# MRIComp4Flow: Compression of 3D Brain MRI for Training Multi-Modal Generative Models

arXiv: https://arxiv.org/abs/2608.10291 · HTML: https://mlsanity.com/paper/2608.10291/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 12 of its 20 references that are in the map.
- First posted on arXiv 10 Aug 2026.

## Builds on (most cited first, 12 of 12)

- [Flow Matching for Generative Modeling](https://mlsanity.com/paper/2210.02747/) (2022)
- [VoxelMorph: A Learning Framework for Deformable Medical Image Registration](https://mlsanity.com/paper/1809.05231/) (2018)
- [MedPerf: Open Benchmarking Platform for Medical Artificial Intelligence using Federated Evaluation](https://mlsanity.com/paper/2110.01406/) (2021)
- [The 2024 Brain Tumor Segmentation (BraTS) Challenge: Glioma Segmentation on Post-treatment MRI](https://mlsanity.com/paper/2405.18368/) (2024)
- [WDM: 3D Wavelet Diffusion Models for High-Resolution Medical Image Synthesis](https://mlsanity.com/paper/2402.19043/) (2024)
- [The Brain Tumor Segmentation (BraTS) Challenge 2023: Brain MR Image Synthesis for Tumor Segmentation (BraSyn)](https://mlsanity.com/paper/2305.09011/) (2023)
- [BraTS orchestrator : Democratizing and Disseminating state-of-the-art brain tumor image analysis](https://mlsanity.com/paper/2506.13807/) (2025)
- [FlowLet: Conditional 3D Brain MRI Synthesis using Wavelet Flow Matching](https://mlsanity.com/paper/2601.05212/) (2026)
- [WFM: 3D Wavelet Flow Matching for Ultrafast Multi-Modal MRI Synthesis](https://mlsanity.com/paper/2604.21146/) (2026)
- [The Effect of Lossy Compression on 3D Medical Images Segmentation with Deep Learning](https://mlsanity.com/paper/2409.16733/) (2024)
- [PREDICT-GBM: A multi-center platform to advance personalized glioblastoma radiotherapy planning](https://mlsanity.com/paper/2509.13360/) (2025)
- [Foundation VAEs for 3D CT Reconstruction, Augmentation, and Generation](https://mlsanity.com/paper/2605.30893/) (2026)

## Abstract (arXiv)

Large-scale multi-modal MRI datasets impose substantial storage and I/O costs, limiting the training of 3D generative models on commodity infrastructure. While lossy compression is known to preserve accuracy for discriminative segmentation networks, its effect on generative models, which must learn the full data distribution rather than a decision boundary, is unexplored. We study whether standard image codecs can effectively compress semantically rich brain tumor MRI while preserving the fidelity required to train and deploy a 3D MRI generative model. Each 3D volume is compressed with JPEG2000 or a near-lossless JPEG-LS pipeline. Next, a Wavelet Flow Matching model, conditioned on BraTS image sequences (T1n, T1c, T2, T2f), is trained on compressed data, and the resulting models are evaluated on the validation set. At a 20:1 compression ratio, synthesis quality is statistically equivalent to a model trained on uncompressed data within a pre-specified margin (DeltaPSNR <1,dB, DeltaSSIM <0.02; paired TOST p=[[p]]): mean PSNR is 27.3,dB vs. 27.0,dB and mean SSIM is 0.95 vs. 0.96 across modalities. Our results indicate that JPEG2000 compression is a practical step toward scalable 3D MRI generative modeling without degrading synthesis quality. The codebase is available at https://github.com/lisafis/MRIComp4Flow .