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    Area of Science:

    • Biomedical Imaging
    • Medical Technology
    • Computational Imaging

    Background:

    • Ultrasound molecular imaging (UMI) holds potential for early cancer detection.
    • Microbubbles functionalized to target cancer biomarkers are used as contrast agents.
    • Distinguishing bound microbubbles from free-floating ones is a key challenge in UMI.

    Purpose of the Study:

    • To develop and validate a fast GPU-based robust principal component analysis (RPCA) method for isolating bound microbubble signals in UMI.
    • To assess the accuracy and computational efficiency of the proposed RPCA method.
    • To evaluate the method's performance on both phantom and in vivo preclinical data.

    Main Methods:

    • A GPU-based robust principal component analysis (RPCA) algorithm was developed to separate bound from free-floating microbubbles.
    • Simulations and phantom experiments with stationary and flowing microbubbles were conducted to validate the method.
    • The RPCA method was applied to in vivo ultrasound molecular imaging data from mouse models of breast cancer.

    Main Results:

    • RPCA reconstruction using 20 frames achieved a Dice score of 0.95 and a computation time of 0.2 seconds in phantom studies.
    • The method demonstrated potential for real-time implementation.
    • On in vivo data, RPCA achieved a Dice score of 0.82 with Differenceкое-Enhanced Ultrasound (DTE), indicating good agreement.

    Conclusions:

    • The proposed GPU-based RPCA method effectively distinguishes bound microbubbles from free-floating ones in ultrasound molecular imaging.
    • The method shows high accuracy and computational efficiency, suitable for real-time applications.
    • This technique can improve the sensitivity and specificity of UMI for early cancer detection.