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Introduction: MRI and CT scans are crucial advancements in medical imaging techniques, playing a vital role in diagnosing conditions related to the gastrointestinal (GI) system. Each scan serves distinct purposes, targets specific areas, and requires unique nursing duties.
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Feasibility Study of a Diffusion-Based Model for Cross-Modal Generation of Knee MRI From X-Ray: Integrating External

Zhe Wang, Yung Hsin Chen, Aladine Chetouani

    IEEE Journal of Biomedical and Health Informatics
    |July 29, 2025
    PubMed
    Summary

    This study introduces a diffusion model to generate MRI scans from X-rays for knee osteoarthritis diagnosis. The AI-generated MRI scans show high visual and quantitative accuracy, improving accessibility to advanced imaging.

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    Area of Science:

    • Medical Imaging
    • Artificial Intelligence in Medicine
    • Radiology

    Background:

    • Knee osteoarthritis (KOA) diagnosis relies heavily on X-rays due to cost and accessibility.
    • Magnetic Resonance Imaging (MRI) offers superior soft tissue detail but is limited by cost and availability.
    • A gap exists in accessible, detailed imaging for KOA diagnosis.

    Purpose of the Study:

    • To assess the feasibility of generating MRI sequences from X-ray images using a diffusion model.
    • To improve the accessibility and detail of imaging for knee osteoarthritis diagnosis.
    • To explore the integration of patient-specific data to enhance generated MRI quality.

    Main Methods:

    • A diffusion-based generative model was employed, conditioned on X-ray images, target depth, and patient-specific features.
    • The model generated MRI sequences, with inference steps optimized for slice depth interpolation.
    • Ablation studies were performed to evaluate the impact of supplemental patient data.

    Main Results:

    • Generated MRI volumes demonstrated superior visual fidelity and quantitative performance (PSNR, SSIM) compared to other methods.
    • Increased inference steps improved the continuity and adjacent slice correlation of generated MRI volumes.
    • Incorporating patient-specific information significantly enhanced the accuracy and clinical relevance of the generated MRI.

    Conclusions:

    • Diffusion models can effectively generate high-quality MRI sequences from X-ray images for KOA.
    • The approach offers a potential solution to bridge the imaging gap for KOA diagnosis.
    • Integrating patient-specific data is crucial for improving the clinical utility of AI-generated MRI.