Ultrasound speckle tracking is a safe and non-invasive diagnostic tool to measure soft tissue deformation and strain. In orthopaedics, it could have broad application to measure how injury or surgery affects muscle, tendon or ligament biomechanics. However, its application requires custom tuning of the speckle-tracking algorithm then validation against gold-standard reference data. Implementing an experiment to acquire these data takes months and is expensive, and therefore prohibits use for new applications. Here, we present an alternative optimisation approach that automatically finds suitable machine and algorithmic settings without requiring gold-standard reference data. The optimisation routine consisted of two steps. First, convergence of the displacement field was tested to exclude the settings that would not track the underlying tissue motion (e.g. frame rates that were too low). Second, repeatability was maximised through a surrogate optimisation scheme. All settings that could influence the strain calculation were included, ranging from acquisition settings to post-processing smoothing and filtering settings, totalling >1,000,000 combinations of settings. The optimisation criterion minimised the normalised standard deviation between strain maps of repeat measures. The optimisation approach was validated for the medial collateral ligament (MCL) with quasi-static testing on porcine joints (n=3), and dynamic testing on a cadaveric human knee (n=1, female, aged 49). Porcine joints were fully dissected except for the MCL and loaded in a material-testing machine (0 to 3% strain at 0.2 Hz), which was captured using both ultrasound (>14 repeats per specimen) and optical digital image correlation (DIC). For the human cadaveric knee (undissected), 3 repeat ultrasound acquisitions were taken at 18 different anterior/posterior positions over the MCL while the knee was extended/flexed between 0° and 90° in a knee extension rig. Simultaneous optical tracking recorded the position of the ultrasound transducer, knee kinematics and the MCL attachments (which were digitised under direct visualisation post testing). Half of the data collected was used for optimisation of the speckle tracking algorithms for the porcine and human MCLs separately, with the remaining unseen data used as a validation test set.Abstract
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