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自主监督学习,以拒绝多维MRI数据

Beomgu Kang^1,2, Wonil Lee³, Hyunseok Seo²

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

Magnetic resonance in medicine

|June 27, 2024

概括

这项研究引入了使用自主监督学习的多维MRI数据的快速解密框架. 该方法显著提高了图像质量,并改善了定量分析,而不需要清洁的参考图像.

科学领域:

医疗成像医学成像
机器学习机器学习
信号处理信号处理

背景情况:

定量MRI需要高的信号噪声比 (SNR) 来准确地估计组织参数.
在MRI数据中的噪音使复杂的,非线性信号模型的拟合复杂,影响量化.
为了培训,获取地面真实清洁的MRI数据往往是不切实际的.

研究的目的:

为高维磁共振成像数据开发一个快速解密的框架.
实施自主监督学习计划,消除对清洁参考图像的需求.
为了提高SNR和MRI数据的量化准确性.

主要方法:

提出了一个深度学习框架,利用多维MRI数据中的冗余性.
一个自我监督的模型只使用噪音图像进行训练,以解决缺乏清洁数据的问题.
该框架在模拟的MTC-MRF和体内DWI数据集上得到了验证.

主要成果:

拟议的方法在消毒方面显著优于现有的技术 (BM3D,tMPPCA,Patch2self).
在各种噪声水平和分布中观察到性能改善.
剥离导致从处理的MRI数据中获得更准确的定量结果.

结论:

MD-S2S无线化技术有效地增强了多维MRI数据.
这种方法可以应用于各种MRI数据集,提高量化准确性.
该框架为强大的定量MRI提供了一个有希望的解决方案.

关键词:

拒绝使用,拒绝使用.扩散扩散是一种扩散.磁化转移对比度 (MTC) 的使用定量的MRI是指MRI的数量.自主监督学习学习

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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...