Myocardial perfusion cardiovascular magnetic resonance: optimized dual sequence and reconstruction for quantification
Myocardial perfusion cardiovascular magnetic resonance: optimized dual sequence and reconstruction for quantification
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DOI：
10.1186/s12968-017-0355-5
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发表时间：
2017-04-07
影响因子：
6.4
通讯作者：
Xue, Hui
中科院分区：
文献类型：
--
作者：
Kellman, Peter;Hansen, Michael S.;Xue, Hui
背景：心肌血流定量需要了解造影剂在心肌组织中的含量以及驱动造影剂输送的动脉输入功能（AIF）。由于测量信号和造影剂浓度之间缺乏线性关系，对准确定量提出了挑战。这项工作表征了非线性的来源，并提出了一种系统的方法来精确测量血液和心肌中的造影剂浓度。方法：采用双序列方法，分别对AIF和心肌组织进行脉冲序列检测，分别对血液和心肌参数进行优化。采用系统方法进行总体设计，以实现信号和造影剂浓度之间的线性关系。信号强度值到造影剂浓度的转换是通过结合表面线圈灵敏度校正、基于Bloch模拟的查表校正以及在AIF测量的情况下T2*损耗校正来实现的。在幻影中验证信号校正，并提供29名正常受试者休息和腺苷应激时的AIF峰值浓度和心肌流量值。结果：对于幻像，AIF和心肌的拟合值均在5%以内。在健康志愿者中，应激时的峰值[Gd]为3.5 +/- 1.2 mmol/ L，休息时为4.4 +/- 1.2 mmol/ L。AIF峰值时左室血池T2*约为10 ms。未经校正的原始信号强度的峰谷比为5.6，而查找表（LUT）校正的AIF的峰谷比为8.3，校正量约为48%。如果没有T2*校正，心肌血流量估计被高估了约10%。在应激状态下，峰值增强（1.5 T）心肌信号的信噪比为17.7 +/- 6.6，峰值[Gd]为0.49 +/- 0.15 mmol/ l。在应激和休息状态下，BTEX模型估计灌注流量分别为3.9 +/- 0.38和1.03 +/- 0.19 ml/ min/ g， Fermi模型估计灌注流量分别为3.4 +/- 0.39和0.95 +/- 0.16。结论：优化了心肌血流定量的心血管磁共振和AIF双序列。在模型中进行了验证，以证实信号与钆浓度的转换是线性的。所提出的序列与全自动在线解决方案集成，用于逐像素绘制心肌血流，并在29名正常健康受试者的腺苷应激和休息研究中进行评估。证明了可靠的灌注映射，并产生了低可变性的估计。
Background: Quantification of myocardial blood flow requires knowledge of the amount of contrast agent in the myocardial tissue and the arterial input function (AIF) driving the delivery of this contrast agent. Accurate quantification is challenged by the lack of linearity between the measured signal and contrast agent concentration. This work characterizes sources of non-linearity and presents a systematic approach to accurate measurements of contrast agent concentration in both blood and myocardium.Methods: A dual sequence approach with separate pulse sequences for AIF and myocardial tissue allowed separate optimization of parameters for blood and myocardium. A systems approach to the overall design was taken to achieve linearity between signal and contrast agent concentration. Conversion of signal intensity values to contrast agent concentration was achieved through a combination of surface coil sensitivity correction, Bloch simulation based look-up table correction, and in the case of the AIF measurement, correction of T2* losses. Validation of signal correction was performed in phantoms, and values for peak AIF concentration and myocardial flow are provided for 29 normal subjects for rest and adenosine stress.Results: For phantoms, the measured fits were within 5% for both AIF and myocardium. In healthy volunteers the peak [Gd] was 3.5 +/- 1.2 for stress and 4.4 +/- 1.2 mmol/ L for rest. The T2* in the left ventricle blood pool at peak AIF was approximately 10 ms. The peak-to-valley ratio was 5.6 for the raw signal intensities without correction, and was 8.3 for the look-up-table (LUT) corrected AIF which represents approximately 48% correction. Without T2* correction the myocardial blood flow estimates are overestimated by approximately 10%. The signal-to-noise ratio of the myocardial signal at peak enhancement (1.5 T) was 17.7 +/- 6.6 at stress and the peak [Gd] was 0.49 +/- 0.15 mmol/ L. The estimated perfusion flow was 3.9 +/- 0.38 and 1.03 +/- 0.19 ml/ min/ g using the BTEX model and 3.4 +/- 0.39 and 0.95 +/- 0.16 using a Fermi model, for stress and rest, respectively.Conclusions: A dual sequence for myocardial perfusion cardiovascular magnetic resonance and AIF measurement has been optimized for quantification of myocardial blood flow. A validation in phantoms was performed to confirm that the signal conversion to gadolinium concentration was linear. The proposed sequence was integrated with a fully automatic in-line solution for pixel-wise mapping of myocardial blood flow and evaluated in adenosine stress and rest studies on N = 29 normal healthy subjects. Reliable perfusion mapping was demonstrated and produced estimates with low variability.