Yan Dong, Li Qiang, Lin Chia-Wei, Shieh Jeng-Yi, Weng Wen-Chin, Tsui Po-Hsiang
School of Microelectronics, Tianjin University, Tianjin 300072, China.
Department of Physical Medicine and Rehabilitation, National Taiwan University Hospital Hsin-Chu Branch, Hsin-Chu 30059, Taiwan.
Entropy (Basel). 2020 Jun 28;22(7):715. doi: 10.3390/e22070715.
Information entropy of ultrasound imaging recently receives much attention in the diagnosis of Duchenne muscular dystrophy (DMD). DMD is the most common muscular disorder; patients lose their ambulation in the later stages of the disease. Ultrasound imaging enables routine examinations and the follow-up of patients with DMD. Conventionally, the probability distribution of the received backscattered echo signals can be described using statistical models for ultrasound parametric imaging to characterize muscle tissue. Small-window entropy imaging is an efficient nonmodel-based approach to analyzing the backscattered statistical properties. This study explored the feasibility of using ultrasound small-window entropy imaging in evaluating the severity of DMD. A total of 85 participants were recruited. For each patient, ultrasound scans of the gastrocnemius were performed to acquire raw image data for B-mode and small-window entropy imaging, which were compared with clinical diagnoses of DMD by using the receiver operating characteristic curve. The results indicated that entropy imaging can visualize changes in the information uncertainty of ultrasound backscattered signals. The median with interquartile range (IQR) of the entropy value was 4.99 (IQR: 4.98-5.00) for the control group, 5.04 (IQR: 5.01-5.05) for stage 1 patients, 5.07 (IQR: 5.06-5.07) for stage 2 patients, and 5.07 (IQR: 5.06-5.07) for stage 3 patients. The diagnostic accuracies were 89.41%, 87.06%, and 72.94% for ≥stage 1, ≥stage 2, and ≥stage 3, respectively. Comparisons with previous studies revealed that the small-window entropy imaging technique exhibits higher diagnostic performance than conventional methods. Its further development is recommended for potential use in clinical evaluations and the follow-up of patients with DMD.
超声成像的信息熵最近在杜氏肌营养不良症（DMD）的诊断中受到了广泛关注。DMD是最常见的肌肉疾病；患者在疾病后期会失去行走能力。超声成像能够对DMD患者进行常规检查和随访。传统上，接收到的后向散射回波信号的概率分布可以使用超声参数成像的统计模型来描述，以表征肌肉组织。小窗口熵成像是一种有效的基于非模型的方法，用于分析后向散射的统计特性。本研究探讨了使用超声小窗口熵成像评估DMD严重程度的可行性。共招募了85名参与者。对每位患者的腓肠肌进行超声扫描，以获取B模式和小窗口熵成像的原始图像数据，并通过使用受试者操作特征曲线将其与DMD的临床诊断进行比较。结果表明，熵成像可以可视化超声后向散射信号信息不确定性的变化。对照组熵值的中位数及四分位间距（IQR）为4.99（IQR：4.98 - 5.00），1期患者为5.04（IQR：5.01 - 5.05），2期患者为5.07（IQR：5.06 - 5.07），3期患者为5.07（IQR：5.06 - 5.07）。≥1期、≥2期和≥3期的诊断准确率分别为89.41%、87.06%和72.94%。与先前研究的比较表明，小窗口熵成像技术比传统方法具有更高的诊断性能。建议进一步开发该技术，以便在DMD患者的临床评估和随访中潜在应用。
