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

An anatomy-aware deep learning framework synthesized post-contrast breast MRI from pre-contrast images across 649 patients and 6,251 paired images, outperforming Pix2Pix, Pix2PixHD, diffusion-based synthesis, and mask-supervised baselines in whole-image and regional evaluations, according to an arXiv paper (2609.27015v1). The framework integrates breast mask consistency, lesion-region supervision, and background parenchymal enhancement (BPE) region supervision into an image-to-image translation model, and a reader study with two breast radiologists plus downstream Ki-67 classification found no statistically significant performance differences between real- and synthetic-image training and testing, though the authors state this does not establish equivalence. The authors conclude anatomy-aware supervision improves synthesis fidelity and support further investigation of synthetic post-contrast MRI for contrast-free imaging workflows.

arXiv:2609.27015v1 Announce Type: new 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.