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通过图形卷积网络加速高分辨率3D MR指纹

Feng Cheng¹, Yong Chen², Xiaopeng Zong³

¹Department of Computer Science, University of North Carolina, Chapel Hill, NC, USA.

Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention

|March 20, 2024

概括

本研究介绍了一种深度学习方法,以加速3D磁共振指纹 (MRF) 采集. 新方法显著加速成像,使先进的组织特性量化更具临床可行性.

科学领域:

医疗成像医学成像
人工智能的人工智能
生物物理学的生物物理.

背景情况:

磁共振指纹 (MRF) 能够快速,同时量化多种组织特性.
目前的3D MRF方法需要进一步提高速度以获得广泛的临床采用.

研究的目的:

开发一种新的深度学习方法,以加速3D MRF获取.
为了提高获取速度沿着切片编码方向在k空间.

主要方法:

引入基于图形的卷积神经网络,适用于MRF中使用的非卡特西亚螺旋轨迹.
实施深度学习模型以加速3D MRF数据采集.

主要成果:

在3D MRF获取速度方面取得了显著的加速.
与现有的最先进的方法相比,提高了组织量化准确性.
能够在不到5分钟的时间内以高空间分辨率覆盖整个大脑.

结论:

开发的深度学习方法使3D MRF更适合临床应用.
快速和高分辨率的3DMRF成像现在可以在临床相关的时间框架内实现.

关键词:

3D MR指纹的使用在GRAPPA中,我们可以使用GRAPPA.图形的卷积可以表示.在K空间中进行插值.

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相关概念视频

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...