Parametric Imaging for Characterizing Focal Liver Lesions in Contrast-Enhanced Ultrasound
Parametric Imaging for Characterizing Focal Liver Lesions in Contrast-Enhanced Ultrasound
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DOI：
10.1109/tuffc.2010.1716
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发表时间：
2010-11-01
影响因子：
3.6
通讯作者：
Tranquart, Francois
中科院分区：
文献类型：
--
作者：
Rognin, Nicolas G.;Arditi, Marcel;Tranquart, Francois
肝局灶性病变的良恶性鉴别对肝脏疾病的诊断及局部或全身疾病的治疗方案的制定具有重要意义。基于表征的这种区分依赖于观察病变相对于相邻实质的动态血管模式（DVP），并且可以在团注后的对比增强超声成像期间进行评估。例如，血管瘤（即，良性病变）随着时间的推移表现出高度增强的特征，而转移瘤（即，恶性病变）经常在动脉期出现高增强病灶，之后总是低增强。这项工作的目的是开发一种新的参数成像技术，旨在将DVP签名映射到称为DVP参数图像的单个图像中，作为表征病变类型的诊断辅助工具。该方法包括处理图像的时间序列（1）结合图像运动校正和线性化的预处理，以在每个像素中导出与随时间的局部造影剂浓度成比例的回波功率信号;（2）通过曲线拟合优化进行信号建模，以计算每个像素中的差信号，作为从回波功率信号中减去相邻的实质动力学;（3）差异信号的分类;以及（4）参数图像绘制以表示分类的像素作为诊断的支持。DVP参数成像是对总共146个病变进行临床评估的对象，使用不同的医学超声系统进行成像。结果灵敏度和特异性分别为97%和91%，与医学文献中报道的灵敏度和特异性分别为81 - 95%和80 - 95%的评分相比，这是有利的。
The differentiation between benign and malignant focal liver lesions plays an important role in diagnosis of liver disease and therapeutic planning of local or general disease. This differentiation, based on characterization, relies on the observation of the dynamic vascular patterns (DVP) of lesions with respect to adjacent parenchyma, and may be assessed during contrast-enhanced ultrasound imaging after a bolus injection. For instance, hemangiomas (i.e., benign lesions) exhibit hyper-enhanced signatures over time, whereas metastases (i.e., malignant lesions) frequently present hyper-enhanced foci during the arterial phase and always become hypo-enhanced afterwards. The objective of this work was to develop a new parametric imaging technique, aimed at mapping the DVP signatures into a single image called a DVP parametric image, conceived as a diagnostic aid tool for characterizing lesion types. The methodology consisted in processing a time sequence of images (DICOM video data) using four consecutive steps: (1) pre-processing combining image motion correction and linearization to derive an echo-power signal, in each pixel, proportional to local contrast agent concentration over time; (2) signal modeling, by means of a curve-fitting optimization, to compute a difference signal in each pixel, as the subtraction of adjacent parenchyma kinetic from the echo-power signal; (3) classification of difference signals; and (4) parametric image rendering to represent classified pixels as a support for diagnosis. DVP parametric imaging was the object of a clinical assessment on a total of 146 lesions, imaged using different medical ultrasound systems. The resulting sensitivity and specificity were 97% and 91%, respectively, which compare favorably with scores of 81 to 95% and 80 to 95% reported in medical literature for sensitivity and specificity, respectively.