目的:超声斑点追踪可以在体内测量软组织的变形或应变,提供一种非侵入性诊断工具来量化组织健康。然而,采用新的领域是具有挑战性的,因为算法需要调整与黄金标准的参考数据,是昂贵的或不切实际的获取。这里,我们提出了一种新的优化方法,只需要重复测量,可以为参考数据可能不容易获得或难以掌握的新应用程序获取。
方法:使用超声捕获三个准静态负载的猪窒息关节的内侧副韧带(MCL)的软组织运动,和动态加载的人类尸体膝关节的内侧韧带结构。使用训练子集,使用代理优化为猪和人的韧带创建了自定义斑点跟踪算法,其目的是通过最小化用于重复测量的计算应变图的归一化标准偏差来最大化可重复性。然后,通过将超声应变与数字图像相关(DIC)表面应变(猪样本)和光学跟踪的韧带附件(人类样本)的长度变化值进行比较,使用看不见的测试子集来验证调谐算法。
结果:经过1500次迭代,基于猪和人类训练数据的优化例程收敛到重复应变图的归一化标准偏差的相似值(猪:0.19,人类:0.26)。使用调谐算法为独立测试集计算的超声应变与猪准静态测量的DIC测量(R>0.99,RMSE<0.59%)和动态人类数据集的跟踪韧带附件之间的长度变化(RMSE<6.28%)紧密匹配。此外,屈曲过程中人体标本内侧韧带结构的应变与韧带的前/后位置密切相关(R>0.91)。
结论:使用基于可重复性的优化例程调整超声斑点追踪算法,对于膝关节内侧韧带结构,可以获得稳健可靠的结果,RMSE较低。该工具在其他软组织位移或应变测量应用中可以同样有益,并且可以帮助开发新的超声诊断工具以评估软组织生物力学。
OBJECTIVE: Ultrasound speckle tracking enables in vivo measurement of soft tissue deformation or strain, providing a non-invasive diagnostic tool to quantify tissue health. However, adoption into new fields is challenging since algorithms need to be tuned with gold-standard reference data that are expensive or impractical to acquire. Here, we present a novel optimization approach that only requires repeated measurements, which can be acquired for new applications where reference data might not be readily available or difficult to get hold of.
METHODS: Soft tissue motion was captured using ultrasound for the medial collateral ligament (MCL) of three quasi-statically loaded porcine
stifle joints, and medial ligamentous structures of a dynamically loaded human cadaveric knee joint. Using a training subset, custom speckle tracking algorithms were created for the porcine and human ligaments using surrogate optimization, which aimed to maximize repeatability by minimizing the normalized standard deviation of calculated strain maps for repeat measurements. An unseen test subset was then used to validate the tuned algorithms by comparing the ultrasound strains to digital image correlation (DIC) surface strains (porcine specimens) and length change values of the optically tracked ligament attachments (human specimens).
RESULTS: After 1500 iterations, the optimization routine based on the porcine and human training data converged to similar values of normalized standard deviations of repeat strain maps (porcine: 0.19, human: 0.26). Ultrasound strains calculated for the independent test sets using the tuned algorithms closely matched the DIC measurements for the porcine quasi-static measurements (R > 0.99, RMSE < 0.59%) and the length change between the tracked ligament attachments for the dynamic human dataset (RMSE < 6.28%). Furthermore, strains in the medial ligamentous structures of the human specimen during flexion showed a strong correlation with anterior/posterior position on the ligaments (R > 0.91).
CONCLUSIONS: Adjusting ultrasound speckle tracking algorithms using an optimization routine based on repeatability led to robust and reliable results with low RMSE for the medial ligamentous structures of the knee. This tool may be equally beneficial in other soft-tissue displacement or strain measurement applications and can assist in the development of novel ultrasonic diagnostic tools to assess soft tissue biomechanics.