# 3D-GlioPREDICT: 3D Latent Diffusion for Post-Radiotherapy Brain MRI Prediction in Patients with Glioma

arXiv: https://arxiv.org/abs/2606.05113 · HTML: https://mlsanity.com/paper/2606.05113/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in Medical imaging and segmentation, one of 14 directions in Diffusion models.
- Builds on 15 of its 28 references that are in the map.
- First posted on arXiv 3 Jun 2026.

## Placed in

- Diffusion models / [Medical imaging and segmentation](https://mlsanity.com/diffusion/medical-imaging-and-segmentation/)

## Builds on (most cited first, 15 of 15)

- [Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2006.11239/) (2020)
- [High-Resolution Image Synthesis with Latent Diffusion Models](https://mlsanity.com/paper/2112.10752/) (2021)
- [Adding Conditional Control to Text-to-Image Diffusion Models](https://mlsanity.com/paper/2302.05543/) (2023)
- [Scaling Rectified Flow Transformers for High-Resolution Image Synthesis](https://mlsanity.com/paper/2403.03206/) (2024)
- [Flow Straight and Fast: Learning to Generate and Transfer Data with Rectified Flow](https://mlsanity.com/paper/2209.03003/) (2022)
- [Brain Imaging Generation with Latent Diffusion Models](https://mlsanity.com/paper/2209.07162/) (2022)
- [SynthSeg: Segmentation of brain MRI scans of any contrast and resolution without retraining](https://mlsanity.com/paper/2107.09559/) (2021)
- [SynthMorph: learning contrast-invariant registration without acquired images](https://mlsanity.com/paper/2004.10282/) (2020)
- [MAISI: Medical AI for Synthetic Imaging](https://mlsanity.com/paper/2409.11169/) (2024)
- [Degenerative Adversarial NeuroImage Nets for Brain Scan Simulations: Application in Ageing and Dementia](https://mlsanity.com/paper/1912.01526/) (2019)
- [Degenerative Adversarial NeuroImage Nets: Generating Images that Mimic Disease Progression](https://mlsanity.com/paper/1907.02787/) (2019)
- [MAISI-v2: Accelerated 3D High-Resolution Medical Image Synthesis with Rectified Flow and Region-specific Contrastive Loss](https://mlsanity.com/paper/2508.05772/) (2025)
- [Treatment-aware Diffusion Probabilistic Model for Longitudinal MRI Generation and Diffuse Glioma Growth Prediction](https://mlsanity.com/paper/2309.05406/) (2023)
- [Diffusion Models in 3D Vision: A Survey](https://mlsanity.com/paper/2410.04738/) (2024)
- [Rectified flow-based prediction of post-treatment brain MRI from pre-radiotherapy priors for patients with glioma](https://mlsanity.com/paper/2603.08385/) (2026)

## Abstract (arXiv)

Radiotherapy is a cornerstone of glioma treatment inducing complex structural changes in brain tissue that are difficult to anticipate. Predicting these changes from pretreatment data could improve understanding of treatment-related effects and support the development of image-based outcome prediction methods. Recent studies have shown that follow-up brain magnetic resonance imaging can be synthesized from baseline imaging and treatment information, but most existing approaches operate on single 2D slices and represent treatment as a global parameter, rather than a spatially dynamic variable. In this work, we address both limitations with a 3D latent diffusion framework that conditions image generation on the spatially resolved voxel-wise dose distribution, alongside a pretreatment image and follow-up time. To make volumetric synthesis computationally feasible, the model combines latent-space compression with ControlNet-based spatial conditioning. The method was trained and evaluated on a public dataset comprising 257 scans from 25 glioma patients. Prediction quality was assessed using mean squared error, peak signal-to-noise ratio, and structural similarity index. Anatomical consistency was further evaluated using Dice scores for cerebrospinal fluid, gray matter, and white matter segmentations, together with hippocampus volume prediction error and deformation analysis based on log Jacobian determinant maps. Compared with our previously proposed 2D approach, the 3D model achieved improved image similarity while maintaining good agreement with ground truth anatomy and deformation patterns. Overall, these results support the feasibility of 3D treatment-aware generative modeling for predicting post-radiotherapy brain MRI using only pretreatment information. Code is available at https://github.com/nordinbelkacemi/fu-pred-3d