WKGM:用于并行成像重建的加权k空间生成模型
Zongjiang Tu1, Die Liu1, Xiaoqing Wang2
1Department of Electronic Information Engineering, Nanchang University, Nanchang, China.
NMR in biomedicine
|August 7, 2023
概括
我们介绍了加权k空间生成模型 (WKGM),这是一个新的深度学习方法,用于更快的MRI扫描. 这种强大而灵活的方法可以增强并行成像 (PI) 重建,而不需要校准数据.
科学领域:
- 医疗成像医学成像
- 人工智能的人工智能
- 信号处理 信号处理
背景情况:
- 深度学习已经通过并行成像 (PI) 加快了磁共振成像 (MRI).
- 现有的PI方法往往缺乏稳定性和灵活性.
- 在加速MRI中,无校准重建仍然是一个挑战.
研究的目的:
- 开发一种强大而灵活的深度学习模型,用于MRI无校准并行成像 (PI).
- 探索使用生成建模来改进MRI重建的k空间域学习.
- 提高加速MRI技术的效率和适用性.
主要方法:
- 提出了加权k空间生成模型 (WKGM),这是一个泛化的k空间域模型.
- 集成的k空间加权和高维空间增强用于基于分数的生成模型训练.
- 设计WKGM与传统的k空间PI模型进行协同组合,利用多线圈数据相关性.
主要成果:
- 使用WKGM实现了良好的和强大的MRI重建.
- 通过将WKGM与现有的PI模型相结合,以实现无校准重建,证明了灵活性.
- 实验结果显示,即使使用有限的训练数据集 (500张图像) 和不同的采样模式,也能达到最先进的性能.
结论:
- 在MRI中,WKGM为无校准并行成像提供了强大而灵活的解决方案.
- 该模型有效地学习k空间生成先验,以进行高质量的图像重建.
- 在保持图像保真的同时,WKGM代表了加速MRI扫描的重大进步.
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