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  3. Multi-sequence Guided Generation Of Contrast-enhanced Magnetic Resonance Imaging Using Diffusion Models.
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  3. Multi-sequence Guided Generation Of Contrast-enhanced Magnetic Resonance Imaging Using Diffusion Models.

Related Experiment Video

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Multi-Sequence Guided Generation of Contrast-Enhanced Magnetic Resonance Imaging Using Diffusion Models.

Yue Xu1, Xiaokun Zhou2, Wei Jiang3

  • 1Department of Clinical Medical Engineering, The First Affiliated Hospital of Nanjing Medical University, Jiangsu Province Hospital, Nanjing 210029, China.

Bioengineering (Basel, Switzerland)
|June 26, 2026

View abstract on PubMed

Summary
This summary is machine-generated.

This study introduces a novel AI model, the Difference-Aware Guided Control Network (DAGCN), to create high-quality contrast-enhanced MRI scans from non-contrast images. This method offers a safe alternative for patients who cannot receive gadolinium contrast agents.

Keywords:
ControlNetcontrast-enhanced magnetic resonance imagingdifference-aware guidancediffusion modelimage generation

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Area of Science:

  • Artificial Intelligence in Medical Imaging
  • Neuroimaging Techniques
  • Radiology

Background:

  • Contrast-enhanced magnetic resonance imaging (CE-MRI) is crucial for brain tumor management but limited by gadolinium-based contrast agent (GBCA) contraindications.
  • Developing non-contrast alternatives for CE-MRI is essential for patient safety and accessibility.

Purpose of the Study:

  • To develop a Diffusion model-based Difference-Aware Guided Control Network (DAGCN) for synthesizing high-quality contrast-enhanced T1-weighted MRI (T1-CE) from non-contrast T1-weighted images and an auxiliary sequence.
  • To provide a viable alternative to GBCA-enhanced MRI for patients with contraindications or requiring frequent examinations.

Main Methods:

  • A two-stage generative framework using the BraTS 2021 dataset: a Difference-Aware Fusion and Prediction (DAFP) module for cue localization and a ControlNet-guided diffusion model for image synthesis.
  • DAFP extracts complementary information from T1 and auxiliary (T2 or FLAIR) sequences to predict a lesion-related discrepancy map.
  • The discrepancy map guides the diffusion model's denoising process to generate synthetic T1-CE images.

    • Main Results:

    • DAGCN successfully synthesized T1-CE images with preserved anatomy and accurate lesion enhancement without contrast agents.
    • The model outperformed baseline methods in PSNR and NCC, with competitive SSIM and VIF.
    • Radiologist evaluations confirmed improved lesion enhancement fidelity, reduced false positives, and superior performance with FLAIR as the auxiliary sequence.

    Conclusions:

    • The DAGCN framework effectively synthesizes clinically valuable contrast-enhanced-like MRI from non-contrast multi-sequence inputs.
    • This approach offers a promising alternative for patients with GBCA contraindications.
    • The FLAIR-guided setting demonstrated superior lesion specificity, background clarity, and diagnostic quality.