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arXiv · 2609.33611

Anguinus Sculpturae: Compositional Synthesis of Peak-Enhancement Breast DCE-MRI Scans

Benjamin Hamm·Nico Albert Disch·Maximilian Rokuss·Yannick Kirchhoff·Constantin Ulrich·Klaus Maier-Hein
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Abstract

Dynamic contrast-enhanced breast MRI (DCE-MRI) is rich in anatomical and perfusion information, but its reliance on gadolinium-based contrast agents raises safety concerns and adds cost. Virtual contrast enhancement, synthesizing post-contrast from pre-contrast images, is a promising alternative. We address the MAMA-SYNTH challenge task of predicting peak-enhancement breast MRI. Rather than adopting the full machinery of diffusion or flow matching, we observe that under a rectified, straight-line path the generative process collapses to a single difference prediction: the synthetic peak image is the pre-contrast image plus a predicted enhancement map, recovered in one forward pass. Around this we build Anguinus Sculpturae, a compositional pipeline in which nnU-Net segmentations of lesion, foreground and breast region guide two generators - one optimized for global fidelity, one for lesion structure through an asymmetric Tversky term routed via a frozen segmenter - composited region-wise with Gaussian-weighted blending. On the held-out Duke subset of MAMA-MIA our model achieves the best FRD and Dice among all evaluated variants, showing that single-step difference prediction with segmentation guidance suffices to recover both global fidelity and lesion structure. Code is available at https://github.com/MIC-DKFZ/AnguinusSculpturae.

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Benjamin Hamm, Nico Albert Disch, Maximilian Rokuss, Yannick Kirchhoff, Constantin Ulrich, Klaus Maier-Hein. 2026-09-27. Anguinus Sculpturae: Compositional Synthesis of Peak-Enhancement Breast DCE-MRI Scans. https://arxiv.org/abs/2609.33611

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