Abstract：
Ultrasound information entropy is a flexible approach for analyzing ultrasound backscattering. Shannon entropy imaging based on probability distribution histograms (PDHs) has been implemented as a promising method for tissue characterization and diagnosis. However, the bin number affects the stability of entropy estimation. In this study, we introduced the k-nearest neighbor (KNN) algorithm to estimate entropy values and proposed ultrasound KNN entropy imaging. The proposed KNN estimator leveraged the Euclidean distance between data samples, rather than the histogram bins by conventional PDH estimators. We also proposed cumulative relative entropy (CRE) imaging to analyze time-series radiofrequency signals and applied it to monitor thermal lesions induced by microwave ablation (MWA). Computer simulation phantom experiments were conducted to validate and compare the performance of the proposed KNN entropy imaging, the conventional PDH entropy imaging, and Nakagami-m parametric imaging in detecting the variations of scatterer densities and visualizing inclusions. Clinical data of breast lesions were analyzed, and porcine liver MWA experiments ex vivo were conducted to validate the performance of KNN entropy imaging in classifying benign and malignant breast tumors and monitoring thermal lesions, respectively. Compared with PDH, the entropy estimation based on KNN was less affected by the tuning parameters. KNN entropy imaging was more sensitive to changes in scatterer densities and performed better visualizable capability than typical Shannon entropy (TSE) and Nakagami-m parametric imaging. Among different imaging methods, KNN-based Shannon entropy (KSE) imaging achieved the higher accuracy in classification of benign and malignant breast tumors and KNN-based CRE imaging had larger lesion-to-normal contrast when monitoring the ablated areas during MWA at different powers and treatment durations. Ultrasound KNN entropy imaging is a potential quantitative ultrasound approach for tissue characterization.
摘要：
超声信息熵是分析超声反向散射的灵活方法。基于概率分布直方图（PDHs）的香农熵成像已被实现为一种有前途的组织表征和诊断方法。然而,bin数影响熵估计的稳定性。在这项研究中,我们引入了k-最近邻(KNN)算法来估计熵值，并提出了超声KNN熵成像。提出的KNN估计器利用了数据样本之间的欧几里德距离，而不是传统的PDH估计器的直方图箱。我们还提出了累积相对熵（CRE）成像来分析时间序列射频信号，并将其应用于监测微波消融（MWA）引起的热损伤。进行了计算机模拟体模实验，以验证和比较所提出的KNN熵成像的性能，传统的PDH熵成像，和Nakagami-m参数成像在检测散射体密度变化和可视化夹杂物方面。分析乳腺病变的临床资料,和离体猪肝MWA实验，以验证KNN熵成像在分类乳腺良恶性肿瘤和监测热病变中的性能，分别。与PDH相比,基于KNN的熵估计受调谐参数的影响较小。与典型的Shannon熵（TSE）和Nakagami-m参数成像相比，KNN熵成像对散射体密度的变化更敏感，并且具有更好的可视化能力。在不同的成像方法中，基于KNN的Shannon熵（KSE）成像在良性和恶性乳腺肿瘤的分类中实现了更高的准确性，并且基于KNN的CRE成像在不同功率和治疗持续时间的MWA期间监测消融区域时具有更大的病变与正常对比。超声KNN熵成像是一种潜在的用于组织表征的定量超声方法。
