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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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Magnetic Resonance Imaging Quantification of Pulmonary Perfusion using Calibrated Arterial Spin Labeling
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使用DeepFermi进行强大的心肌 perfusion MRI量化.

Sherine Brahma, Andreas Kofler, Felix F Zimmermann

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    此摘要是机器生成的。

    一种新的深度学习方法提供了快速,准确和强大的心肌 perfusion 量化,使用压力 perfusion 心脏磁共振. 这种人工智能方法克服了传统方法的局限性,改善了对心脏血液供应的临床评估.

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    科学领域:

    • 心血管成像 - 心血管成像
    • 人工智能在医学中的应用
    • 医学物理 医学物理

    背景情况:

    • 压力输液心磁共振 (CMR) 对于评估心肌血管血液供应至关重要.
    • 目前的视觉评估是主观的;定量方法是缓慢的,对文物敏感.
    • 现有的定量方法,如解卷分析,耗时且容易出现与运动相关的异常值.

    研究的目的:

    • 引入一种新的深度学习方法,用于快速,准确和强大的心肌 perfusion 量化.
    • 开发一种对心肌输液的用户独立评估.
    • 为了提高在 perfusion 分析中对运动工件和数据异常值的稳定性.

    主要方法:

    • 费米函数与神经网络架构的整合,用于 perfusion 量化.
    • 使用一个3D卷积神经网络之前的时空概括.
    • 采用自主监督的学习框架和异常耐药的培训方法.

    主要成果:

    • 与传统的解卷分析相比,在模拟实验中表现出更好的准确性和稳定性.
    • 在数据异常值的不同信号噪音比率场景中实现一致的性能.
    • 产生了与临床诊断一致的强大的体内 perfusion 估计,比传统算法快大约五倍.

    结论:

    • 拟议的深度学习方法在心肌输液量化方面取得了重大进展.
    • 这种方法为现有的定量技术提供了更快,更准确和更强大的替代方案.
    • 自主监督和抗异常值的框架增强了CMR perfusion 分析的临床适用性.