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    This study introduces a novel full-domain model for low-dose computed tomography (LDCT) reconstruction. The method improves image quality by considering noise properties, outperforming current state-of-the-art techniques.

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    Area of Science:

    • Medical Imaging
    • Computational Imaging
    • Radiology

    Background:

    • Low-dose computed tomography (LDCT) aims to minimize radiation exposure while preserving diagnostic image quality.
    • Existing deep learning methods for LDCT reconstruction often overlook the inherent noise characteristics of projection data, limiting performance.
    • Current frameworks treat LDCT reconstruction as a general inverse problem, failing to fully leverage domain-specific information.

    Purpose of the Study:

    • To develop a novel full-domain reconstruction model for LDCT that accounts for noise generation mechanisms.
    • To integrate statistical properties of intrinsic LDCT noise and prior information from both sinogram and image domains.
    • To enhance the performance and interpretability of LDCT reconstruction compared to existing methods.

    Main Methods:

    • A novel full-domain reconstruction model incorporating noise-generating and imaging mechanisms was proposed.
    • An optimization algorithm based on proximal gradient techniques was developed to solve the model.
    • The optimization algorithm was unrolled into a deep network, implicitly learning proximal operators for sinogram and image regularizers via two deep neural networks.

    Main Results:

    • The proposed method demonstrated significant improvements over state-of-the-art LDCT techniques.
    • Achieved > 2.9 dB increase in peak signal-to-noise ratio (PSNR).
    • Showcased > 1.4% promotion in structural similarity index measure (SSIM) and > 9 HU decrement in root mean square error (RMSE).

    Conclusions:

    • The developed full-domain model effectively addresses the noise characteristics in LDCT reconstruction.
    • The unrolled deep network provides an interpretable and effective approach for LDCT image reconstruction.
    • The proposed method offers superior performance in quantitative metrics, advancing the field of low-dose CT imaging.