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相关概念视频

Magnetic Resonance Imaging01:24

Magnetic Resonance Imaging

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Magnetic resonance imaging (MRI) is a noninvasive medical imaging technique based on a phenomenon of nuclear physics discovered in the 1930s, in which matter exposed to magnetic fields and radio waves was found to emit radio signals. In 1970, a physician and researcher named Raymond Damadian noticed that malignant (cancerous) tissue gave off different signals than normal body tissue. He applied for a patent for the first MRI scanning device in clinical use by the early 1980s. The early MRI...
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Quantitative Magnetic Resonance Imaging of Skeletal Muscle Disease
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联合亚采样模式和双域重建,以加速多对比度MRI.

Pengcheng Lei, Le Hu, Faming Fang

    IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
    |August 23, 2024
    PubMed
    概括

    这项研究引入了一种新的深度展开的双域网络 (MC-DuDoN),用于加速多对比磁共振成像 (MCMRI). 该方法优化了不足采样模式,并在图像和k空间领域重建图像,提高速度和准确性.

    科学领域:

    • 医疗成像医学成像
    • 人工智能的人工智能
    • 信号处理 信号处理

    背景情况:

    • 多对比磁共振成像 (MCMRI) 通过使用参考图像加速MRI扫描.
    • 现有的MCMRI方法存在局限性,包括冗余数据,未充分探索k空间信息,以及在网络设计中缺乏物理可解释性.

    研究的目的:

    • 开发一种新的深度展开的双域网络,以加速MCMRI.
    • 共同优化不足采样模式和图像重建,以提高MCMRI性能.

    主要方法:

    • 一个框架来学习MCMRI的最佳亚采样模式,减少冗余和采样对比特征信息.
    • 一个双域网络模型,可以在图像和k空间领域重建图像,并结合结构不一致的空间转换和全球信息的k空间学习.
    • 使用近接梯度算法进行优化,并展开成一个深度网络 (MC-DuDoN).

    主要成果:

    • 拟议的MC-DuDoN在MCMRI超分辨率和重建任务中表现出卓越的性能.
    • 该方法在空间错位的MCMRI上显示出稳定性,这是由于不一致结构的明确建模.
    • 在重建任务中学习到的面具可以恢复更现实的图像,即使在超高的加速度比率 (×30).

    结论:

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    • 开发的MC-DuDoN通过优化不足采样和利用双域重建,有效地加速MCMRI.
    • 这种方法提供了更好的准确性,对错位的稳定性和在高加速度因子下增强的图像现实性.