Department of Biomedical Engineering, College of Chemistry and Life Sciences, Beijing University of Technology, Beijing, China.
Department of Medical Imaging and Radiological Sciences, College of Medicine, Chang Gung University, Taoyuan, Taiwan; Division of Pediatric Gastroenterology, Department of Pediatrics, Chang Gung Memorial Hospital at Linkou, Taoyuan, Taiwan; Liver Research Center, Chang Gung Memorial Hospital, Linkou, Taoyuan, Taiwan; Research Center for Radiation Medicine, Chang Gung University, Taoyuan, Taiwan.
Ultrason Sonochem. 2024 Jul;107:106910. doi: 10.1016/j.ultsonch.2024.106910. Epub 2024 May 17.
Ultrasound envelope statistics imaging, including ultrasound Nakagami imaging, homodyned-K imaging, and information entropy imaging, is an important group of quantitative ultrasound techniques for characterizing tissue scatterer distribution patterns, such as scatterer concentrations and arrangements. In this study, we proposed a machine learning approach to integrate the strength of multimodality quantitative ultrasound envelope statistics imaging techniques and applied it to detecting microwave ablation induced thermal lesions in porcine liver ex vivo. The quantitative ultrasound parameters included were homodyned-K α which is a scatterer clustering parameter related to the effective scatterer number per resolution cell, Nakagami m which is a shape parameter of the envelope probability density function, and Shannon entropy which is a measure of signal uncertainty or complexity. Specifically, the homodyned-K log(α), Nakagami-m, and horizontally normalized Shannon entropy parameters were combined as input features to train a support vector machine (SVM) model to classify thermal lesions with higher scatterer concentrations from normal tissues with lower scatterer concentrations. Through heterogeneous phantom simulations based on Field II, the proposed SVM model showed a classification accuracy above 0.90; the area accuracy and Dice score of higher-scatterer-concentration zone identification exceeded 83% and 0.86, respectively, with the Hausdorff distance <26. Microwave ablation experiments of porcine liver ex vivo at 60-80 W, 1-3 min showed that the SVM model achieved a classification accuracy of 0.85; compared with single log(α),m, or hNSE parametric imaging, the SVM model achieved the highest area accuracy (89.1%) and Dice score (0.77) as well as the smallest Hausdorff distance (46.38) of coagulation zone identification. We concluded that the proposed multimodality quantitative ultrasound envelope statistics imaging based SVM approach can enhance the capability to characterize tissue scatterer distribution patterns and has the potential to detect the thermal lesions induced by microwave ablation.
超声包络统计成像，包括超声 Nakagami 成像、同相检波 K 成像和信息熵成像，是一组重要的定量超声技术，用于描述组织散射体分布模式，如散射体浓度和排列。在这项研究中，我们提出了一种机器学习方法，将多种定量超声包络统计成像技术的优势相结合，并将其应用于检测猪离体肝脏微波消融诱导的热损伤。所使用的定量超声参数包括与每个分辨率单元内有效散射体数量相关的散射体聚类参数同相检波 Kα、包络概率密度函数形状参数 Nakagami m 以及信号不确定性或复杂度的度量 Shannon 熵。具体来说，同相检波 K 对数（α）、Nakagami m 和水平归一化 Shannon 熵参数被组合为输入特征，以训练支持向量机（SVM）模型来对具有更高散射体浓度的热损伤与具有较低散射体浓度的正常组织进行分类。通过基于 Field II 的异质体模拟，所提出的 SVM 模型的分类准确率超过 0.90；对于高散射体浓度区域的识别，其面积准确率和 Dice 评分分别超过 83%和 0.86，Hausdorff 距离<26。在 60-80 W、1-3 分钟的离体猪肝微波消融实验中，SVM 模型的分类准确率为 0.85；与单个 log(α)、m 或 hNSE 参数成像相比，SVM 模型实现了最高的面积准确率（89.1%）和 Dice 评分（0.77），以及最小的凝固区识别 Hausdorff 距离（46.38）。我们得出结论，所提出的基于 SVM 的多模态定量超声包络统计成像方法可以增强对组织散射体分布模式进行特征描述的能力，并且有可能检测到微波消融诱导的热损伤。
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