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Edge feature extraction-based dual CNN for LDCT denoising.

Zhiyuan Li, Yi Liu, Kunpeng Li

    Journal of the Optical Society of America. A, Optics, Image Science, and Vision
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    This study introduces Ed-DuCNN, a novel dual convolutional neural network (CNN) designed for low-dose computed tomography (LDCT) denoising. It effectively balances noise reduction with edge and detail preservation in medical images.

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    Area of Science:

    • Medical Imaging
    • Artificial Intelligence
    • Computer Vision

    Background:

    • Low-dose computed tomography (LDCT) imaging is crucial for reducing radiation exposure.
    • Denoising LDCT images presents a challenge in preserving crucial edge and detail information while reducing noise and artifacts.

    Purpose of the Study:

    • To develop an advanced deep learning model for effective LDCT denoising.
    • To improve the balance between noise reduction and the preservation of fine details in LDCT images.

    Main Methods:

    • A dual convolutional neural network (CNN) architecture named Ed-DuCNN was proposed.
    • Ed-DuCNN incorporates an edge feature extraction subnet (Edge_Net) and a feature fusion subnet (Fusion_Net) with an attention mechanism.
    • Trainable Sobel convolutional blocks were used for initial edge detail extraction.

    Main Results:

    • The proposed Ed-DuCNN demonstrated competitive performance against state-of-the-art denoising methods.
    • Quantitative metrics and visual perceptual quality of denoised images were significantly improved.
    • The model successfully preserved edge details while effectively reducing noise and artifacts.

    Conclusions:

    • Ed-DuCNN offers a robust solution for LDCT denoising tasks.
    • The dual-branch architecture with edge feature extraction and attention-based fusion enhances image quality.
    • This method holds promise for improving diagnostic accuracy in low-dose CT imaging.