Barrois Guillaume, Coron Alain, Lucidarme Olivier, Bridal S Lori
Laboratoire d'Imagerie Biomédical Sorbonne Universités, UPMC Univ Paris 6, UMR, U1146 INSERM, and UMR7371 CNRS, F-75005, Paris, France.
Phys Med Biol. 2015 Mar 21;60(6):2117-33. doi: 10.1088/0031-9155/60/6/2117. Epub 2015 Feb 16.
Dynamic contrast-enhanced ultrasound (DCE-US) sequences are subject to motion which can disturb functional flow quantification. This can make estimated parameters more variable or unreliable. Methods that compensate for motion are therefore desirable. The most commonly used motion correction techniques in DCE-US register the images in the sequence with respect to a user-selected reference image. However, this image may not include all features that are representative of the whole sequence. Moreover, image-based registration neglects pertinent, functional-flow information contained in the DCE-US sequence. An operator-free method is proposed that combines the motion estimation and flow-parameter quantification (M/Q method) in a single mathematical framework. This method is based on a realistic multiplicative model of the DCE-US noise. By computing likelihood in this model, motion and flow parameters are both estimated iteratively. First, the maximization is accomplished by estimating functional and motion parameters. Then, a final registration based on a non-parametric temporal smoothing of the sequence is performed. This method is compared to a conventional (mutual information) registration method where all the images of the sequence are registered with respect to a reference image chosen by an expert. The two methods are evaluated on simulated sequences and DCE-US sequences acquired in patients (N = 15). The M/Q method demonstrates significantly (p < 0.05) lower Dice coefficients and Hausdorff distance than the conventional method on the simulated data sets. On the in vivo sequences analysed, the M/Q methods outperformed the conventional method in terms of mean Dice and Hausdorff distance on 80% of the sequences, and in terms of standard deviation of Dice and Hausdorff distance on 87% of the sequences.
动态对比增强超声（DCE-US）序列容易受到运动影响，这可能会干扰功能血流定量分析。这会使估计参数的变异性更大或不可靠。因此，需要采用能够补偿运动的方法。DCE-US中最常用的运动校正技术是将序列中的图像与用户选择的参考图像进行配准。然而，该参考图像可能并未包含代表整个序列的所有特征。此外，基于图像的配准忽略了DCE-US序列中包含的相关功能血流信息。本文提出了一种无需操作人员干预的方法，该方法在单一数学框架中结合了运动估计和血流参数定量分析（M/Q方法）。此方法基于DCE-US噪声的实际乘法模型。通过计算该模型中的似然性，可迭代估计运动和血流参数。首先，通过估计功能和运动参数来实现最大化。然后，基于序列的非参数时间平滑进行最终配准。将该方法与传统的（互信息）配准方法进行比较，在传统方法中，序列的所有图像都与专家选择的参考图像进行配准。这两种方法在模拟序列和在患者中采集的DCE-US序列（N = 15）上进行评估。在模拟数据集上，M/Q方法的Dice系数和豪斯多夫距离显著低于（p < 0.05）传统方法。在分析的体内序列中，M/Q方法在80%的序列的平均Dice和豪斯多夫距离方面以及在87%的序列的Dice和豪斯多夫距离标准差方面均优于传统方法。
