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相关概念视频

Upsampling01:22

Upsampling

749
Managing signal sampling rates is essential in digital signal processing to maintain signal integrity. A decimated signal, characterized by a reduced frequency range due to its lower sampling rate, can be upsampled by inserting zeros between each sample. This upsampling process expands the original spectrum and introduces repeated spectral replicas at intervals dictated by the new Nyquist frequency. To refine this zero-inserted sequence, it is passed through a lowpass filter with a cutoff...
749

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Magnetic Resonance Derived Myocardial Strain Assessment Using Feature Tracking
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PyHySCO:在几秒钟内通过GPU启用易感性工件扭曲纠正.

Abigail Julian1, Lars Ruthotto1,2

  • 1Department of Computer Science, Emory University, Atlanta, GA, United States.

Frontiers in neuroscience
|June 12, 2024
PubMed
概括

PyHySCO使用反向梯度极性 (RGP) 方法提供了快速,准确的回声平面成像 (EPI) 扭曲的校正. 这个新工具利用GPU进行秒级3D校正,性能优于现有方法.

科学领域:

  • 医疗成像医学成像
  • 神经成像是一种神经成像.
  • 计算神经科学是一种神经科学.

背景情况:

  • 在回声平面成像 (EPI) 中的敏感性人工物是MRI的一个重大挑战.
  • 现有的反向梯度极性 (RGP) 校正方法是计算密集的,每卷需要几分钟.
  • 硬件和算法的进步需要更新的校正工具.

研究的目的:

  • 推出PyTorch超弹性易感性纠正 (PyHySCO),这是一个快速和用户友好的工具来纠正EPI扭曲.
  • 通过多线程和GPU加速,在几秒钟内实现3D RGP校正.
  • 提供基于既有物理模型的可靠,无需培训的校正方法.

主要方法:

  • PyHySCO使用物理扭曲模型和数学公式进行可靠的校正.
  • 一个改进的初始化方案采用了Chang和Fitzpatrick的1D扭曲校正方法.
  • 该工具在PyTorch中实现,支持多线程和GPU利用速度.
  • 验证涉及对3T和7T人类结合体项目数据进行广泛的数值测试.

主要成果:

  • PyHySCO实现了每体积秒的校正时间,这显著提高了速度.
  • 该工具的准确性与领先的RGP方法相美.
关键词:
在 GPU 加速加速.声波平面成像系统的成像平行化的平行化.反向梯度极性的反向梯度极性.软件 软件 软件 软件 软件

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  • 验证证实了新的初始化方案的有效性,并比较了优化算法.
  • 性能在不同的硬件和算术精度上进行了测试.

    • 结论:

    • PyHySCO提供了一个快速,准确和可靠的EPI扭曲校正解决方案.
    • 该工具的效率和用户友好性使其适用于先进的神经成像研究.
    • 在GNU公共许可证下,PyHySCO是自由可用的,促进了可访问性.