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Effects of Flip Angle Uncertainty and Noise on the Accuracy of DCE-MRI Metrics: Comparison Between Standard Concentration-Based and Signal Difference Methods

机译:倾角不确定度和噪声对DCE-MRI度量准确性的影响:基于标准浓度法和信号差法的比较

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摘要

Dynamic contrast-enhanced MRI is becoming an increasingly important tool to assess tumors and their response to treatment. In the most common method of computing tumor perfusion parameters, the concentration of the injected contrast agent is first computed in both tumor and blood which is subsequently fit to a perfusion model, typically the Tofts two compartment model. However, this strategy can be highly sensitive to errors in the excitation flip angle and noise. More recently, a simpler method of determining perfusion was developed in which the difference signal, obtained by subtracting the measured time course signal by the signal prior to bolus arrival, is utilized in lieu of the concentration values. The goal of this work is to compare the performance of these two strategies with simulation experiments in the presence of flip angle errors and different levels of image signal to noise ratios (SNRs). Results show that with the conventional method, if assumed pre-contrast T1 of blood is used, large errors in perfusion (exceeding 400% and 200% for Ktrans and ve, respectively) can occur in the presence of flip angle deviations typically observed in vivo. However, when baseline T1 values are measured for both tumor and blood, the errors become a function of flip angle difference between the two locations, with nearly no error if the flip angle errors are identical at both locations. The errors are substantially smaller with the signal difference strategy (less than 100% for both Ktrans and ve). The latter method also yields more consistent perfusion values at varying SNR levels. The results suggest that measuring the actual flip angle may be critical for obtaining absolute perfusion values, but in studies in which relative changes in perfusion is of primary interest or if true flip angles are not known, the signal difference strategy may be preferred over the standard concentration-based method.
机译:动态对比增强MRI已成为评估肿瘤及其对治疗反应的越来越重要的工具。在最常见的计算肿瘤灌注参数的方法中,首先要在肿瘤和血液中计算注入的造影剂的浓度,随后将其拟合到灌注模型,通常是Tofts两室模型。但是,此策略对激发翻转角和噪声中的误差可能非常敏感。最近,开发了一种更简单的确定灌注的方法,在该方法中,通过使用推注到达之前的信号减去测得的时程信号获得的差信号代替浓度值。这项工作的目的是在存在翻转角误差和不同水平的图像信噪比(SNR)的情况下,将这两种策略的性能与仿真实验进行比较。结果表明,使用常规方法,如果使用假定的血液造影剂T1,则在存在下会发生较大的灌注误差(K trans 和ve分别超过400%和200%)通常在体内观察到的翻转角偏差。但是,如果同时测量了肿瘤和血液的基线T1值,则误差将成为两个位置之间翻转角差的函数,如果两个位置的翻转角误差相同,则几乎没有误差。利用信号差策略,误差明显较小(K trans 和ve均小于100%)。后一种方法还可以在变化的SNR级别上产生更一致的灌注值。结果表明,测量实际的翻转角对于获得绝对灌注值可能至关重要,但是在以灌注的相对变化为主要关注点或如果不知道真正的翻转角的研究中,信号差策略可能比标准方法更可取基于浓度的方法。

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