Anatomy aware AI creates post contrast breast MRI from pre contrast scans
Anatomy-Aware Synthesis of Post-Contrast Breast MRI from Pre-Contrast Images
Computer Vision and Pattern Recognition
Summary
Getting detailed breast images often requires injecting a contrast dye, which can be inconvenient or risky. The authors developed a smart computer program that learns to create these detailed dye-enhanced images from the regular images taken before the dye is used. Their program pays special attention to important areas like tumors and breast tissue, making the generated images more accurate. They tested their approach on many patients and found it works better than other methods and could support tasks like estimating tumor growth activity. However, they note that more studies are needed before replacing real contrast-based scans.
What this means in practice
- •For radiology departments: Generate post-contrast breast MRI images from pre-contrast scans to reduce the need for contrast injections during imaging workflows.
- •For medical imaging software developers: Integrate anatomy-aware synthesis models to improve virtual contrast generation for breast MRI analysis and diagnostic support.
Authors
Zhengbo Zhou, Dooman Arefan, Lin Gu, Ufara Zuwasti Curran, Shandong Wu
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.