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相关概念视频

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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在EPI中深度无监督校正易感性工件的对齐引导前向扭曲模型.

Muhammed Hasan Kayapinar, Abdallah Zaid Alkilani, M Okan Irfanoglu

    Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
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    在回声平面成像 (EPI) 中的磁共振成像 (MRI) 易感性人工物通过新的对齐引导前向扭曲网络 (agFD-Net) 得到更快的纠正. 这种深度学习方法可以解释主体运动,提高临床适用性.

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    科学领域:

    • 医疗成像医学成像
    • 深度学习 (Deep Learning) 是一种深度学习.
    • 神经成像是一种神经成像.

    背景情况:

    • 在回声平面成像 (EPI) 中,敏感性诱导的扭曲是磁共振成像 (MRI) 中的主要挑战.
    • 传统的文物校正方法是计算密集型的,不适合临床使用.
    • 深度学习为高效的EPI工件纠正提供了潜力.

    研究的目的:

    • 开发一种深度学习模型,以快速准确地纠正EPI易感性伪造.
    • 为了应对艺术品纠正过程中主体运动的挑战.
    • 为了提高EPI人工物纠正的临床可行性.

    主要方法:

    • 提出了一个以对齐为导向的前向扭曲网络 (agFD-Net),用于无监督的,以物理为导向的培训.
    • 集成了一个预先训练的对齐网络 (AlignNet) 来处理主体运动.
    • 在实验NIH数据集上评估agFD-Net,具有现实的运动水平.

    主要成果:

    • agFD-Net实现了快速和高准确度的易感性人工物校正.
    • 该模型成功地计算了反相编码采集之间的对象运动.
    • 与古典方法相比,在计算效率上演示了超过两倍的加速.

    结论:

    • agFD-Net 在 EPI 工件校正效率和准确性方面取得了重大进展.
    • 该模型处理主体运动的能力使其在临床MRI应用中非常有前途.
    • 这种深度学习方法克服了传统方法的局限性,使得实际临床使用成为可能.