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相关概念视频

NMR Spectrometers: Radiofrequency Pulses and Pulse Sequences01:17

NMR Spectrometers: Radiofrequency Pulses and Pulse Sequences

793
A pulse is a short burst of radio waves distributed over a range of frequencies that simultaneously excites all the nuclei in the sample. Upon passing a radio frequency pulse along the x-axis, the nuclei absorb energy corresponding to their Larmor frequencies and achieve resonance. This shifts the net magnetization vector from the z-axis toward the transverse plane. This angle of rotation of the magnetization vector, or the flip angle, is proportional to the duration and intensity of the pulse.
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NMR Spectrometers: Resolution and Error Correction01:14

NMR Spectrometers: Resolution and Error Correction

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When magnetic nuclei in a sample achieve resonance and undergo relaxation, the signal detected in NMR is an approximately exponential free induction decay. Fourier transform of an exponential decay yields a Lorentzian peak in the frequency domain. Lorentzian peaks in an NMR spectrum are defined by their amplitude, full width at half maximum, and position, where the peak width is governed by the spin-spin relaxation time alone. In real experiments, however, the applied magnetic field is rendered...
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Updated: Jun 26, 2025

Quantitative Magnetic Resonance Imaging of Skeletal Muscle Disease
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高效的脉冲序列设计框架用于高维的MR指纹扫描,使用系统错误指数.

Siyuan Hu1, Zhilang Qiu1, Richard James Adams1

  • 1Department of Biomedical Engineering, Case Western Reserve University, Cleveland, Ohio, USA.

Magnetic resonance in medicine
|May 10, 2024
PubMed
概括
此摘要是机器生成的。

一个新的系统性错误指数 (SEI) 模型通过近似真实世界的错误有效地优化磁共振指纹 (MRF) 序列. 这加速了高维的MRF序列设计,提高了准确性和减少了文物.

关键词:
加速模拟的加速模拟.错误的表征错误的表征.磁共振指纹的使用脉冲序列优化优化 脉冲序列优化下面的样本是对文物进行的.

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科学领域:

  • 磁共振成像 (MRI) 是一种磁共振成像技术.
  • 医学物理 医学物理
  • 生物医学工程 生物医学工程

背景情况:

  • 磁共振指纹 (MRF) 能够进行定量组织属性映射.
  • 优化MRF脉冲序列对于准确性至关重要,但对于高维模型来说,计算密集.
  • 现有的虚拟扫描模拟对于复杂的MRF框架来说太慢了.

研究的目的:

  • 引入一种新的数学模型,即系统性错误指数 (SEI),用于高效的MRF序列优化.
  • 为了应对高维MRF序列设计中的可扩展性挑战.
  • 为了实现MRF序列的实际优化,考虑复杂的组织性质相互作用.

主要方法:

  • 开发了系统错误指数 (SEI) 模型,以低计算成本近似量化错误.
  • 消除了对计算上昂贵的字典匹配的需求.
  • 对SEI模型与虚拟扫描模拟进行验证.
  • 应用SEI模型来优化高维MRF序列 (2-4种组织特性).

主要成果:

  • 该SEI模型非常接近虚拟扫描模拟结果.
  • 在计算速度方面实现了百倍至千倍的加速.
  • 优化的MRF序列显示了更高的测量准确度和更少的低样本人工物.
  • 与启发式设计的序列相比,优化扫描实现了更短的扫描时间.

结论:

  • 开发了一种有效的方法来估计MRF扫描中的真实世界的错误.
  • 该SEI模型提供了准确的定性和定量错误近似值.
  • 证明了SEI模型对优化高维MRF序列的实用性.
  • 优化的MRF扫描显示了对低样本和系统缺陷的强化稳定性,并更快地获取.