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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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取代2Self:基于voxel替换和图像混合进行扩散MRI的自我监督的剥离.

Linhai Wu, Lihui Wang, Zeyu Deng

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    此摘要是机器生成的。

    这项研究介绍了Replace2Self,一种新的自我监督学习模型,用于减少扩散加权 (DW) 成像中的噪音. 该方法有效地抑制空间相关噪声,改善微观结构分析的图像质量.

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    科学领域:

    • 医疗成像医学成像
    • 神经成像是一种神经成像.
    • 生物物理学的生物物理.

    背景情况:

    • 低信号噪声比 (SNR) 是扩散权重 (DW) 成像的一个显著限制.
    • 在DW图像中的噪音使组织微观结构的分析变得复杂.

    研究的目的:

    • 提出一种新的自我监督学习模型,Replace2Self,以有效地减少DW图像中的空间相关噪声.
    • 为了提高来自DW成像的组织微观结构分析的准确性.

    主要方法:

    • 开发了一种自我监督的学习模型 (Replace2Self),利用基于Q空间类似区块匹配的voxel替换策略.
    • 实施了一种图像混合策略,使用互补的面具来产生各种噪音输入,用于网络培训.
    • 引入了补充面膜混合一致性和反向替换规则化损失,以优化消除噪声的性能.

    主要成果:

    • 在各种噪声分布,水平和b值中,Replace2Self在减少空间相关噪声方面表现出卓越的性能.
    • 实现了最高的峰值信号噪声比 (PSNR),在10%的噪声水平下至少超过1.9%的亚最佳方法.
    • 通过广泛的模拟,现实世界数据集和消去实验验证实有效性,证实了卓越的概括能力.

    结论:

    • Replace2Self有效地减少了DW图像中的空间相关噪声,克服了该模式的一个关键限制.
    • 拟议的方法为DW成像中的微结构分析提供了更好的图像质量和强大的概括.
    • 这种方法有望促进神经成像和其他使用DW成像的领域的定量分析.