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    Projection-domain denoising using self-supervised learning offers superior image quality and accuracy for ultra low-dose cone-beam CT (CBCT) compared to image-domain methods. This approach enhances diagnostic capabilities in low-photon environments.

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    Area of Science:

    • Medical Imaging
    • Radiology
    • Computer Vision

    Background:

    • Ultra low-dose cone-beam CT (CBCT) imaging is crucial for reducing radiation exposure.
    • Traditional denoising methods struggle with the significant noise in ultra low-dose CBCT.
    • Self-supervised learning presents a promising avenue for noise reduction without extensive labeled data.

    Purpose of the Study:

    • To compare the efficacy of image-domain versus projection-domain self-supervised denoising for ultra low-dose CBCT.
    • To evaluate the impact of these denoising techniques on image quality and diagnostic accuracy.
    • To determine the optimal self-supervised denoising strategy for low-photon CBCT acquisition.

    Main Methods:

    • Image-domain denoising: Filtered backprojection reconstruction followed by self-supervised learning using blind-spot filtering.
    • Projection-domain denoising: Utilizing post-log projections as training data for a convolutional neural network.
    • Experimental validation across various ultra low-dose CBCT scenarios.

    Main Results:

    • The projection-domain self-supervised denoiser demonstrated superior performance over the image-domain approach.
    • Significant improvements in both image quality and quantitative accuracy were observed with the projection-domain method.
    • The projection-domain approach effectively mitigated noise artifacts inherent in ultra low-dose CBCT.

    Conclusions:

    • Self-supervised denoising in the projection domain is more effective for ultra low-dose CBCT than in the image domain.
    • This finding has significant implications for improving diagnostic confidence and patient safety in CBCT imaging.
    • The projection-domain strategy offers a robust solution for noise reduction in photon-starved imaging applications.