An Optimization Approach for Creating Application-specific Ultrasound Speckle Tracking Algorithms

Isabelle M Kuder; Mick Rock; Gareth G Jones; Andrew A Amis; Frederic B Cegla; Richard J van Arkel

doi:10.1016/j.ultrasmedbio.2024.03.012

An Optimization Approach for Creating Application-specific Ultrasound Speckle Tracking Algorithms

Ultrasound Med Biol. 2024 May 6:S0301-5629(24)00141-8. doi: 10.1016/j.ultrasmedbio.2024.03.012. Online ahead of print.

Authors

Isabelle M Kuder¹, Mick Rock², Gareth G Jones³, Andrew A Amis¹, Frederic B Cegla¹, Richard J van Arkel⁴

Affiliations

¹ Imperial College London Department of Mechanical Engineering, London, UK.
² DePuy Synthes, Leeds, UK.
³ Imperial College London Department of Surgery and Cancer, London, UK.
⁴ Imperial College London Department of Mechanical Engineering, London, UK. Electronic address: r.vanarkel@imperial.ac.uk.

PMID: 38714465
DOI: 10.1016/j.ultrasmedbio.2024.03.012

Abstract

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).

Conclusion: 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.

Keywords: Block matching; Elongation; Image correlation; Ligament; Musculoskeletal; Registration; Speckle tracking; Strain imaging; Tendon; Ultrasound elastography.