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A Biophysically-Conditioned Generative Framework for 3D Brain Tumor MRI Synthesis

arXiv 2510.09365 ↗First posted 10 Oct 2025eess.IV cs.CV cs.LGcited by 0

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Abstract (arXiv)

Magnetic resonance imaging (MRI) inpainting supports numerous clinical and research applications. We introduce the first generative model that conditions on voxel-level, continuous tumor concentrations to synthesize high-fidelity brain tumor MRIs. For the BraTS 2025 Inpainting Challenge, we adapt this architecture to the complementary task of healthy tissue restoration by setting the tumor concentrations to zero. Our latent diffusion model conditioned on both tissue segmentations and the tumor concentrations generates 3D spatially coherent and anatomically consistent images for both tumor synthesis and healthy tissue inpainting. For healthy inpainting, we achieve a PSNR of 18.5, and for tumor inpainting, we achieve 17.4. Our code is available at: https://github.com/valentin-biller/ldm.git

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 12 of its 22 references that are in the map. First posted on arXiv 10 Oct 2025.

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References · 12

  1. 2020Denoising Diffusion Probabilistic Modelscited by 20,851
  2. 2021High-Resolution Image Synthesis with Latent Diffusion Modelscited by 18,694
  3. 2023Adding Conditional Control to Text-to-Image Diffusion Modelscited by 5,466
  4. 2021The RSNA-ASNR-MICCAI BraTS 2021 Benchmark on Brain Tumor Segmentation and Radiogenomic Classificationcited by 507
  5. 2022MONAI: An open-source framework for deep learning in healthcarecited by 438
  6. 2024MAISI: Medical AI for Synthetic Imagingcited by 86
  7. 2023Conditional Diffusion Models for Semantic 3D Brain MRI Synthesiscited by 85
  8. 2022The Swiss Army Knife for Image-to-Image Translation: Multi-Task Diffusion Modelscited by 24
  9. 2021Learn-Morph-Infer: a new way of solving the inverse problem for brain tumor modelingcited by 19
  10. 2023Treatment-aware Diffusion Probabilistic Model for Longitudinal MRI Generation and Diffuse Glioma Growth Predictioncited by 19
  11. 2024A Learnable Prior Improves Inverse Tumor Growth Modelingcited by 9
  12. 2024Physics-Regularized Multi-Modal Image Assimilation for Brain Tumor Localizationcited by 9

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  1. 2024Denoising Diffusion Models for Inpainting of Healthy Brain Tissue
  2. 2025DiffKAN-Inpainting: KAN-based Diffusion model for brain tumor inpainting
  3. 2025Hierarchical Diffusion Framework for Pseudo-Healthy Brain MRI Inpainting with Enhanced 3D Consistency
  4. 2022Multitask Brain Tumor Inpainting with Diffusion Models: A Methodological Report
  5. 2026PCaPaint: Prostate Cancer Inpainting by Mitigating Shortcut Learning
  6. 2026Longitudinal Lesion Inpainting in Brain MRI via 3D Region Aware Diffusion
  7. 2024Promptable Counterfactual Diffusion Model for Unified Brain Tumor Segmentation and Generation with MRIs
  8. 2025fastWDM3D: Fast and Accurate 3D Healthy Tissue Inpainting