Fourier transform of the decimated sequence reveals a combination of scaled and shifted versions of the original spectrum. This...
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