Quantitative Ultrasound Assessment of Duchenne Muscular Dystrophy Using Edge Detection Analysis.
Quantitative Ultrasound Assessment of Duchenne Muscular Dystrophy Using Edge Detection Analysis.
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DOI：
10.7863/ultra.15.04065
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发表时间：
2016-09
期刊：
影响因子：
--
通讯作者：
Wu JS
中科院分区：
文献类型：
--
作者：
Koppaka S;Shklyar I;Rutkove SB;Darras BT;Anthony BW;Zaidman CM;Wu JS
本研究的目的是探讨定量超声（US）的能力，使用边缘检测分析，以评估患者与杜氏肌营养不良症（DMD）。在机构审查委员会批准后，对19名DMD男孩和21名年龄匹配的对照参与者的6块肌肉（二头肌、三角肌、腕屈肌、四头肌、内侧腓肠肌和胫骨前肌）进行了单侧固定技术参数的US检查。感兴趣的肌肉通过追踪工具勾勒，肌肉的上三分之一用于分析。通过Canny边缘检测算法对每个肌肉的边缘检测值进行量化，然后归一化为肌肉区域中的边缘像素数。在多个灵敏度阈值（0.01-0.99）下提取边缘检测值，以确定区分DMD与正常的最佳阈值。生成每块肌肉的受试者工作曲线下面积值，并对6块肌肉取平均值。DMD组的平均年龄为8.8岁（范围3.0-14.3岁），对照组的平均年龄为8.7岁（范围3.4-13.5岁）。对于边缘检测，0.05的Canny阈值提供DMD和正常之间的最佳区分（曲线下面积，0.96; 95%置信区间，0.84-1.00）。根据Mann-Whitney检验，DMD和对照组之间的边缘检测值显著不同（P < .0001）。使用边缘检测的定量US成像可以在低Canny阈值下区分DMD患者和健康对照，在低Canny阈值下对小结构的区分是最好的。边缘检测本身或与其他测试组合可以潜在地作为肌肉疾病中疾病进展和治疗有效性的有用生物标志物。
The purpose of this study was to investigate the ability of quantitative ultrasound (US) using edge detection analysis to assess patients with Duchenne muscular dystrophy (DMD). After Institutional Review Board approval, US examinations with fixed technical parameters were performed unilaterally in 6 muscles (biceps, deltoid, wrist flexors, quadriceps, medial gastrocnemius, and tibialis anterior) in 19 boys with DMD and 21 age-matched control participants. The muscles of interest were outlined by a tracing tool, and the upper third of the muscle was used for analysis. Edge detection values for each muscle were quantified by the Canny edge detection algorithm and then normalized to the number of edge pixels in the muscle region. The edge detection values were extracted at multiple sensitivity thresholds (0.01–0.99) to determine the optimal threshold for distinguishing DMD from normal. Area under the receiver operating curve values were generated for each muscle and averaged across the 6 muscles. The average age in the DMD group was 8.8 years (range, 3.0–14.3 years), and the average age in the control group was 8.7 years (range, 3.4–13.5 years). For edge detection, a Canny threshold of 0.05 provided the best discrimination between DMD and normal (area under the curve, 0.96; 95% confidence interval, 0.84–1.00). According to a Mann-Whitney test, edge detection values were significantly different between DMD and controls (P < .0001). Quantitative US imaging using edge detection can distinguish patients with DMD from healthy controls at low Canny thresholds, at which discrimination of small structures is best. Edge detection by itself or in combination with other tests can potentially serve as a useful biomarker of disease progression and effectiveness of therapy in muscle disorders.
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