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LRD-ESR-Net: Pseudo-Healthy Image Synthesis Based on Low-Resolution Residual Decoupling and Edge-Prior-Guided

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    This study introduces LRD-ESR-Net, a novel framework for generating pseudo-healthy medical images from pathological scans. It effectively synthesizes realistic, pathology-free images, aiding in medical analysis and disease understanding.

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    Area of Science:

    • Medical Image Analysis
    • Artificial Intelligence in Healthcare
    • Computational Pathology

    Background:

    • Generating pathology-free images from pathological scans (pseudo-healthy synthesis) is crucial for tasks like anomaly detection.
    • Existing methods struggle with subject identity preservation and pathology restoration, especially for large or complex pathological regions.
    • Paired healthy and pathological scans of the same individual are infeasible to obtain for supervised learning.

    Purpose of the Study:

    • To develop a novel pseudo-healthy synthesis framework, LRD-ESR-Net, addressing limitations of existing methods.
    • To enable accurate generation of subject-specific, pathology-free images from pathological scans.
    • To improve downstream medical image analysis tasks through enhanced image synthesis.

    Main Methods:

    • Proposed LRD-ESR-Net framework combining low-resolution residual decoupling and edge-prior-guided super-resolution reconstruction.
    • Utilized a coarse-to-fine synthesis pipeline: residual decoupling network followed by a residual-shifting diffusion network with Canny edge maps.
    • Applied to pathological Magnetic Resonance Images (MRIs) for decoupling healthy tissues from pathological regions.

    Main Results:

    • LRD-ESR-Net demonstrated superior performance in pseudo-healthy image quality and anatomical preservation across multiple datasets (brain and liver).
    • Validated effectiveness in downstream tasks including low-contrast lesion segmentation and pre-/postoperative brain tumor MRI registration.
    • Showcased strong robustness and generalization capabilities across different organs, modalities, and lesion types.

    Conclusions:

    • LRD-ESR-Net effectively generates high-quality pseudo-healthy images, outperforming state-of-the-art methods.
    • The framework shows significant promise for improving medical image analysis and understanding disease-induced changes.
    • The approach offers a robust solution for synthesizing subject-specific, pathology-free medical images.