一种对x核核核磁共振成像和MRS的用户独立无效化方法
Nichlas Vous Christensen1, Michael Vaeggemose1,2, Nikolaj Bøgh1,3
1The MR Research Centre, Department of Clinical Medicine, Aarhus University, Aarhus, Denmark.
Magnetic resonance in medicine
|August 1, 2023
概括
一种新的排斥方法,张量马琴科-帕斯图尔主要成分分析 (tMPPCA),在改善X核MRI质量方面表现强. 这种多功能工具可以提高图像质量,用于在各种X核成像应用中进行诊断.
科学领域:
- 医疗成像医学成像
- 生物物理学的生物物理.
- 计算神经科学是一种神经科学.
背景情况:
- 包括 (23Na) 和碳 (13C) 在内的X核MRI提供了独特的见解,但与质子MRI相比,其信号噪声比率 (SNR) 较低.
- 提高图像质量对于X核成像技术的临床翻译和诊断实用性至关重要.
研究的目的:
- 为了比较一个新的几输入的消极化方法,张量马琴科-帕斯图尔主要组件分析 (tMPPCA),与当前最先进的全球-本地更高阶奇点值分解 (GL-HOSVD).
- 在各种X核核MRI数据集中评估这些消极化方法的性能.
主要方法:
- 除方法 (tMPPCA和GL-HOSVD) 应用于人类23Na和2H脑部扫描,13C心脏和脑部扫描,以及模拟的动态超极化13C脑部扫描.
- 使用残余分布,平均平方误差和布兰德-阿尔特曼分析量化消除噪声的有效性,以评估原始和无噪声图像之间的一致性.
主要成果:
- 无论GL-HOSVD还是tMPPCA都在拒绝各种X核MRI数据方面表现相似.
- 与GL-HOSVD相比,tMPPCA方法平均消除了大约5%的噪音.
- 布兰德-阿尔特曼分析显示,这两种方法的原始数据和被拒绝的数据之间存在类似的一致性.
结论:
- tMPPCA的少数输入方法在不同的X核成像数据源中提供了出色的概括性.
- tMPPCA被认为是提高X核MRI质量的强大而多功能工具.
- 推使用tMPPCA作为改进X核成像的首选消毒方法.
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