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无监督的运动工件校正的旋回声MRI使用深度图像先前的图像

Jongyeon Lee^1,2, Hyunseok Seo², Wonil Lee^3,4

¹School of Electrical Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, Republic of Korea.

Magnetic resonance in medicine

|January 29, 2024

概括

这项研究引入了一种无监督的深度学习方法,用于纠正MRI扫描中的运动工件. 这种新的方法显著提高了图像质量,而不需要大量的训练数据集,证明了临床潜力.

科学领域:

医疗成像医学成像
人工智能的人工智能
磁共振成像是一种磁共振成像技术.

背景情况:

运动工件会降低磁共振成像 (MRI) 的质量.
深度学习纠正方法通常需要广泛的,资源密集的培训.

研究的目的:

提出一种无监督的深度学习方法,用于在轮旋转回声MRI中进行运动工件校正.
为了利用深度图像先前框架来有效地去除文物.

主要方法:

该方法利用神经网络参数化来抑制运动工件.
它涉及图像参数化,空间转换和运动模拟模型.
一个优化过程将损坏的图像合成到与已获取的图像相匹配,从而最大限度地减少差异.

主要成果:

在模拟研究中观察到结构相似性指数的显著改善.
废弃性研究证实了单个成分在人工物校正中的有效性.
现实世界的数据实验表明了强大的临床潜力.

结论:

拟议的方法有效地纠正了轮旋回回声MRI中的刚性和内平面运动工件.
它在图像质量方面提供了大量的定量和质量改进.

关键词:

深度图像之前的图像.深度学习是一种深度学习.轮旋转回声轮旋转回声无监督的运动校正.

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Magnetic Resonance Imaging

Magnetic Resonance Imaging

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...