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    This study introduces a new iterative method for region-of-interest (ROI) tomography reconstruction. The approach effectively suppresses truncation artifacts in images, improving overall quality for interior tomography applications.

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    Area of Science:

    • Medical Imaging
    • Computational Imaging
    • Image Reconstruction

    Background:

    • Optimization-based iterative reconstruction is key for interior tomography.
    • Challenges include relating region-of-interest (ROI) images to truncated projection data.
    • Bright truncation artifacts arise from unsuitable data fidelity representations.

    Purpose of the Study:

    • To develop an iterative reconstruction method for direct ROI image reconstruction.
    • To suppress truncation artifacts and enhance image quality.
    • To address the challenge of data fidelity in ROI tomography.

    Main Methods:

    • Proposed a novel reconstruction approach using an optimization problem.
    • Implemented a two-step filtering-based data fidelity: data differentiation followed by Hilbert filtering.
    • Validated the method through numerical simulations and real data reconstructions.

    Main Results:

    • The proposed method effectively suppresses truncation artifacts.
    • Detailed features within the reconstructed images are preserved.
    • Both qualitative and quantitative results confirm the method's performance.

    Conclusions:

    • The two-step filtering strategy offers a simple and efficient approach for iterative reconstruction from truncated projections.
    • The method improves image quality in direct ROI image reconstruction.
    • It provides a viable solution for interior tomography challenges.