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相关概念视频

Computed Tomography01:10

Computed Tomography

8.0K
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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¹³C NMR: Distortionless Enhancement by Polarization Transfer (DEPT)01:20

¹³C NMR: Distortionless Enhancement by Polarization Transfer (DEPT)

1.6K
When proton-coupled carbon-13 spectra are simplified by a broadband proton decoupling technique, structural information about the coupled protons is lost. Distortionless enhancement by polarization transfer (DEPT) is a technique that provides information on the number of hydrogens attached to each carbon in a molecule. While the DEPT experiment utilizes complex pulse sequences, the pulse delay and flip angle are specifically manipulated. The resulting signals have different phases depending on...
1.6K
¹³C NMR: ¹H–¹³C Decoupling01:04

¹³C NMR: ¹H–¹³C Decoupling

1.7K
The probability of having two carbon-13 atoms next to each other is negligible because of the low natural abundance of carbon-13. Consequently, peak splitting due to carbon-carbon spin-spin coupling is not observed in spectra. However, protons up to three sigma bonds away split the carbon signal according to the n+1 rule, resulting in complicated spectra.
A broadband decoupling technique is used to simplify these complex, sometimes overlapping, signals. Broadband decoupling relies on a...
1.7K

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相关实验视频

Updated: Jan 11, 2026

3D Imaging of Soft-Tissue Samples using an X-ray Specific Staining Method and Nanoscopic Computed Tomography
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基于CBCT的少数拍摄合成CT生成与无声扩散概率模型.

Ping Lin Yeap1, Xin Du2, Meng Zhou3

  • 1Department of Oncology, University of Cambridge, Cambridge, UK.

Medical physics
|November 13, 2025
PubMed
概括

这项研究引入了一种新的无噪声扩散概率模型,用于从束CT (CBCT) 生成合成CT (sCT) 图像,使用最小的训练数据. 该方法显著提高了图像质量,并通过允许准确的剂量评估,有助于适应性放射治疗.

关键词:
适应性放射疗法适应性放射疗法圆光束CT CT 的情况.扩散模型的扩散模型.合成CT生成的合成CT生成

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相关实验视频

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科学领域:

  • 医疗成像医学成像
  • 辐射疗法 辐射疗法
  • 人工智能的人工智能

背景情况:

  • 适应性放射治疗需要经常根据通过室内成像观察到的解剖变化重新优化治疗计划.
  • 室内形光束CT (CBCT) 提供每日监测,但图像质量差,Hounsfield Unit (HU) 值不准确,妨碍准确的剂量评估.
  • 现有的CBCT-to-CT生成模型通常需要广泛的数据集进行培训,这对临床实施构成了挑战.

研究的目的:

  • 开发一种数据效率高的方法,使用无噪声扩散概率模型 (DDPM) 来从CBCT生成高保真度合成CT (sCT) 图像.
  • 为了实现sCT生成的少数射击学习,减少对大型培训数据集的依赖.

主要方法:

  • 一个DDPM被训练在刚性注册的CT-CBCT图像对上,消除了复杂可变形注册的需要.
  • 采用了利用通道和噪声调节的修改采样过程,利用CBCT和规划CT (pCT) 表示之间的潜空间收.
  • 该模型在有限的数据集 (25名头癌患者) 上进行了训练,并在单独的患者队列上进行了验证/测试,证明了几次射击的学习能力.

主要成果:

  • 在头癌患者中,生成的sCT显著优于CBCT,将掩盖平均绝对误差 (MAE) 从131 HU降至49 HU.
  • 图像质量指标得到了大幅改善,峰值信号噪声比 (PSNR) 从20.0dB增加到22.9dB,正常化交叉相关性 (NCC) 从0.93提高到0.96.
  • 该方法在不同的解剖部位 (骨盆) 和数据集中证明了可通用性,在重新培训后实现了可比性能,并显示临床医生更喜欢sCTs而不是pCTs进行分数评估.

结论:

  • 拟议的DDPM提供了一种实用,数据效率高,站点稳固的解决方案,用于从CBCT图像中生成高保真度的sCT.
  • 这种方法促进了基于CBCT的准确剂量评估和在适应性放射治疗工作流程中的计划调整.
  • 该方法在有限的数据上表现良好的能力使其成为临床环境中宝贵的工具.