CoRRECT:运动校正定量R2*映射的深度展开框架
Xiaojian Xu1, Weijie Gan1, Satya V V N Kothapalli2
1Department of Computer Science and Engineering, Washington University in St. Louis, St. Louis, MO 63130, USA.
概括
这项研究引入了CORRECT,一个统一的定量MRI (qMRI) 深度展开框架. 在加速MRI扫描中,CoRRECT有效地减少了运动和磁场不均质的物体,产生了高质量的R2*地图.
科学领域:
- 医学成像
- 生物物理
- 人工智能
背景情况:
- 定量核磁共振 (qMRI) 量化生物组织参数,但面临着文物方面的挑战.
- 传统的qMRI方法分别处理运动和磁场等不均质的物体,从而限制性能.
- 在QMRI中加速获取数据加剧了文物问题,需要先进的解决方案.
研究的目的:
- 介绍CORRECT,一个统一的深度展开框架,用于QMRI的 reduction.
- 开发基于模型的神经网络,集成运动和现场不均性纠正.
- 在没有预先计算的校正参数的情况下实现高质量的qMRI加速采集.
主要方法:
- 开发了一个名为CORRECT的统一深度展开 (DU) 框架.
- 实现基于模型的端到端神经网络,并进行自我监督学习.
- 网络直接从k空间数据中学习纠正运动和场的不均性.
主要成果:
- CoRRECT成功地从加速多梯度回忆回声 (mGRE) 的MRI数据中恢复了无工件的R2*地图.
- 该框架考虑了运动和场的不均性,而不需要预先计算的校正参数.
- 在高度加速的收购环境中表现出强的表现.
结论:
- CoRRECT提供了一种统一的方法来纠正QMRI中的文物,提高图像质量.
- 深度展开的方法可以整合物理,生物物理和学习模型进行先进的QMRI.
- 这项工作为更有效,更准确的定量核磁共振技术铺平了道路.
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