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<p>Synthesizing brain MRI lesions is challenging due to the heterogeneity of lesion characteristics and the complexity of capturing fine-grained pathological information across MRI contrasts. Additionally, leveraging complementary information across multiple contrasts is difficult due to their diverse feature representations. To address these challenges, we propose a mutual learning-based framework with an adversarial diffusion approach. Our framework uses two denoising networks: one captures contrast-specific features to handle diverse representations, while the other emphasizes contrast-aware adaptation to model subtle pathological variations. A shared critic network ensures consistency, facilitates collaborative learning, and identifies critical lesion regions for focused synthesis. We benchmark our method on two public lesion datasets, treat each contrast as a missing target, and validate it on a brain tumor and multi-contrast healthy MRI dataset. Our approach outperforms state-of-the-art methods, delivering accurate lesion synthesis and superior downstream segmentation performance, highlighting the diagnostic value and accuracy of the proposed framework.</p>

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MU-Diff: a mutual learning diffusion model for synthetic MRI with Application for brain lesions

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年7月7日

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  • Sanuwani Dayarathna,
  • Yicheng Wu,
  • Jianfei Cai,
  • Tien-Tsin Wong,
  • Meng Law,
  • Kh Tohidul Islam,
  • Himashi Peiris,
  • Zhaolin Chen

摘要

Synthesizing brain MRI lesions is challenging due to the heterogeneity of lesion characteristics and the complexity of capturing fine-grained pathological information across MRI contrasts. Additionally, leveraging complementary information across multiple contrasts is difficult due to their diverse feature representations. To address these challenges, we propose a mutual learning-based framework with an adversarial diffusion approach. Our framework uses two denoising networks: one captures contrast-specific features to handle diverse representations, while the other emphasizes contrast-aware adaptation to model subtle pathological variations. A shared critic network ensures consistency, facilitates collaborative learning, and identifies critical lesion regions for focused synthesis. We benchmark our method on two public lesion datasets, treat each contrast as a missing target, and validate it on a brain tumor and multi-contrast healthy MRI dataset. Our approach outperforms state-of-the-art methods, delivering accurate lesion synthesis and superior downstream segmentation performance, highlighting the diagnostic value and accuracy of the proposed framework.