Speckle Tracking of Tendon Displacement in the Carpal Tunnel: Improved Quantification Using Singular Value Decomposition

Raja Sekhar Bandaru; Stefanie Evers; Ruud W. Selles; Andrew R. Thoreson; Peter C. Amadio; Steven E. Hovius; Johan G. Bosch

doi:10.1109/JBHI.2018.2822548

Speckle Tracking of Tendon Displacement in the Carpal Tunnel: Improved Quantification Using Singular Value Decomposition

Raja Sekhar Bandaru^*
, Stefanie Evers
, Ruud W. Selles
, Andrew R. Thoreson
, Peter C. Amadio
, Steven E. Hovius
, Johan G. Bosch

^*Corresponding author for this work

Mayo Clinic Rochester, MN
Xpert Clinic (Hilversum)

Research output: Contribution to journal › Article › Academic › peer-review

12 Citations (Scopus)

Abstract

Ultrasound is a real-time image modality enabling the analysis of tendon dynamics for the diagnosis of carpal tunnel syndrome. Automatic tendon displacement quantification algorithms based on speckle tracking generally suffer from underestimation due to stationary background present in the tendon region. We propose an improved quantification method using singular value decomposition (SVD) filtering to suppress the clutter. The accuracy of our improved speckle tracking (IST) method was validated against a ground truth and compared to the accuracy of our original block matching (OBM) algorithm and commercial tissue tracking (CTT) software. The methods were evaluated in experiments involving six human cadaver arms. The ground-truth displacements were generated by tracking metal markers inserted in the tendons. The relative displacement errors with respect to the ground truth for IST were 12 ± 16.9%, which was significantly lower than for OBM (19.7 ± 20.8%) and for CTT (25.8 ± 18.4%). These findings show that SVD filtering improves the tendon tracking by reducing underestimation due to clutter.

Original language	English
Article number	8329509
Pages (from-to)	817-824
Number of pages	8
Journal	IEEE Journal of Biomedical and Health Informatics
Volume	23
Issue number	2
DOIs	https://doi.org/10.1109/JBHI.2018.2822548
Publication status	Published - Mar 2019

Bibliographical note

Funding Information: This work was supported by the National Institutes of Health/National Institute of Arthritis and Musculoskeletal and Skin Diseases under Grant RO1 AR62613.

Publisher Copyright: © 2018 IEEE.

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@article{8407f48b22de4adf94948d3e368c9316,

title = "Speckle Tracking of Tendon Displacement in the Carpal Tunnel: Improved Quantification Using Singular Value Decomposition",

abstract = "Ultrasound is a real-time image modality enabling the analysis of tendon dynamics for the diagnosis of carpal tunnel syndrome. Automatic tendon displacement quantification algorithms based on speckle tracking generally suffer from underestimation due to stationary background present in the tendon region. We propose an improved quantification method using singular value decomposition (SVD) filtering to suppress the clutter. The accuracy of our improved speckle tracking (IST) method was validated against a ground truth and compared to the accuracy of our original block matching (OBM) algorithm and commercial tissue tracking (CTT) software. The methods were evaluated in experiments involving six human cadaver arms. The ground-truth displacements were generated by tracking metal markers inserted in the tendons. The relative displacement errors with respect to the ground truth for IST were 12 ± 16.9\%, which was significantly lower than for OBM (19.7 ± 20.8\%) and for CTT (25.8 ± 18.4\%). These findings show that SVD filtering improves the tendon tracking by reducing underestimation due to clutter.",

author = "Bandaru, \{Raja Sekhar\} and Stefanie Evers and Selles, \{Ruud W.\} and Thoreson, \{Andrew R.\} and Amadio, \{Peter C.\} and Hovius, \{Steven E.\} and Bosch, \{Johan G.\}",

note = "Funding Information: This work was supported by the National Institutes of Health/National Institute of Arthritis and Musculoskeletal and Skin Diseases under Grant RO1 AR62613. Publisher Copyright: {\textcopyright} 2018 IEEE.",

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T2 - Improved Quantification Using Singular Value Decomposition

AU - Bandaru, Raja Sekhar

AU - Evers, Stefanie

AU - Selles, Ruud W.

AU - Thoreson, Andrew R.

AU - Amadio, Peter C.

AU - Hovius, Steven E.

AU - Bosch, Johan G.

N1 - Funding Information: This work was supported by the National Institutes of Health/National Institute of Arthritis and Musculoskeletal and Skin Diseases under Grant RO1 AR62613. Publisher Copyright: © 2018 IEEE.

PY - 2019/3

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N2 - Ultrasound is a real-time image modality enabling the analysis of tendon dynamics for the diagnosis of carpal tunnel syndrome. Automatic tendon displacement quantification algorithms based on speckle tracking generally suffer from underestimation due to stationary background present in the tendon region. We propose an improved quantification method using singular value decomposition (SVD) filtering to suppress the clutter. The accuracy of our improved speckle tracking (IST) method was validated against a ground truth and compared to the accuracy of our original block matching (OBM) algorithm and commercial tissue tracking (CTT) software. The methods were evaluated in experiments involving six human cadaver arms. The ground-truth displacements were generated by tracking metal markers inserted in the tendons. The relative displacement errors with respect to the ground truth for IST were 12 ± 16.9%, which was significantly lower than for OBM (19.7 ± 20.8%) and for CTT (25.8 ± 18.4%). These findings show that SVD filtering improves the tendon tracking by reducing underestimation due to clutter.

AB - Ultrasound is a real-time image modality enabling the analysis of tendon dynamics for the diagnosis of carpal tunnel syndrome. Automatic tendon displacement quantification algorithms based on speckle tracking generally suffer from underestimation due to stationary background present in the tendon region. We propose an improved quantification method using singular value decomposition (SVD) filtering to suppress the clutter. The accuracy of our improved speckle tracking (IST) method was validated against a ground truth and compared to the accuracy of our original block matching (OBM) algorithm and commercial tissue tracking (CTT) software. The methods were evaluated in experiments involving six human cadaver arms. The ground-truth displacements were generated by tracking metal markers inserted in the tendons. The relative displacement errors with respect to the ground truth for IST were 12 ± 16.9%, which was significantly lower than for OBM (19.7 ± 20.8%) and for CTT (25.8 ± 18.4%). These findings show that SVD filtering improves the tendon tracking by reducing underestimation due to clutter.

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Speckle Tracking of Tendon Displacement in the Carpal Tunnel: Improved Quantification Using Singular Value Decomposition

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