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Research on respiratory motion correction method based on liver contrast-enhanced ultrasound images of single mode

机译：基于单模肝对比增强超声图像的呼吸运动校正方法研究

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To reduce the effects of respiratory motion in the quantitative analysis based on liver contrast-enhanced ultrasound (CEUS) image sequencesof single mode. The image gating method and the iterative registration method using model image were adopted to register liver contrast-enhanced ultrasound image sequences of single mode. The feasibility of the proposed respiratory motion correction method was explored preliminarily using 10 hepatocellular carcinomas CEUS cases. The positions of the lesions in the time series of 2D ultrasound images after correction were visually evaluated. Before and after correction, the quality of the weighted sum of transit time (WSTT) parametric images were also compared, in terms of the accuracy and spatial resolution. For the corrected and uncorrected sequences, their mean deviation values (mDVs) of time-intensity curve (TIC) fitting derived from CEUS sequences were measured. After the correction, the positions of the lesions in the time series of 2D ultrasound images were almost invariant. In contrast, the lesions in the uncorrected images all shifted noticeably. The quality of the WSTT parametric maps derived from liver CEUS image sequences were improved more greatly. Moreover, the mDVs of TIC fitting derived from CEUS sequences after the correction decreased by an average of 48.48±42.15. The proposed correction method could improve the accuracy of quantitative analysis based on liver CEUS image sequences of single mode, which would help in enhancing the differential diagnosis efficiency of liver tumors.
机译：基于肝脏对比增强超声（CEUS）图像序列的定量分析中，减少呼吸运动的影响。采用了使用模型图像的图像门控方法和迭代登记方法来注册单模的肝对比增强超声图像序列。初步使用10肝细胞癌CEUS案例初步探讨了所提出的呼吸运动校正方法的可行性。在视觉评估校正后2D超声图像时间序列中的病变的位置。在校正之前和之后，在准确度和空间分辨率方面，还比较了传输时间（WSTT）参数图像的加权和参数图像的质量。对于校正和未校正的序列，测量了从CEUS序列衍生的时间强度曲线（TIC）拟合的平均偏差值（MDV）。在校正之后，2D超声图像的时间序列中的病变的位置几乎不变。相反，未经校正图像中的病变全部偏移明显。源自肝CEU图像序列的WSTT参数图的质量更大。此外，在校正之后衍生自Ceus序列的TiC配件的MDV平均平均为48.48±42.15。所提出的校正方法可以提高基于单模肝CEU图像序列的定量分析的准确性，这将有助于提高肝肿瘤的差异诊断效率。

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