使用多回声,多对比度MRI快速同时估计放松率
Elizabeth G Keeling1, Nicholas J Sisco2, Molly M McElvogue2
1Barrow Neurological Institute, 350 W Thomas Rd, Phoenix, AZ 85013, USA; School of Life Sciences, Arizona State University, 427 E Tyler Mall, Tempe, AZ 85281, USA.
Magnetic resonance imaging
|July 6, 2024
概括
一般化的线性最小平方 (LLSQ) 为动态成像研究中的R2*和R2提供了一个快速可靠的方法. 与非线性最小平方 (NLSQ) 拟合相比,这种方法显著降低了计算需求.
科学领域:
- 医疗成像医学成像
- 生物物理学的生物物理.
- 量化MRI是指数量化的MRI.
背景情况:
- 多回声,多对比的动态成像方法对于同时量化R2*和R2.2至关重要.
- 传统的非线性最小正方形 (NLSQ) 拟合对这些动态成像研究提出了重大计算挑战.
研究的目的:
- 引入和验证一个通用的线性最小平方 (LLSQ) 解决方案,以快速适配R2*和R2.
- 为了克服与NLSQ配合动态成像相关的计算负担.
主要方法:
- 模拟旋转和梯度回声 (SAGE) 数据在高和低信号噪声比 (SNR) 的不同T2*和T2值中.
- 对于三参数和四参数模型,LLSQ和NLSQ配件的比较.
- 在20名复发性复发性多发性硬化症患者体内SAGE输液数据采集.
主要成果:
- 在模拟和体内数据中,LLSQ证明了对R2*和R2的可靠匹配,具有高的一致性相关系数 (CCC) 和低的变化系数 (CV).
- 在体内LLSQ R2*和R2估计与NLSQ结果非常相匹配,在高SNR时观察到的差异很小.
- 从16-18小时 (NLSQ) 到3-4分钟,LLSQ显著减少了全脑适配时间.
结论:
- 在动态成像中,LLSQ为R2*和R2量化提供了NLSQ的计算效率高和可靠的替代方案.
- 由于LLSQ的计算需求减少,可以快速估计R2*和R2,从而促进先进的定量MRI应用.
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