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    Area of Science:

    • Medical Imaging
    • Computed Tomography
    • Image Reconstruction

    Background:

    • Current computed tomography (CT) methods assume consistent data acquisition, but real-world data often exhibits inconsistencies.
    • Inconsistent data leads to artifacts in reconstructed CT images, compromising diagnostic accuracy.
    • Existing reconstruction algorithms struggle with inconsistent projection data.

    Purpose of the Study:

    • To develop a method for classifying CT data based on consistency levels.
    • To incorporate data consistency information into image reconstruction for artifact reduction.
    • To simultaneously reconstruct sub-images from spectrally consistent projection data.

    Main Methods:

    • An empirical data inconsistency metric (DIM) was developed to quantify projection data inconsistency at each view angle.
    • Cone beam CT projection data was classified into subsets based on DIM values.
    • A synchronized multi-artifact reduction with tomographic reconstruction algorithm was applied to reconstruct sub-images from these subsets.

    Main Results:

    • The proposed method successfully classified projection data into spectral consistency classes.
    • Simultaneous reconstruction of sub-images based on DIM values reduced artifacts in CT images.
    • Validation using numerical phantoms and human subject data demonstrated the method's practical utility.

    Conclusions:

    • The data inconsistency metric (DIM) provides a viable approach to characterize and manage inconsistencies in CT projection data.
    • Incorporating DIM-based classification into reconstruction frameworks enhances image quality by mitigating artifacts.
    • This method offers a practical solution for improving CT image reconstruction from inconsistent datasets.