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SLaM-DiMM: Shared Latent Modeling for Diffusion Based Missing Modality Synthesis in MRI

arXiv 2509.16019 ↗First posted 19 Sep 2025eess.IV cs.CVcited by 0

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Abstract (arXiv)

Brain MRI scans are often found in four modalities, consisting of T1-weighted with and without contrast enhancement (T1ce and T1w), T2-weighted imaging (T2w), and Flair. Leveraging complementary information from these different modalities enables models to learn richer, more discriminative features for understanding brain anatomy, which could be used in downstream tasks such as anomaly detection. However, in clinical practice, not all MRI modalities are always available due to various reasons. This makes missing modality generation a critical challenge in medical image analysis. In this paper, we propose SLaM-DiMM, a novel missing modality generation framework that harnesses the power of diffusion models to synthesize any of the four target MRI modalities from other available modalities. Our approach not only generates high-fidelity images but also ensures structural coherence across the depth of the volume through a dedicated coherence enhancement mechanism. Qualitative and quantitative evaluations on the BraTS-Lighthouse-2025 Challenge dataset demonstrate the effectiveness of the proposed approach in synthesizing anatomically plausible and structurally consistent results. Code is available at https://github.com/BheeshmSharma/SLaM-DiMM-MICCAI-BraTS-Challenge-2025.

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References · 14

  1. 2020Denoising Diffusion Probabilistic Modelscited by 21,076
  2. 2021High-Resolution Image Synthesis with Latent Diffusion Modelscited by 18,863
  3. 2021Improved Denoising Diffusion Probabilistic Modelscited by 3,761
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  5. 2021UNETR: Transformers for 3D Medical Image Segmentationcited by 959
  6. 2022Swin UNETR: Swin Transformers for Semantic Segmentation of Brain Tumors in MRI Imagescited by 830
  7. 2021ResViT: Residual vision transformers for multi-modal medical image synthesiscited by 177
  8. 2018Image Synthesis in Multi-Contrast MRI with Conditional Generative Adversarial Networkscited by 132
  9. 2024WDM: 3D Wavelet Diffusion Models for High-Resolution Medical Image Synthesiscited by 56
  10. 2024cWDM: Conditional Wavelet Diffusion Models for Cross-Modality 3D Medical Image Synthesiscited by 19
  11. 2025BraTS orchestrator : Democratizing and Disseminating state-of-the-art brain tumor image analysiscited by 8
  12. 2024BraSyn 2023 challenge: Missing MRI synthesis and the effect of different learning objectivescited by 7
2 more
  1. 2024Two-Stage Approach for Brain MR Image Synthesis: 2D Image Synthesis and 3D Refinementcited by 2
  2. 2025Multi-modal brain MRI synthesis based on SwinUNETRcited by 1

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  2. 2023CoLa-Diff: Conditional Latent Diffusion Model for Multi-Modal MRI Synthesis
  3. 2024Physics-Informed Latent Diffusion for Multimodal Brain MRI Synthesis
  4. 2023Make-A-Volume: Leveraging Latent Diffusion Models for Cross-Modality 3D Brain MRI Synthesis
  5. 2026Multiscale Structure-Guided Latent Diffusion for Multimodal MRI Translation
  6. 2026Posterior Samplings are Missing Modalities Generators for Medical Image Translation
  7. 2026Exploiting Completeness Perception with Diffusion Transformer for Unified 3D MRI Synthesis
  8. 2026Multimodal synthesis of MRI and tabular data with diffusion in a joint latent space via cross-attention