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arXiv Computer Vision By Zhengbo Zhou, Dooman Arefan, Lin Gu, Ufara Zuwasti Curran, Shandong Wu

Anatomy-Aware Synthesis of Post-Contrast Breast MRI from Pre-Contrast Images

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The study presents an anatomy-aware deep learning framework that generates post-contrast breast MRI from pre-contrast images, focusing on tumor and background parenchymal enhancement regions. Using a dataset of 649 patients and 6,251 image pairs, the model incorporates breast mask consistency, lesion-region supervision, and BPE-region supervision within an image-to-image translation architecture. Quantitative metrics, a radiologist reader study, and Ki‑67 classification experiments demonstrate that the proposed method surpasses Pix2Pix, Pix2PixHD, diffusion-based synthesis, and mask-supervised baselines, while Ki‑67 performance remains comparable between real and synthetic images.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Computer Vision.

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