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相关概念视频

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

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

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...
¹³C NMR: ¹H–¹³C Decoupling01:04

¹³C NMR: ¹H–¹³C Decoupling

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...
Insensitive Nuclei Enhanced by Polarization Transfer (INEPT)01:15

Insensitive Nuclei Enhanced by Polarization Transfer (INEPT)

Insensitive Nuclei Enhanced by Polarization Transfer (INEPT) is an advanced Nuclear Magnetic Resonance (NMR) technique specifically designed to detect and enhance the signals of low-abundance nuclei, such as carbon-13 and nitrogen-15, in small molecules. The fundamental principle behind INEPT is the transfer of polarization from a more abundant and highly polarizable nucleus, typically hydrogen-1, to the low-abundance nucleus of interest. This process effectively boosts the NMR signal of the...
Double Resonance Techniques: Overview01:12

Double Resonance Techniques: Overview

Double resonance techniques in Nuclear Magnetic Resonance (NMR) spectroscopy involve the simultaneous application of two different frequencies or radiofrequency pulses to manipulate and observe two distinct nuclear spins. One important application of double resonance is spin decoupling, which selectively suppresses coupling with one type of nucleus while observing the NMR signal from another nucleus, simplifying the spectrum and enhancing resolution.
Spin decoupling is usually achieved by...

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相关实验视频

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采用深度学习的加速EPR成像,消除噪音.

Irene Canavesi1, Navin Viswakarma1, Boris Epel1,2

  • 1Oxygen Measurement Core, O2M Technologies, LLC, Chicago, Illinois, USA.

Magnetic resonance in medicine
|March 17, 2025
PubMed
概括
此摘要是机器生成的。

深度学习无线化增强了电子磁共振成像 (EPRI) 以更快地绘制氧气图. 这种技术提高了信号噪声比 (SNR),使EPRI更接近临床使用.

关键词:
深度学习,消除恶意.电子偏磁共振成像学 电子偏磁共振缺氧成像检查 缺氧成像检查氧气成像成像技术 氧气成像

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科学领域:

  • 医学成像医学成像
  • 生物物理学的生物物理.
  • 医学中的人工智能

背景情况:

  • 使用Trityl OXO71的脉冲电子磁共振成像 (EPRI) 是有效地绘制组织氧气部分压力 (pO2) 的.
  • 获取高质量的3D EPRI 地图,特别是pO2的3D EPRI 地图,可能会耗时,因为需要多个信号平均值来实现足够的信号噪声比 (SNR).
  • 深度学习为加速图像采集和改善各种医学成像模式的图像质量提供了潜力.

研究的目的:

  • 应用深度学习技术来消除3DEPRI振幅和pO2地图的模糊性.
  • 评估不同神经网络架构的有效性,特别是带有联合双边过器 (JBF) 的UNet,以提高EPRI图像质量.
  • 为了确定深度学习是否可以在不影响图像质量的情况下加速EPRI获取.

主要方法:

  • 使用MONAI实现了四个神经网络 (自动编码器,注意力UNet,UNETR,UNet),并对227张3DEPRI图像 (体内和体外) 的数据集进行了测试.
  • 性能最好的模型UNet用双边联合波器 (JBF) 进行了增强,并接受了训练,以提高图像SNR,同时保持结构相似性和边缘细节.
  • 优化的UNet+JBF模型使用体外幽灵和体内小鼠瘤数据进行了验证,这些数据采用了不同数量的平均值 (15,30,150).

主要成果:

  • 拥有2个JBF层 (UNet+JBF2) 的UNet模型在消除和增强EPRI图像方面表现最好.
  • UNet+JBF2模型在15次拍摄的幅度图中实现了较高的SNR,而不是150次拍摄的预过图,从而使成像加速10倍.
  • 深度学习算法显著改善了幽灵和体内瘤数据中的振幅和pO2图的SNR.

结论:

  • 深度学习技术,特别是UNet+JBF2模型,对于拒绝3DEPRI数据是有效的.
  • 开发的方法显著改善了图像SNR,并允许加速数据采集.
  • 这一进步使EPRI技术更接近氧气测绘的常规临床应用.