mlsanityData to 14 Oct 2026

Rectified flow-based prediction of post-treatment brain MRI from pre-radiotherapy priors for patients with glioma

arXiv 2603.08385 ↗First posted 9 Mar 2026eess.IV cs.CVcited by 1

On the map: Diffusion models → Medical imaging and segmentation

Abstract (arXiv)

Brain tumors result in 20 years of lost life on average. Standard therapies induce complex structural changes in the brain that are monitored through MRI. Recent developments in artificial intelligence (AI) enable conditional multimodal image generation from clinical data. In this study, we investigate AI-driven generation of follow-up MRI in patients with intracranial tumors through conditional image generation. This approach enables realistic modeling of post-radiotherapy changes, allowing for treatment optimization. The public SAILOR dataset of 25 patients was used to create a 2D rectified flow model conditioned on axial slices of pre-treatment MRI and RT dose maps. Cross-attention conditioning was used to incorporate temporal and chemotherapy data. The resulting images were validated with structural similarity index measure (SSIM), peak signal-to-noise ratio (PSNR), Dice scores and Jacobian determinants. The resulting model generates realistic follow-up MRI for any time point, while integrating treatment information. Comparing real versus predicted images, SSIM is 0.88, and PSNR is 22.82. Tissue segmentations from real versus predicted MRI result in a mean Dice-Sorensen coefficient (DSC) of 0.91. The rectified flow (RF) model enables up to 250x faster inference than Denoising Diffusion Probabilistic Models (DDPM). The proposed model generates realistic follow-up MRI in real-time, preserving both semantic and visual fidelity as confirmed by image quality metrics and tissue segmentations. Conditional generation allows counterfactual simulations by varying treatment parameters, producing predicted morphological changes. This capability has potential to support adaptive treatment dose planning and personalized outcome prediction for patients with intracranial tumors. Code will be available upon peer-reviewed publication at: https://github.com/SelenaIHuisman/RF-GlioPREDICT

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Placed in Medical imaging and segmentation, one of 14 directions in Diffusion models. Builds on 16 of its 51 references that are in the map. First posted on arXiv 9 Mar 2026.

Builds on

16 of 51 references.

Closest ancestors · 2

Same direction, newest first

  1. 2023Treatment-aware Diffusion Probabilistic Model for Longitudinal MRI Generation and Diffuse Glioma Growth Prediction11 Sep
  2. 2022Brain Imaging Generation with Latent Diffusion Models15 Sep

Other references · 14

  1. 2020Denoising Diffusion Implicit Modelscited by 9,781
  2. 2021Improved Denoising Diffusion Probabilistic Modelscited by 3,761
  3. 2022Flow Straight and Fast: Learning to Generate and Transfer Data with Rectified Flowcited by 3,700
  4. 2022DPM-Solver: A Fast ODE Solver for Diffusion Probabilistic Model Sampling in Around 10 Stepscited by 1,979
  5. 20163D U-Net: Learning Dense Volumetric Segmentation from Sparse Annotationcited by 1,476
  6. 2022Imagic: Text-Based Real Image Editing with Diffusion Modelscited by 1,178
  7. 2018VoxelMorph: A Learning Framework for Deformable Medical Image Registrationcited by 662
  8. 2018fastMRI: An Open Dataset and Benchmarks for Accelerated MRIcited by 470
  9. 2021SynthSeg: Segmentation of brain MRI scans of any contrast and resolution without retrainingcited by 237
  10. 2020SynthMorph: learning contrast-invariant registration without acquired imagescited by 140
  11. 2019Learning Conditional Deformable Templates with Convolutional Networkscited by 65
  12. 2022Robust machine learning segmentation for large-scale analysis of heterogeneous clinical brain MRI datasetscited by 51
2 more
  1. 2021Deep learning-based synthetic-CT generation in radiotherapy and PET: a reviewcited by 28
  2. 2024Comprehensive Multimodal Deep Learning Survival Prediction Enabled by a Transformer Architecture: A Multicenter Study in Glioblastomacited by 9

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 1.

1 paper.

  1. 20263D-GlioPREDICT: 3D Latent Diffusion for Post-Radiotherapy Brain MRI Prediction in Patients with Gliomacited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2025Pre to Post-Treatment Glioblastoma MRI Prediction using a Latent Diffusion Model
  2. 2025Mechanistic Learning with Guided Diffusion Models to Predict Spatio-Temporal Brain Tumor Growth
  3. 2026Brain-WM: Brain Glioblastoma World Model
  4. 2026A continually expandable foundation model for brain MRI
  5. 2025Diffusion Models for conditional MRI generation
  6. 2026Any2Any 3D Diffusion Models with Knowledge Transfer: A Radiotherapy Planning Study
  7. 2025Exploring Strategies for Personalized Radiation Therapy Part II Predicting Tumor Drift Patterns with Diffusion Models
  8. 2026Implicit Neural Representations for Multimodal Longitudinal Image Imputation and Interpolation