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Double Transformer Super-Resolution for Breast Cancer ADC Images.

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    This study introduces a new AI model, DTformer, to improve the resolution of apparent diffusion coefficient (ADC) images from breast cancer scans. Enhanced ADC images improve tumor characteristic prediction, aiding clinical management.

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    Area of Science:

    • Medical Imaging
    • Artificial Intelligence
    • Radiomics

    Background:

    • Diffusion-weighted imaging (DWI) aids breast cancer management, but low resolution limits tumor characterization.
    • Apparent diffusion coefficient (ADC) images require higher resolution for accurate analysis.

    Purpose of the Study:

    • To develop a super-resolution (SR) method for ADC images using a novel deep learning network.
    • To evaluate the clinical utility of SR-ADC images in breast cancer radiomics analysis.

    Main Methods:

    • Proposed a double transformer-based network (DTformer) with a U-shaped encoder-decoder architecture (UTNet).
    • UTNet utilizes locally-enhanced Swin transformer (LeSwin-T) and convolutional transformer (Conv-T) blocks for feature extraction.
    • Incorporated a residual upsampling network (RUpNet) for image resolution enhancement.

    Main Results:

    • DTformer demonstrated superior performance in enhancing ADC image resolution.
    • Radiomics analysis of SR-ADC images showed improved prediction of tumor characteristics, including histological grade and HER2 status.

    Conclusions:

    • Super-resolution of ADC images can significantly enhance tumor characterization in breast cancer.
    • The proposed DTformer model offers a promising approach for improving diagnostic accuracy in breast cancer management.