Department of Orthopedic Surgery, Mayo Clinic, Rochester, MN, United States; Department of Plastic, Reconstructive and Hand Surgery, Erasmus Medical Center, Rotterdam, the Netherlands.
Biomedical Engineering, Thorax Center, Erasmus Medical Center, Rotterdam, the Netherlands.
J Biomech. 2019 Mar 6;85:141-147. doi: 10.1016/j.jbiomech.2019.01.022. Epub 2019 Jan 21.
Inhibited movement patterns of carpal tunnel structures have been found in carpal tunnel syndrome (CTS) patients. Motion analysis on ultrasound images allows us to non-invasively study the (relative) movement of carpal tunnel structures and recently a speckle tracking method using singular value decomposition (SVD) has been proposed to optimize this tracking. This study aims to assess the reliability of longitudinal speckle tracking with SVD in both healthy volunteers and patients with CTS. Images from sixteen healthy volunteers and twenty-two CTS patients were used. Ultrasound clips of the third superficial flexor tendon and surrounding subsynovial connective tissue (SSCT) were acquired during finger flexion-extension. A custom made tracking algorithm was used for the analysis. Intra-class correlation coefficients (ICCs) were calculated using a single measure, two-way random model with absolute agreement and Bland-Altman plots were added for graphical representation. ICC values varied between 0.73 and 0.95 in the control group and 0.66-0.98 in the CTS patients, with the majority of the results classified as good to excellent. Tendon tracking showed higher reliability values compared to the SSCT, but values between the control and CTS groups were comparable. Speckle tracking with SVD can reliably be used to analyze longitudinal movement of anatomical structures with different sizes and compositions within the context of the carpal tunnel in both a healthy as well as a pathological state. Based on these results, this technique also holds relevant potential for areas where ultrasound based dynamic imaging requires quantification of motion.
在腕管综合征 (CTS) 患者中发现腕管结构的运动受限模式。超声图像上的运动分析允许我们无创性地研究腕管结构的(相对)运动,最近提出了一种使用奇异值分解 (SVD) 的散斑跟踪方法来优化这种跟踪。本研究旨在评估 SVD 纵向散斑跟踪在健康志愿者和 CTS 患者中的可靠性。使用了十六名健康志愿者和二十二名 CTS 患者的图像。在手指屈伸过程中采集了第三浅层屈肌腱和周围滑膜下结缔组织 (SSCT) 的超声片段。使用定制的跟踪算法进行分析。使用单测量、双向随机模型和绝对一致性计算了组内相关系数 (ICC),并添加了 Bland-Altman 图进行图形表示。对照组的 ICC 值在 0.73 到 0.95 之间,CTS 患者的 ICC 值在 0.66 到 0.98 之间,大多数结果被归类为良好到优秀。与 SSCT 相比,肌腱跟踪显示出更高的可靠性值,但对照组和 CTS 组之间的值相当。SVD 散斑跟踪可可靠地用于分析腕管内不同大小和组成的解剖结构的纵向运动,无论是在健康状态还是病理状态下。基于这些结果,该技术在需要量化运动的基于超声的动态成像领域也具有相关潜力。