HDRSNet: Learning Hemodynamic Representations via Residual Diffusion for Tumor Segmentation from Non-Contrast MRI

Dynamic contrast-enhanced MRI (DCE-MRI) provides rich hemodynamic information for breast tumor segmentation, but its clinical use is often limited by contrast-agent risks, acquisition cost, and incomplete temporal sampling—sometimes with only a pre-contrast volume available. Existing generate-then-segment methods synthesize missing post-contrast phases before segmentation, but synthesis artifacts and over-smoothed enhancement dynamics can compromise downstream performance. To address this challenge, we propose HDRSNet, a Hemodynamic Diffusion Representation Network that learns task-aligned hemodynamic representations for accurate breast tumor segmentation from a single non-contrast MRI volume. During training, HDRSNet exploits complete DCE-MRI sequences to learn enhancement dynamics as residual variations relative to the pre-contrast baseline. Specifically, a residual diffusion module incorporates recurrent modeling and local decoupling to suppress background-driven noise accumulation and capture tumor-relevant enhancement patterns. The resulting hemodynamic representations are integrated into a segmentation backbone through multi-scale task-aligned conditioning, enabling the model to exploit learned dynamic cues when only a non-contrast volume is available at inference. Experiments on multiple DCE-MRI benchmarks demonstrate that HDRSNet outperforms state-of-the-art 4D models requiring complete temporal sequences and generate-then-segment pipelines. These results highlight task-aligned hemodynamic representation learning as an effective alternative to explicit image synthesis for tumor segmentation under missing contrast-enhanced observations. The source code is available at: https://anonymous.4open.science/r/HDRSNet-6A42.