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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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相关实验视频

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Reliable Acquisition of Electroencephalography Data during Simultaneous Electroencephalography and Functional MRI
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可泛化深度学习方法用于使用元学习来抑制未见的和多个MRI文物.

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    此摘要是机器生成的。

    课程-MAML (CMAML) 通过从多个文物中进行自适应学习来改善磁共振 (MR) 图像文物删除. 这种方法增强了概括性,并减少了在临床环境中对众多工件特定深度学习模型的需求.

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    Protocol for the Evaluation of MRI Artifacts Caused by Metal Implants to Assess the Suitability of Implants and the Vulnerability of Pulse Sequences
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    科学领域:

    • 医学成像医学成像
    • 人工智能的人工智能是人工智能.
    • 信号处理 信号处理

    背景情况:

    • 磁共振 (MR) 图像容易出现来自运动,分辨率和样本不足的工件.
    • 当前的深度学习模型通常需要为每个文物类型提供单独的培训,从而限制了概括性和效率.
    • 对多个文物进行联合训练可能无法充分解决文物复杂性的差异.

    研究的目的:

    • 引入课程-MAML (CMAML),一种新的方法,将模型不可知的超级学习 (MAML) 与课程学习相结合,以进行强大的MR图像文物删除.
    • 在单个培训过程中增强复杂度不同的多个文物恢复的自适应性学习.
    • 为了减少临床MR图像人工物校正所需的专业深度学习模型的数量.

    主要方法:

    • 实现了一个嵌套的双层优化框架 (MAML),以学习文物和文物特定恢复的共同知识.
    • 将课程学习集成到MAML (CMAML) 中,以在培训期间管理文物复杂性.
    • 进行了比较研究,使用两个心脏数据集与随机梯度下降和标准MAML对比.

    主要成果:

    • 在所有情况下,CMAML表现出卓越的概括性,改善了83%的未见的文物类型/数量的峰值信号噪声比 (PSNR),并改善了结构相似度指数 (SSIM).
    • 该方法在5个复合工件场景中的4个中显示出更好的工件抑制.
    • 在受多个组合工件影响的图像中,CMAML在80%的案例中表现更好.

    结论:

    • 在临床部署中,CMAML有效地减少了对众多工件特定深度学习模型的需求,这对于临床部署至关重要.
    • 与现有技术相比,拟议的方法提供了改进的概括和文物抑制能力.
    • CMAML为提高临床MR成像的质量和可靠性提供了一个有希望的解决方案.