Contrast-X: A Multi-Modal Contrast Image Synthesis Benchmark and Universal Modality Flow Matching

TL;DR

Contrast-X introduces a comprehensive benchmark for paired contrast and non-contrast imaging across multiple organs, and proposes FlowMI, a versatile model for synthesizing contrast images from arbitrary modality subsets.

Contribution

The paper presents Contrast-X, a new benchmark dataset with lesion-level annotations, and FlowMI, a universal model capable of handling arbitrary missing modalities in contrast image synthesis.

Findings

FlowMI effectively handles arbitrary modality subsets.

Benchmark results show high-quality contrast image synthesis.

Cross-organ tests indicate transferable contrast-enhancement learning.

Abstract

Contrast-enhanced imaging is central to oncologic diagnosis, but contrast agents can be contraindicated for many of the patients who need them most. Synthesizing contrast scans from non-contrast inputs is the natural response. Two obstacles stand in the way: no benchmark provides paired contrast data with lesion-level evaluation, and no single model handles the arbitrary missing patterns seen in practice. We introduce Contrast-X, a benchmark of paired contrast-enhanced and non-contrast imaging spanning 10 organs in CT (1{,}526 patients) and multi-phase breast DCE-MRI (1116 patients). Every case carries radiologist-verified phase labels and tumor masks. We further propose FlowMI, a single model that handles arbitrary subsets of available modalities through a unified multi-modal latent space and flow matching. We benchmark a range of missing-modality configurations, reporting standard image-quality metrics, radiologist reader studies, and downstream lesion analysis on the synthesized scans. We further evaluate cross-organ generalization to test whether the model has learned a transferable contrast-enhancement operation. Dataset, code, and leaderboard will be released. Our code are available at https://github.com/YifanChen02/Contrast-X.