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Gauss's law states that the electric flux through any closed surface equals the net charge enclosed within the surface. This law is beneficial for determining the expressions for the electric field for a particular charge distribution if the electric flux is known.
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The German physicist Heinrich Hertz (1857–1894) was the first to generate and detect certain types of electromagnetic waves in the laboratory. Starting in 1887, he performed a series of experiments that confirmed the existence of electromagnetic waves and verified that they travel at the speed of light. Hertz used an alternating-current RLC (resistor-inductor-capacitor) circuit that resonated at a known frequency and connected it to a loop of wire. High voltages induced across the gap in...
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Shortly after de Broglie published his ideas that the electron in a hydrogen atom could be better thought of as being a circular standing wave instead of a particle moving in quantized circular orbits, Erwin Schrödinger extended de Broglie’s work by deriving what is now known as the Schrödinger equation. When Schrödinger applied his equation to hydrogen-like atoms, he was able to reproduce Bohr’s expression for the energy and, thus, the Rydberg formula governing hydrogen spectra.
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The arithmetic mean is usually skewed towards the larger values in the data set. Therefore, to avoid this inherent bias towards smaller values, the harmonic mean is used.
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Updated: Jul 4, 2025

Author Spotlight: Unveiling the Potential of VSFG Microscopy in Studying Mesoscopically Heterogeneous Self-Assembled Structures
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对于使用基于物理的生成对抗网络来减少空间覆盖率的QSM,波场扩展使用了物理信息生成对抗网络.

Siyun Jung1, Soohyun Jeon1, Sung-Min Gho2

  • 1Department of Electrical and Electronic Engineering, Yonsei University, Seoul, South Korea.

NeuroImage
|February 4, 2024
PubMed
概括

使用有限视野 (FOV) 的定量敏感性映射 (QSM) 可以低估大脑铁. 一种新的基于物理的生成对抗网络方法纠正了这些错误,为临床应用提供了更快,更准确的QSM.

关键词:
移除背景字段的移除方法深度学习是一种深度学习.生成性对抗性网络 (GAN) 是一种对抗性网络.波场的扩展和波场的扩展.有限制的FOV FOV.量化易感性映射 (QSM) 是一种方法.

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科学领域:

  • 神经成像是一种神经成像.
  • 医学物理 医学物理
  • 人工智能在医学中的应用

背景情况:

  • 定量敏感性测绘 (QSM) 对于研究大脑发育和疾病中的铁来说至关重要.
  • 全脑QSM获取是耗时的,限制了临床效用.
  • 有限视野 (FOV) QSM减少了扫描时间,但由于背景场移除错误而受到敏感性低估.

研究的目的:

  • 开发和验证一种新的方法,用于精确的QSM重建,具有有限的FOV.
  • 为了克服降低FOV QSM中的敏感性低估人工物.
  • 为深灰质 (DGM) 和神经疾病提供更快速和临床可行的QSM.

主要方法:

  • 提出了一种波场扩展方法,利用物理信息生成对抗网络 (GAN).
  • 实施基于GAN的方法来纠正在有限FOV的边界处的背景场移除错误.
  • 与传统技术和完全FOV QSM采购相比验证了该方法.

主要成果:

  • 拟议的方法在有限的FOV QSM中显著降低了敏感性低估.
  • 定量和质量的结果表明,性能与完整的FOV QSM相美.
  • 该方法显示改善了帕金森病患者的局部现场结果和QSM结果,以及前性获得的数据.

结论:

  • 基于物理信息的基于GAN的波场扩展方法有效地解决了在有限的FOV QSM中敏感性低估的问题.
  • 这种方法提高了快速,高质量的QSM的临床可行性,用于脑铁量化.
  • 该方法对现实世界的临床应用有希望,特别是在DGM成像和神经退行性疾病中.