Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany.
Department of Imaging Physics, Delft University of Technology, Delft, The Netherlands.
Magn Reson Med. 2024 Jan;91(1):118-132. doi: 10.1002/mrm.29834. Epub 2023 Sep 5.
To investigate and mitigate the influence of physiological and acquisition-related parameters on myocardial blood flow (MBF) measurements obtained with myocardial Arterial Spin Labeling (myoASL).
A Flow-sensitive Alternating Inversion Recovery (FAIR) myoASL sequence with bSSFP and spoiled GRE (spGRE) readout is investigated for MBF quantification. Bloch-equation simulations and phantom experiments were performed to evaluate how variations in acquisition flip angle (FA), acquisition matrix size (AMS), heart rate (HR) and blood relaxation time ( ) affect quantification of myoASL-MBF. In vivo myoASL-images were acquired in nine healthy subjects. A corrected MBF quantification approach was proposed based on subject-specific values and, for spGRE imaging, subtracting an additional saturation-prepared baseline from the original baseline signal.
Simulated and phantom experiments showed a strong dependence on AMS and FA ( >0.73), which was eliminated in simulations and alleviated in phantom experiments using the proposed saturation-baseline correction in spGRE. Only a very mild HR dependence ( >0.59) was observed which was reduced when calculating MBF with individual . For corrected spGRE, in vivo mean global spGRE-MBF ranged from 0.54 to 2.59 mL/g/min and was in agreement with previously reported values. Compared to uncorrected spGRE, the intra-subject variability within a measurement (0.60 mL/g/min), between measurements (0.45 mL/g/min), as well as the inter-subject variability (1.29 mL/g/min) were improved by up to 40% and were comparable with conventional bSSFP.
Our results show that physiological and acquisition-related factors can lead to spurious changes in myoASL-MBF if not accounted for. Using individual and a saturation-baseline can reduce these variations in spGRE and improve reproducibility of FAIR-myoASL against acquisition parameters.
研究并减轻生理和采集相关参数对心肌动脉自旋标记(myoASL)测量心肌血流(MBF)的影响。
研究了带有 bSSFP 和扰相 GRE(spGRE)读出的流动敏感反转恢复(FAIR)myoASL 序列,用于 MBF 定量。通过 Bloch 方程模拟和体模实验评估了采集翻转角(FA)、采集矩阵大小(AMS)、心率(HR)和血液弛豫时间( )的变化如何影响 myoASL-MBF 的定量。在 9 名健康受试者中采集了体内 myoASL 图像。提出了一种基于个体 值的校正 MBF 定量方法,对于 spGRE 成像,从原始基线信号中减去附加的饱和准备基线。
模拟和体模实验表明,对 AMS 和 FA 的依赖性很强( >0.73),在模拟和使用 spGRE 中提出的饱和基线校正减轻体模实验中的依赖性。仅观察到非常轻微的 HR 依赖性( >0.59),当使用个体 计算 MBF 时会降低。对于校正后的 spGRE,体内平均全局 spGRE-MBF 范围为 0.54 至 2.59 mL/g/min,与先前报道的值一致。与未校正的 spGRE 相比,测量内的个体内变异性(0.60 mL/g/min)、测量间的变异性(0.45 mL/g/min)以及个体间的变异性(1.29 mL/g/min)提高了 40%,与常规 bSSFP 相当。
我们的结果表明,如果不考虑生理和采集相关因素,它们可能导致 myoASL-MBF 出现虚假变化。使用个体 和饱和基线可以减少 spGRE 中的这些变化,并提高 FAIR-myloASL 对采集参数的可重复性。