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Measuring Local Tissue Strains in Tendons via Open-Source Digital Image Correlation
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Improving Ultrasound Lateral Strain Estimation Accuracy using Log Compression of Regularized Correlation Function.

Rashid Al Mukaddim, Tomy Varghese

    Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
    |October 6, 2020
    PubMed
    Summary
    This summary is machine-generated.

    Log compression of the normalized cross-correlation (NCC) function improves ultrasound strain imaging accuracy. This method enhances lateral estimation and makes Bayesian regularization robust to over-regularization artifacts, improving displacement error reduction.

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    Area of Science:

    • Medical Imaging
    • Biomedical Engineering
    • Signal Processing

    Background:

    • Ultrasound strain imaging utilizes normalized cross-correlation (NCC) for displacement estimation.
    • Signal decorrelation can corrupt NCC, leading to significant displacement errors and reduced accuracy.
    • Bayesian regularization iteratively improves NCC but risks over-regularization if not carefully applied.

    Purpose of the Study:

    • To propose log compression of the regularized NCC function to enhance subsample estimation in ultrasound strain imaging.
    • To evaluate the effectiveness of log compression combined with parabolic interpolation for improving lateral and axial displacement estimations.
    • To assess the robustness of Bayesian regularization against over-regularization artifacts using the proposed log compression method.

    Main Methods:

    • Numerical simulations were performed using uniform and inclusion phantoms to test the proposed method.
    • The performance of parabolic interpolation was compared before and after log compression of the regularized NCC function.
    • Lateral and axial estimation results were analyzed, focusing on signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR).

    Main Results:

    • Log compression significantly improved lateral estimation results, achieving a 10 dB higher lateral SNR in a uniform phantom at 3% strain.
    • Lateral CNR improved by 1.81 dB with the proposed method in an inclusion phantom at 3% strain.
    • No significant difference in axial estimation was observed, likely due to inherent phase information and high sampling frequency.

    Conclusions:

    • Log compression of the regularized NCC function is an effective method for improving subsample estimation in ultrasound strain imaging.
    • The proposed approach enhances lateral estimation accuracy and robustness against over-regularization artifacts associated with Bayesian regularization.
    • This simple log compression technique offers a practical solution for mitigating errors in ultrasound-based strain imaging.