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Quantitative assessment of nerve echogenicity: Comparison of methods for evaluating nerve echogenicity in ulnar neuropathy at the elbow

机译:神经回声能力的定量评估:肘部尺神经病中评估神经回声能力的方法的比较

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Objective: The objective is to evaluate different methods to assess nerve echogenicity in a quantitative way by comparing a group of patients with ulnar neuropathy at the elbow (UNE) and a healthy control group, subsequently selecting the best tests for quantitative assessment of nerve echogenicity. Methods: We included 56 patients with UNE and 37 healthy controls. High-resolution ultrasonography images of the ulnar nerve at the level of the medial epicondyle were saved in JPEG, TIFF or DICOM format, with a 19. pixels/mm resolution. Hypoechoic fraction was calculated by using 1 manual and 16 automatic thresholding methods. Results: A significant difference in mean hypoechoic fraction between patients and controls was found using the following automatic thresholding methods: MaxEntropy 82% versus 74% (p< 0.001), RenyiEntropy 80% versus 69% (p< 0.001), Shanbhag 76% versus 68% (p= 0.002), Triangle 45% versus 58% (p= 0.036) and Yen 79% versus 67% (p< 0.001). Of these five tests a significant correlation between hypoechoic fraction and the cross-sectional area was found for: MaxEntropy 0.542 (p< 0.001), RenyiEntropy 0.558 (p< 0.001), Shanbhag 0.219 (p= 0.035) and Yen 0.513 (p< 0.001). The manual thresholding method did not detect a significant difference in hypoechoic fraction between patients and controls, and inter-rater agreement in hypoechoic fraction for manual thresholding was poor. Conclusion: Quantitative nerve echogenicity assessment can be successfully used to distinguish between a group of patients with UNE and a healthy control group, preferably by using the MaxEntropy, RenyiEntropy or Yen methods. Significance: Automatic thresholding techniques using the MaxEntropy, RenyiEntropy or Yen methods are the best quantitative tests, and these quantitative measures can probably be used in further studies evaluating echogenicity in mono- and polyneuropathies.
机译:目的:目的是通过比较一组肘部尺神经病患者(UNE)和健康对照组,然后选择最佳定量量化神经回声性的最佳测试方法,以定量方式评估评估神经回声性的不同方法。方法:我们纳入了56例UNE患者和37例健康对照者。尺神经的上con内侧水平的高分辨率超声图像以JPEG,TIFF或DICOM格式保存,分辨率为19.像素/毫米。低回声分数通过使用1手动和16自动阈值法计算。结果:使用以下自动阈值方法,发现患者和对照组之间的平均低回声分数存在显着差异:MaxEntropy 82%vs 74%(p <0.001),RenyiEntropy 80%vs 69%(p <0.001),Shambhag 76%vs 68%(p = 0.002),三角形45%对58%(p = 0.036)和日元79%对67%(p <0.001)。在这五项测试中,发现低回声率与横截面积之间存在显着相关性:最大熵0.542(p <0.001),Renyi熵0.558(p <0.001),Shambhag 0.219(p = 0.035)和日元0.513(p <0.001) )。手动阈值方法在患者和对照组之间未发现低回声分数的显着差异,并且对于手动阈值的低回声分数的评估者之间的一致性较差。结论:定量神经回声评估可成功地用于区分UNE患者组和健康对照组,最好使用MaxEntropy,RenyiEntropy或Yen方法。意义:使用MaxEntropy,RenyiEntropy或Yen方法的自动阈值技术是最佳的定量测试,这些定量方法可能可用于进一步研究评估单神经病和多神经病中的回声性。

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