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

MCML Authors

Link to Profile Benedikt Wiestler

Benedikt Wiestler

Prof. Dr.

Core PI

AI for Image-Guided Diagnosis and Therapy

Abstract

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 (ΔPSNR <1,dB, ΔSSIM <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.

inproceedings FRR+26

SASHIMI @MICCAI 2026

11th International Workshop on Simulation and Synthesis in Medical Imaging at the 29th International Conference on Medical Image Computing and Computer Assisted Intervention. Strasbourg, France, Sep 27-Oct 01, 2026. To be published. Preprint available.

Authors

L. K. Fischer • M. Riabets • D. Rückert • B. Wiestler • A. Meyer-Baese • S. Nagar

Links

URL GitHub

Research Area

 C1 | Medicine

BibTeXKey: FRR+26

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