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Updated: Jun 27, 2026
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Co-analysis of Brain Structure and Function using fMRI and Diffusion-weighted Imaging
Published on: November 8, 2012
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
Summary
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.
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.
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