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    Model-based image reconstruction improves X-ray-induced acoustic computed tomography (XACT) imaging quality. Algorithms like LSQR minimization and model back-projection (MBP) reduce artifacts and enhance accuracy, paving the way for clinical translation.

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    Area of Science:

    • Medical Imaging
    • Biomedical Engineering
    • Computational Imaging

    Background:

    • X-ray-induced acoustic computed tomography (XACT) offers X-ray absorption contrast with acoustic detection.
    • Clinical translation of XACT is hindered by limited field of view, causing artifacts and reduced quantification accuracy.

    Purpose of the Study:

    • To demonstrate model-based XACT image reconstruction methods to overcome limited-view artifacts and improve accuracy.
    • To compare model-based reconstruction algorithms with standard back-projection (BP).

    Main Methods:

    • Implemented matrix-free regularized LSQR (MF-LSQR) minimization and noniterative model back-projection (MBP) schemes.
    • Validated algorithms numerically and experimentally using XACT measurements.
    • Evaluated reconstructions for limited-view data (up to 120° view).

    Main Results:

    • Model-based LSQR minimization effectively overcomes limited-view and noisy artifacts common with standard BP.
    • Model-based algorithms reduce ring artifacts from multichannel data acquisition non-uniformity.
    • MBP is efficient for structural information, while LSQR provides superior overall image quality.

    Conclusions:

    • Model-based image reconstruction significantly enhances XACT image quality compared to standard BP.
    • Combining model-based reconstruction with regularization addresses the limited-view problem in XACT.
    • These advancements facilitate the clinical translation of XACT imaging.