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Ultrasonic Wave-Speed Diffraction Tomography With Undersampled Data Using Virtual Transducers.

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    This study introduces a new iterative method using virtual transducers to improve ultrasonic diffraction tomography. The technique enhances high-resolution imaging from undersampled data, crucial for medical and industrial applications.

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    Area of Science:

    • Physics
    • Engineering
    • Medical Imaging

    Background:

    • Ultrasonic diffraction tomography (UDT) enables high-resolution wave-speed map imaging.
    • Complete sensor arrays and fully sampled data are ideal for UDT, but spatial undersampling causes image distortion and artifacts.
    • Existing methods struggle with incomplete measurement data.

    Purpose of the Study:

    • To develop an iterative approach for high-resolution UDT using undersampled measurements.
    • To improve the accuracy and robustness of ultrasonic tomography with limited data.
    • To address image distortions caused by spatial undersampling.

    Main Methods:

    • An iterative approach employing virtual transducers is proposed.
    • An extent constraint based on object shape is applied at each iteration for regularization.
    • Virtual measurement data are calculated using a forward model and combined with original data.
    • The combined dataset is used for tomographic reconstruction in subsequent iterations.

    Main Results:

    • The iterative method successfully achieves high-resolution tomographic imaging with undersampled data.
    • Improvements in robustness and accuracy of ultrasonic tomography were demonstrated.
    • Numerical results with complex wave-speed maps and experimental validation using guided ultrasonic waves were presented.

    Conclusions:

    • The proposed iterative method with virtual transducers effectively overcomes limitations of undersampled data in UDT.
    • This approach enhances image quality and reliability for applications in medical diagnosis and nondestructive evaluation.
    • The technique offers a practical solution for scenarios where full data acquisition is challenging.