[Submitted on 22 Sep 2026]
Title:Anatomy-Aware Synthesis of Post-Contrast Breast MRI from Pre-Contrast Images
View a PDF of the paper titled Anatomy-Aware Synthesis of Post-Contrast Breast MRI from Pre-Contrast Images, by Zhengbo Zhou and 4 other authors
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Abstract:We developed an anatomy-aware deep learning framework to synthesize post-contrast breast MRI from pre-contrast images, emphasizing tumor and background parenchymal enhancement (BPE) regions. This retrospective study included 649 patients with 6,251 paired pre-contrast and post-contrast images. The framework integrates breast mask consistency, lesion-region supervision, and BPE-region supervision into an image-to-image translation model. Evaluation included quantitative image quality metrics, a reader study with two breast radiologists, and downstream Ki-67 classification. The proposed method outperformed Pix2Pix, Pix2PixHD, diffusion-based synthesis, and mask-supervised baselines in whole-image and regional evaluations. Ki-67 classification showed no statistically significant performance differences across real- and synthetic-image training and testing settings, although this does not establish equivalence. These findings suggest that anatomy-aware supervision improves synthesis fidelity and support further investigation of synthetic post-contrast MRI for contrast-free imaging workflows.
Subjects:
Computer Vision and Pattern Recognition (cs.CV)
Cite as: arXiv:2609.27015 [cs.CV]
(or arXiv:2609.27015v1 [cs.CV] for this version)
https://doi.org/10.48550/arXiv.2609.27015
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Zhengbo Zhou [view email] [v1] Tue, 22 Sep 2026 19:57:46 UTC (2,916 KB)
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